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дигитализацију","\u002Fportfolio\u002Fdigitalisierungsportal-archiv-museum-bibliothek-ead-lido-mets-mods",[],{"statusCode":4,"data":206,"message":2637},{"id":207,"title":208,"slug":209,"content":210,"contentJson":211,"excerpt":1225,"featuredImage":1226,"featuredImageAlt":1227,"featuredImageCaption":10,"featuredImageTitle":10,"featuredImageCopyright":10,"featuredImageAuthor":10,"featuredImageSourceUrl":10,"featuredImageLicense":10,"featuredImageIsAiGenerated":43,"status":1228,"publishedAt":1229,"createdAt":1230,"updatedAt":1231,"seoLocalePaths":1232,"categories":1241,"author":1242,"translations":1246},"465","Od istraživačkog protokola do opšteg okvira za AI rezonovanje","from-research-protocol-to-a-general-ai-reasoning-framework","\u003Cp>Metodologija razvijena kroz ovu seriju započela je istraživačkim problemom: kako AI model može pomoći u istraživanju složenog pitanja, a da pritom ne pojačava samo pretpostavke koje su već prisutne u korisničkom upitu?\u003C\u002Fp>\n\u003Cp>Taj problem na prvi pogled deluje kao da pripada istorijskom ili akademskom istraživanju. U stvarnosti, on je znatno širi.\u003C\u002Fp>\n\u003Cp>Otklanjanje grešaka u softveru, arhitektonske odluke, tehnička dijagnostika, analiza bezbednosti, strategija proizvoda i mnogi oblici podrške pri odlučivanju dele istu osnovnu strukturu. Problem je predstavljen. Neki dokazi su dostupni. Jedno ili više objašnjenja deluju uverljivo. Pretpostavke ulaze u analizu. Sistem mora da utvrdi koji zaključak ima najbolju potporu.\u003C\u002Fp>\n\u003Cblockquote class=\"border-l-4 border-gray-300 pl-4 italic\">Domen se menja. Epistemički problem često ostaje isti.\u003C\u002Fblockquote>\n\u003Cp>Ovaj članak stoga preduzima sledeći korak: pretvaranje istraživačkog protokola razvijenog u prethodnim člancima u opšti okvir zaključivanja za analitički rad uz podršku veštačke inteligencije.\u003C\u002Fp>\n\u003Cp>Okvir se direktno oslanja na četiri prethodna sloja: \u003Ca href=\"https:\u002F\u002Fstajic.de\u002Fsr\u002Fblog\u002Fbeyond-prompt-engineering-a-methodology-for-more-reliable-ai-reasoning\">Beyond Prompt Engineering: A Methodology for More Reliable AI Reasoning\u003C\u002Fa> definiše opšti metodološki problem; \u003Ca href=\"https:\u002F\u002Fstajic.de\u002Fsr\u002Fblog\u002Fthe-prompt-is-part-of-the-bias-how-ai-framing-shapes-reasoning\">The Prompt Is Part of the Bias\u003C\u002Fa> ispituje uokviravanje i zavisnost od upita; \u003Ca href=\"https:\u002F\u002Fstajic.de\u002Fsr\u002Fblog\u002Fprompt-invariance-does-the-conclusion-survive-the-prompt\">Prompt Invariance: Does the Conclusion Survive the Prompt?\u003C\u002Fa> uvodi test robusnosti protiv uokviravanja; i \u003Ca href=\"https:\u002F\u002Fstajic.de\u002Fsr\u002Fblog\u002Ffalsification-for-ai-reasoning-from-answers-to-tested-hypotheses\">Falsification for AI Reasoning: From Answers to Tested Hypotheses\u003C\u002Fa> dodaje sistematsku diskonfirmaciju i konkurentske hipoteze.\u003C\u002Fp>\n\u003Ch2>Ključna generalizacija\u003C\u002Fh2>\n\u003Cp>Generalizacija se ne sastoji u tome da svaki zadatak treba tretirati kao istorijsko istraživanje.\u003C\u002Fp>\n\u003Cp>To bi bila kategorijalna greška.\u003C\u002Fp>\n\u003Cp>Istorijsko istraživanje zavisi od hronologije, porekla, prenosa, kritike izvora i arhivske potpunosti. Otklanjanje softverskih grešaka zavisi od logova, konfiguracije, ponovljivosti, stanja izvršavanja i kontrolisanih testova. Arhitektura zavisi od zahteva, ograničenja, interfejsa, atributa kvaliteta i operativnih kompromisa.\u003C\u002Fp>\n\u003Cp>Ono što se može generalizovati jeste proces zaključivanja koji okružuje te domenski specifične oblike dokaza.\u003C\u002Fp>\n\u003Cblockquote class=\"border-l-4 border-gray-300 pl-4 italic\">Okvir je u svojoj suštini nezavisan od domena, ali nikada nije lišen domena.\u003C\u002Fblockquote>\n\u003Cp>Ova razlika je fundamentalna. Univerzalna metodologija zaključivanja ne može zameniti stručnost. Umesto toga, ona može definisati kako treba postupati sa dokazima, pretpostavkama, hipotezama, kontradikcijama i neizvesnošću pre nego što domenska stručnost donese konačni sud.\u003C\u002Fp>\n\u003Ch2>Jezgro zaključivanja nezavisno od domena\u003C\u002Fh2>\n\u003Cp>Kroz različite analitičke domene može se primeniti isti redosled visokog nivoa:\u003C\u002Fp>\n\u003Cblockquote class=\"border-l-4 border-gray-300 pl-4 italic\">Problem → dekompozicija → dokazi → pretpostavke → konkurentske hipoteze → predviđanja → protividokazi → diskriminišući testovi → domenska validacija → rekalibracija poverenja → zaključak\u003C\u002Fblockquote>\n\u003Cp>Ovaj proces namerno sprečava da prvo koherentno objašnjenje modela po automatizmu postane konačan odgovor.\u003C\u002Fp>\n\u003Cp>Umesto toga, prvo objašnjenje postaje kandidat.\u003C\u002Fp>\n\u003Ch3>1. Definišite stvarni problem\u003C\u002Fh3>\n\u003Cp>Pre generisanja rešenja, sistem bi trebalo da utvrdi šta se zapravo traži.\u003C\u002Fp>\n\u003Cp>Korisnički zahtevi često sadrže i problem i predloženu dijagnozu:\u003C\u002Fp>\n\u003Cblockquote class=\"border-l-4 border-gray-300 pl-4 italic\">Zašto nginx izaziva neuspehe povezivanja na moj API?\u003C\u002Fblockquote>\n\u003Cp>Stvarni problem, međutim, može biti:\u003C\u002Fp>\n\u003Cblockquote class=\"border-l-4 border-gray-300 pl-4 italic\">Šta izaziva neuspehe povezivanja na API?\u003C\u002Fblockquote>\n\u003Cp>Razlika je lingvistički mala, a metodološki ogromna.\u003C\u002Fp>\n\u003Cp>Prva formulacija ugrađuje uzročnu hipotezu. Druga izlaže tu hipotezu konkurenciji.\u003C\u002Fp>\n\u003Cp>Ovo je upravo problem uokvirivanja ispitan u tekstu \u003Ca href=\"https:\u002F\u002Fstajic.de\u002Fsr\u002Fblog\u002Fthe-prompt-is-part-of-the-bias-how-ai-framing-shapes-reasoning\">The Prompt Is Part of the Bias\u003C\u002Fa>.\u003C\u002Fp>\n\u003Ch3>2. Razdvojte činjenice, pretpostavke i nepoznate stavke\u003C\u002Fh3>\n\u003Cp>Pouzdan proces rezonovanja mora sprečiti neprimetno spajanje zapažanja i interpretacija.\u003C\u002Fp>\n\u003Cul>\u003Cli>\u003Cb>Činjenica:\u003C\u002Fb> direktno potkrepljena dostupnim dokazima.\u003C\u002Fli>\u003Cli>\u003Cb>Interpretacija:\u003C\u002Fb> objašnjenje izvedeno iz činjenica.\u003C\u002Fli>\u003Cli>\u003Cb>Pretpostavka:\u003C\u002Fb> tvrdnja neophodna za trenutni tok rezonovanja, ali nije nezavisno utvrđena.\u003C\u002Fli>\u003Cli>\u003Cb>Nepoznato:\u003C\u002Fb> informacija neophodna za pouzdanije razlikovanje, ali trenutno nedostupna.\u003C\u002Fli>\u003C\u002Ful>\n\u003Cp>Ova klasifikacija može delovati elementarno, ali rešava uobičajeni obrazac greške u analizama koje generiše veštačka inteligencija: pretpostavke bivaju ugrađene u tečan tekst i kasnije se ponovo pojavljuju kao da su već utvrđene.\u003C\u002Fp>\n\u003Cblockquote class=\"border-l-4 border-gray-300 pl-4 italic\">Sledivost počinje sprečavanjem zaključka da se preruši u dokaz.\u003C\u002Fblockquote>\n\u003Ch3>3. Generišite konkurentna objašnjenja\u003C\u002Fh3>\n\u003Cp>Kandidat za objašnjenje ne bi trebalo da se ocenjuje izolovano kada postoje verodostojne alternative.\u003C\u002Fp>\n\u003Cp>Sistem bi stoga trebalo da generiše nekoliko verovatnih hipoteza pre nego što se opredeli za jednu.\u003C\u002Fp>\n\u003Cp>Svrha nije veštački brejnstorming. Alternative bi trebalo da predstavljaju suštinski različite mehanizme sposobne da objasne zapažanja.\u003C\u002Fp>\n\u003Cblockquote class=\"border-l-4 border-gray-300 pl-4 italic\">Preferirana hipoteza nije pobedila ako nijednom ozbiljnom konkurentu nije bilo dozvoljeno da uđe u testiranje.\u003C\u002Fblockquote>\n\u003Cp>Ovaj koncept je centralan za \u003Ca href=\"https:\u002F\u002Fstajic.de\u002Fsr\u002Fblog\u002Ffalsification-for-ai-reasoning-from-answers-to-tested-hypotheses\">Falsifikaciju za AI rezonovanje\u003C\u002Fa>, gde se hipoteze procenjuju kroz očekivana zapažanja, kontra-dokaze i diskriminativne testove, a ne kroz nagomilanu prozu podrške.\u003C\u002Fp>\n\u003Ch3>4. Očuvajte poreklo dokaza\u003C\u002Fh3>\n\u003Cp>Dokazi treba da ostanu sledljivi do svog izvora.\u003C\u002Fp>\n\u003Cblockquote class=\"border-l-4 border-gray-300 pl-4 italic\">Zaključak → tumačenje → zapažanje → izvor\u003C\u002Fblockquote>\n\u003Cp>Linija u logu, zvanična tehnička specifikacija, eksperiment, primarni istorijski dokument, stručno tumačenje i sažetak generisan od strane veštačke inteligencije nemaju identičan dokazni status.\u003C\u002Fp>\n\u003Cp>Okvir stoga ne skladišti samo informacije. On treba da sačuva dovoljno metapodataka kako bi se znalo odakle tvrdnja potiče i koliko je konačni zaključak udaljen od prvobitnog zapažanja.\u003C\u002Fp>\n\u003Ch3>5. Izvedite predviđanja pre objašnjavanja rezultata\u003C\u002Fh3>\n\u003Cp>Kada hipoteze postoje, svaka bi trebalo da generiše očekivanja.\u003C\u002Fp>\n\u003Cp>Ako je hipoteza tačna, šta bi trebalo da uočimo?\u003C\u002Fp>\n\u003Cp>Ako je alternativa tačna, šta bi trebalo da bude drugačije?\u003C\u002Fp>\n\u003Cp>Izvođenje očekivanja pre tumačenja svih dostupnih dokaza pomaže da se spreči naknadno uklapanje svakog zapažanja u preferirani narativ.\u003C\u002Fp>\n\u003Ch3>6. Potraga za kontra-dokazima\u003C\u002Fh3>\n\u003Cp>Generativni model je prirodno efikasan u konstruisanju koherentne podrške za verovatnu ideju. Zbog toga je opovrgavanje posebno važno.\u003C\u002Fp>\n\u003Cp>Sistem bi trebalo izričito da pita:\u003C\u002Fp>\n\u003Cul>\u003Cli>Koje bi zapažanje značajno oslabilo ovo objašnjenje?\u003C\u002Fli>\u003Cli>Koje dokaze hipoteza slabo objašnjava?\u003C\u002Fli>\u003Cli>Koja alternativa objašnjava ista zapažanja sa manje pretpostavki?\u003C\u002Fli>\u003Cli>Koja očekivana zapažanja nedostaju?\u003C\u002Fli>\u003Cli>Da li bi se prividna podrška mogla objasniti nekim drugim mehanizmom?\u003C\u002Fli>\u003C\u002Ful>\n\u003Cp>Ovaj princip je detaljno razrađen u tekstu \u003Ca href=\"https:\u002F\u002Fstajic.de\u002Fsr\u002Fblog\u002Ffalsification-for-ai-reasoning-from-answers-to-tested-hypotheses\">Falsifikacija za AI rezonovanje: od odgovora do testiranih hipoteza\u003C\u002Fa>.\u003C\u002Fp>\n\u003Ch3>7. Testirajte zavisnost od prompta\u003C\u002Fh3>\n\u003Cp>Samo testiranje dokaza ne otkriva da li analiza modela ostaje preterano zavisna od formulacije korisnika.\u003C\u002Fp>\n\u003Cp>Za dovoljno važne probleme, zaključak se stoga može ponovo testirati pod alternativnim legitimnim okvirima.\u003C\u002Fp>\n\u003Cul>\u003Cli>\u003Cb>Originalno:\u003C\u002Fb> sačuvati formulaciju korisnika.\u003C\u002Fli>\u003Cli>\u003Cb>Slepo:\u003C\u002Fb> ukloniti preferirani zaključak.\u003C\u002Fli>\u003Cli>\u003Cb>Invertovano:\u003C\u002Fb> staviti u prvi plan najjaču alternativu.\u003C\u002Fli>\u003Cli>\u003Cb>Adversarijalno:\u003C\u002Fb> namerno tražiti najjači izazov zasnovan na dokazima.\u003C\u002Fli>\u003C\u002Ful>\n\u003Cp>Ovo je metod invarijantnosti upita predstavljen u tekstu \u003Ca href=\"https:\u002F\u002Fstajic.de\u002Fsr\u002Fblog\u002Fprompt-invariance-does-the-conclusion-survive-the-prompt\">Prompt Invariance: Does the Conclusion Survive the Prompt?\u003C\u002Fa>.\u003C\u002Fp>\n\u003Cp>Njegova funkcija nije da dokaže da je stabilan odgovor tačan. Njegova funkcija je da razotkrije zaključke koji se menjaju prvenstveno zato što se promenio okvir.\u003C\u002Fp>\n\u003Ch3>8. Primenite validatore specifične za domen\u003C\u002Fh3>\n\u003Cp>Ovde opšti okvir namerno prestaje da bude univerzalan.\u003C\u002Fp>\n\u003Cp>Svaki domen zahteva sopstvena pravila za utvrđivanje da li je objašnjenje zaista verodostojno.\u003C\u002Fp>\n\u003Ctable class=\"w-full border-collapse\">\u003Ctr>\u003Ctd class=\"border border-gray-300 px-4 py-2\">Domen\u003C\u002Ftd>\u003Ctd class=\"border border-gray-300 px-4 py-2\">Tipični validatori\u003C\u002Ftd>\u003C\u002Ftr>\u003Ctr>\u003Ctd class=\"border border-gray-300 px-4 py-2\">Istorijsko istraživanje\u003C\u002Ftd>\u003Ctd class=\"border border-gray-300 px-4 py-2\">Hronologija, provenijencija, geografska verovatnoća, kanali prenosa, primarni izvori, istoriografija\u003C\u002Ftd>\u003C\u002Ftr>\u003Ctr>\u003Ctd class=\"border border-gray-300 px-4 py-2\">Otklanjanje grešaka u softveru\u003C\u002Ftd>\u003Ctd class=\"border border-gray-300 px-4 py-2\">Dnevnici (logs), reprodukcija, stanje izvršavanja, konfiguracija, kontrolisane promene, korelacija grešaka\u003C\u002Ftd>\u003C\u002Ftr>\u003Ctr>\u003Ctd class=\"border border-gray-300 px-4 py-2\">Softverska arhitektura\u003C\u002Ftd>\u003Ctd class=\"border border-gray-300 px-4 py-2\">Zahtevi, ograničenja, skalabilnost, održivost, bezbednost, operativnost, troškovi\u003C\u002Ftd>\u003C\u002Ftr>\u003Ctr>\u003Ctd class=\"border border-gray-300 px-4 py-2\">Analiza bezbednosti\u003C\u002Ftd>\u003Ctd class=\"border border-gray-300 px-4 py-2\">Telemetrija, putanja napada, dozvole, uočljivi indikatori, ponovljivost, model pretnji\u003C\u002Ftd>\u003C\u002Ftr>\u003Ctr>\u003Ctd class=\"border border-gray-300 px-4 py-2\">Strategija proizvoda\u003C\u002Ftd>\u003Ctd class=\"border border-gray-300 px-4 py-2\">Dokazi od korisnika, ponašanje pri određivanju cena, konverzija, alternative, tržišna ograničenja, kriterijumi neuspeha\u003C\u002Ftd>\u003C\u002Ftr>\u003Ctr>\u003Ctd class=\"border border-gray-300 px-4 py-2\">Upravljanje projektima\u003C\u002Ftd>\u003Ctd class=\"border border-gray-300 px-4 py-2\">Obim, zavisnosti, resursi, rizici, kriterijumi prihvatanja, ključne tačke, ograničenja zainteresovanih strana\u003C\u002Ftd>\u003C\u002Ftr>\u003Ctr>\u003Ctd class=\"border border-gray-300 px-4 py-2\">Naučna analiza\u003C\u002Ftd>\u003Ctd class=\"border border-gray-300 px-4 py-2\">Dizajn eksperimenta, kvalitet merenja, kontrole, ponovljivost, statistički dokazi, alternativna objašnjenja\u003C\u002Ftd>\u003C\u002Ftr>\u003C\u002Ftable>\n\u003Cp>Okvir kontroliše kako se tvrdnje obrađuju. Validatori domena određuju da li te tvrdnje preživljavaju kontakt sa stvarnošću.\u003C\u002Fp>\n\u003Ch3>9. Rekalibrišite nivo pouzdanosti\u003C\u002Fh3>\n\u003Cp>Konačni zaključak ne bi trebalo automatski da zadrži nivo pouzdanosti početnog odgovora.\u003C\u002Fp>\n\u003Cp>Pouzdanost treba ponovo izračunati nakon što se razmotre alternative, protivdokazi, varijacije upita i validacija domena.\u003C\u002Fp>\n\u003Cp>Korisni ishod stoga može ostati neizvestan.\u003C\u002Fp>\n\u003Cul>\u003Cli>snažno podržano;\u003C\u002Fli>\u003Cli>privremeno podržano;\u003C\u002Fli>\u003Cli>slabo podržano;\u003C\u002Fli>\u003Cli>nedovoljno determinisano;\u003C\u002Fli>\u003Cli>značajno opovrgnuto;\u003C\u002Fli>\u003Cli>nije proverljivo sa trenutnim dokazima.\u003C\u002Fli>\u003C\u002Ful>\n\u003Cblockquote class=\"border-l-4 border-gray-300 pl-4 italic\">Neizvesnost nije neuspeh rezonovanja kada su sami dokazi neizvesni.\u003C\u002Fblockquote>\n\u003Ch2>Isti okvir kroz različite domene\u003C\u002Fh2>\n\u003Cp>Najlakši način da se razume opšti okvir jeste posmatranje kako se ista arhitektura rezonovanja ponaša kada se domen promeni.\u003C\u002Fp>\n\u003Ch3>Istorijsko istraživanje\u003C\u002Fh3>\n\u003Cp>Problem: utvrditi da li sličnosti između dve intelektualne tradicije ukazuju na istorijski prenos.\u003C\u002Fp>\n\u003Cul>\u003Cli>Odvojiti dokumentovane sličnosti od interpretativnih paralela.\u003C\u002Fli>\u003Cli>Uporediti direktan prenos, indirektno nasleđe, konvergenciju i retrospektivnu interpretaciju.\u003C\u002Fli>\u003Cli>Proveriti hronologiju i geografski kontakt.\u003C\u002Fli>\u003Cli>Potražiti dokumentarne ili terminološke tragove.\u003C\u002Fli>\u003Cli>Identifikovati dokaze koji se očekuju u slučaju direktnog prenosa.\u003C\u002Fli>\u003Cli>Potražiti ranije nezavisne primere koji slabe tu hipotezu.\u003C\u002Fli>\u003Cli>Preformulisati pitanje bez preferirane genealogije.\u003C\u002Fli>\u003Cli>Donositi zaključke samo na nivou pouzdanosti koji podržavaju sačuvani dokazi.\u003C\u002Fli>\u003C\u002Ful>\n\u003Ch3>Otklanjanje grešaka u softveru\u003C\u002Fh3>\n\u003Cp>Problem: utvrditi zašto API veza povremeno otkazuje.\u003C\u002Fp>\n\u003Cul>\u003Cli>Odvojiti uočene greške od uzroka na koje sumnja programer.\u003C\u002Fli>\u003Cli>Generisati hipoteze vezane za proksi, aplikaciju, bazu podataka, mrežu i klijenta.\u003C\u002Fli>\u003Cli>Utvrditi koje zapise i ponašanje u toku izvršavanja svaka hipoteza predviđa.\u003C\u002Fli>\u003Cli>Izvršiti testove koji mogu napraviti razliku među njima.\u003C\u002Fli>\u003Cli>Pokušati reprodukovanje greške uz postepeno eliminisanje slojeva.\u003C\u002Fli>\u003Cli>Odbaciti objašnjenja koja su u suprotnosti sa kontrolisanim posmatranjima.\u003C\u002Fli>\u003Cli>Identifikovati prvi potvrđeni mehanizam otkaza umesto najuverljivijeg narativa.\u003C\u002Fli>\u003C\u002Ful>\n\u003Ch3>Softverska arhitektura\u003C\u002Fh3>\n\u003Cp>Problem: izabrati arhitekturu za novu platformu.\u003C\u002Fp>\n\u003Cul>\u003Cli>Odvojiti zahteve od implementacionih preferencija.\u003C\u002Fli>\u003Cli>Uporediti modularni monolit, servise i hibridne alternative.\u003C\u002Fli>\u003Cli>Testirati svaku opciju u odnosu na skalabilnost, strukturu tima, razmeštanje, operativnu složenost i troškove.\u003C\u002Fli>\u003Cli>Identifikovati koji zahtev zaista iziskuje arhitektonsku složenost.\u003C\u002Fli>\u003Cli>Potražiti jednostavnija rešenja sposobna da zadovolje ista ograničenja.\u003C\u002Fli>\u003Cli>Tretirati tehnološku preferenciju kao pretpostavku, a ne kao zahtev.\u003C\u002Fli>\u003C\u002Ful>\n\u003Ch3>Strategija proizvoda\u003C\u002Fh3>\n\u003Cp>Problem: utvrditi da li određenu mogućnost proizvoda treba komercijalizovati.\u003C\u002Fp>\n\u003Cul>\u003Cli>Odvojiti tehničku izvodljivost od dokazane potražnje.\u003C\u002Fli>\u003Cli>Definisati alternativne probleme korisnika i alternativna rešenja.\u003C\u002Fli>\u003Cli>Identifikovati vidljivo ponašanje tržišta predviđeno poslovnom tezom.\u003C\u002Fli>\u003Cli>Definisati kriterijume neuspeha pre tumačenja rezultata.\u003C\u002Fli>\u003Cli>Potražiti dokaze o tome da korisnici rešavaju problem na drugačiji način.\u003C\u002Fli>\u003Cli>Razlikovati interesovanje, spremnost na testiranje i spremnost na plaćanje.\u003C\u002Fli>\u003C\u002Ful>\n\u003Ch3>Analiza projekta i isporuke\u003C\u002Fh3>\n\u003Cp>Problem: utvrditi zašto projekat ne ostvaruje planirane rezultate.\u003C\u002Fp>\n\u003Cul>\u003Cli>Odvojiti simptome kao što je kašnjenje od njihovih pretpostavljenih uzroka.\u003C\u002Fli>\u003Cli>Uporediti obim, zavisnosti, kapacitet, zahteve, upravljanje i tehnički rizik.\u003C\u002Fli>\u003Cli>Pratiti dokaze kroz planove, odluke, izmene i kriterijume prihvatanja.\u003C\u002Fli>\u003Cli>Proveriti da li korektivna mera rešava stvarni mehanizam, a ne samo vidljivi simptom.\u003C\u002Fli>\u003Cli>Ponovo proceniti hipotezu projekta kako se pojavljuju novi dokazi o isporuci.\u003C\u002Fli>\u003C\u002Ful>\n\u003Ch2>Zašto je ovo više od inženjeringa upita\u003C\u002Fh2>\n\u003Cp>Inženjering upita menja instrukciju koja se daje modelu.\u003C\u002Fp>\n\u003Cp>Okvir za rezonovanje menja proces kroz koji je odgovoru dopušteno da postane zaključak.\u003C\u002Fp>\n\u003Cblockquote class=\"border-l-4 border-gray-300 pl-4 italic\">Inženjering upita optimizuje poziv. Okvir za rezonovanje upravlja procesom zaključivanja.\u003C\u002Fblockquote>\n\u003Cp>Ova razlika postaje važna kako se AI sistemi pomeraju od interfejsa za ćaskanje ka agentima, RAG pipeline-ovima, automatizovanom istraživanju, razvojnim alatima i sistemima za podršku odlučivanju.\u003C\u002Fp>\n\u003Cp>Jedan pažljivo dizajniran upit može poboljšati jedan poziv modela. Okvir za rezonovanje može definisati kako više poziva, pronađeni dokazi, rezultati alata i faze validacije interaguju pre nego što sistem donese odgovor.\u003C\u002Fp>\n\u003Ch2>Okvir je povezan sa — ali različit od — postojećih metoda rezonovanja LLM-a\u003C\u002Fh2>\n\u003Cp>Širi istraživački pejzaž već sadrži nekoliko važnih pristupa koji pokazuju da se performanse LLM-a mogu poboljšati kada je zaključivanje strukturirano kao proces, a ne kao jedna generacija.\u003C\u002Fp>\n\u003Cp>\u003Cb>ReAct\u003C\u002Fb> kombinuje rezonovanje sa akcijama i zapažanjima iz spoljašnjeg okruženja. Umesto da se oslanja isključivo na interno znanje modela, model može da deluje, posmatra nove informacije i ažurira svoj sledeći korak.\u003C\u002Fp>\n\u003Cp>\u003Cb>Self-Refine\u003C\u002Fb> koristi iterativnu generaciju, povratne informacije i rafiniranje, pokazujući da se početni izlaz LLM-a često može poboljšati kroz eksplicitne samopovratne informacije bez dodatne obuke modela.\u003C\u002Fp>\n\u003Cp>\u003Cb>Reflexion\u003C\u002Fb> koristi verbalne povratne informacije i epizodičnu memoriju kako bi omogućio jezičkim agentima da uče iz prethodnih pokušaja i modifikuju kasnije ponašanje.\u003C\u002Fp>\n\u003Cp>\u003Cb>Tree of Thoughts\u003C\u002Fb> istražuje više putanja rezonovanja umesto da se odmah opredeli za jedan lanac s leva na desno, omogućavajući pretragu, evaluaciju i vraćanje unazad preko kandidatskih pristupa.\u003C\u002Fp>\n\u003Cp>Ove metode se značajno razlikuju po nameni i implementaciji, ali uspostavljaju važnu opštu poentu: struktura u vreme zaključivanja može materijalno promeniti performanse modela.\u003C\u002Fp>\n\u003Cp>Okvir predložen u ovoj seriji bavi se drugačijim primarnim ciljem.\u003C\u002Fp>\n\u003Cblockquote class=\"border-l-4 border-gray-300 pl-4 italic\">Cilj nije jednostavno naterati model da duže traži, više razmišlja ili generiše bolji odgovor. Cilj je učiniti put od dokaza do zaključka otpornijim na uokvirivanje, potvrđivanje i nepotkrepljene pretpostavke.\u003C\u002Fblockquote>\n\u003Cp>Nepromenljivost upita, testiranje usmereno na falsifikaciju i validacija specifična za domen stoga služe kao epistemičke kontrole, a ne kao generičke tehnike proširenja zaključivanja.\u003C\u002Fp>\n\u003Ch2>Slojeviti pogled na kvalitet AI rezonovanja\u003C\u002Fh2>\n\u003Cp>Rezultujući sistem se može razumeti kao nekoliko slojeva.\u003C\u002Fp>\n\u003Ctable class=\"w-full border-collapse\">\u003Ctr>\u003Ctd class=\"border border-gray-300 px-4 py-2\">Sloj\u003C\u002Ftd>\u003Ctd class=\"border border-gray-300 px-4 py-2\">Pitanje\u003C\u002Ftd>\u003C\u002Ftr>\u003Ctr>\u003Ctd class=\"border border-gray-300 px-4 py-2\">Razumevanje zadatka\u003C\u002Ftd>\u003Ctd class=\"border border-gray-300 px-4 py-2\">Koji problem zapravo rešavamo?\u003C\u002Ftd>\u003C\u002Ftr>\u003Ctr>\u003Ctd class=\"border border-gray-300 px-4 py-2\">Dokazi\u003C\u002Ftd>\u003Ctd class=\"border border-gray-300 px-4 py-2\">Šta zapravo znamo?\u003C\u002Ftd>\u003C\u002Ftr>\u003Ctr>\u003Ctd class=\"border border-gray-300 px-4 py-2\">Pretpostavke\u003C\u002Ftd>\u003Ctd class=\"border border-gray-300 px-4 py-2\">Šta trenutno uzimamo zdravo za gotovo?\u003C\u002Ftd>\u003C\u002Ftr>\u003Ctr>\u003Ctd class=\"border border-gray-300 px-4 py-2\">Hipoteze\u003C\u002Ftd>\u003Ctd class=\"border border-gray-300 px-4 py-2\">Koji verodostojni mehanizmi bi mogli objasniti dokaze?\u003C\u002Ftd>\u003C\u002Ftr>\u003Ctr>\u003Ctd class=\"border border-gray-300 px-4 py-2\">Falsifikacija\u003C\u002Ftd>\u003Ctd class=\"border border-gray-300 px-4 py-2\">Koji dokazi bi mogli oslabiti svako objašnjenje?\u003C\u002Ftd>\u003C\u002Ftr>\u003Ctr>\u003Ctd class=\"border border-gray-300 px-4 py-2\">Nepromenljivost upita\u003C\u002Ftd>\u003Ctd class=\"border border-gray-300 px-4 py-2\">Da li zaključak prekomerno zavisi od uokvirivanja?\u003C\u002Ftd>\u003C\u002Ftr>\u003Ctr>\u003Ctd class=\"border border-gray-300 px-4 py-2\">Validacija domena\u003C\u002Ftd>\u003Ctd class=\"border border-gray-300 px-4 py-2\">Da li objašnjenje zadovoljava pravila relevantne discipline?\u003C\u002Ftd>\u003C\u002Ftr>\u003Ctr>\u003Ctd class=\"border border-gray-300 px-4 py-2\">Kalibracija pouzdanja\u003C\u002Ftd>\u003Ctd class=\"border border-gray-300 px-4 py-2\">Koliko snažno treba verovati preostalom zaključku?\u003C\u002Ftd>\u003C\u002Ftr>\u003C\u002Ftable>\n\u003Cp>Odgovor može da padne na bilo kom sloju.\u003C\u002Fp>\n\u003Cp>Može tačno da odgovori na pogrešan problem. Može ispravno da rezonuje na osnovu lažnih dokaza. Može da pronađe tačne činjenice, ali da izabere pogrešno kauzalno objašnjenje. Može da proizvede snažno objašnjenje koje nestaje kada se upit preformuliše. Ili može da preživi sve opšte provere rezonovanja i dalje prekrši ograničenje specifično za domen.\u003C\u002Fp>\n\u003Cblockquote class=\"border-l-4 border-gray-300 pl-4 italic\">Ispravnost nije jedna osobina. Ona je rezultat nekoliko zavisnosti koje istovremeno opstaju.\u003C\u002Fblockquote>\n\u003Ch2>Kvalitet zaključivanja naspram sposobnosti modela\u003C\u002Fh2>\n\u003Cp>Ovo nas vraća na jednu od centralnih ideja serije.\u003C\u002Fp>\n\u003Cp>Jači model ne podrazumeva automatski da će svaki poziv iskoristiti njegove sposobnosti na najrigorozniji mogući način.\u003C\u002Fp>\n\u003Cp>Model može posedovati sposobnost da generiše alternative, pregleda zapise, pretražuje izvore, dovodi u pitanje pretpostavke i revidira zaključak. Da li se sve te operacije zaista odvijaju zavisi od zadatka, podsticanja, dostupnih alata, konteksta i okolne sistemske arhitekture.\u003C\u002Fp>\n\u003Cp>Okvir stoga razdvaja dva pitanja:\u003C\u002Fp>\n\u003Cul>\u003Cli>\u003Cb>Sposobnost:\u003C\u002Fb> šta model može da uradi?\u003C\u002Fli>\u003Cli>\u003Cb>Metodološka disciplina:\u003C\u002Fb> koje od tih sposobnosti moraju biti primenjene pre nego što sistem prihvati zaključak?\u003C\u002Fli>\u003C\u002Ful>\n\u003Cp>Ova razlika bila je polazna tačka za \u003Ca href=\"https:\u002F\u002Fstajic.de\u002Fsr\u002Fblog\u002Fbeyond-prompt-engineering-a-methodology-for-more-reliable-ai-reasoning\">Beyond Prompt Engineering\u003C\u002Fa> i postaje još važnija kada se metodologija generalizuje izvan istraživanja.\u003C\u002Fp>\n\u003Ch2>Nije svaki zadatak potreban pun okvir\u003C\u002Fh2>\n\u003Cp>Okvir ne treba da postane obavezni dodatni teret za svaku interakciju sa AI.\u003C\u002Fp>\n\u003Cp>Mnogi zadaci imaju nisku epistemičku složenost:\u003C\u002Fp>\n\u003Cul>\u003Cli>formatiraj ovaj JSON;\u003C\u002Fli>\u003Cli>prevedi ovu rečenicu;\u003C\u002Fli>\u003Cli>konvertuj ove jedinice;\u003C\u002Fli>\u003Cli>prepiši ovu poruku;\u003C\u002Fli>\u003Cli>izvuci ova polja;\u003C\u002Fli>\u003Cli>sažmi ovaj dostavljeni tekst.\u003C\u002Fli>\u003C\u002Ful>\n\u003Cp>Pokretanje četiri varijante podsticaja, tri konkurentske hipoteze i faze falsifikacije za takve zadatke dodalo bi trošak bez proporcionalne vrednosti.\u003C\u002Fp>\n\u003Cp>Puna metoda postaje vrednija kada:\u003C\u002Fp>\n\u003Cul>\u003Cli>odgovor zavisi od interpretacije, a ne od direktnog pronalaženja;\u003C\u002Fli>\u003Cli>korisnik već ima preferirano objašnjenje;\u003C\u002Fli>\u003Cli>više kauzalnih mehanizama je verodostojno;\u003C\u002Fli>\u003Cli>dokazi su nepotpuni ili kontradiktorni;\u003C\u002Fli>\u003Cli>odluka ima značajne tehničke, finansijske ili istraživačke posledice;\u003C\u002Fli>\u003Cli>očekuje se da AI sistem deluje autonomno na osnovu zaključka.\u003C\u002Fli>\u003C\u002Ful>\n\u003Cp>Metodološka dubina stoga treba da se skalira sa epistemičkim rizikom.\u003C\u002Fp>\n\u003Ch2>Od statičnog podsticaja do adaptivne politike zaključivanja\u003C\u002Fh2>\n\u003Cp>Kada se generalizuje, okvir više ne mora da postoji kao jedan ogroman podsticaj.\u003C\u002Fp>\n\u003Cp>Praktičan sistem može dinamički da odluči koje kontrole zadatak zahteva.\u003C\u002Fp>\n\u003Cblockquote class=\"border-l-4 border-gray-300 pl-4 italic\">Jednostavan zadatak → direktan odgovor\nAnalitički zadatak → strukturisana provera dokaza\nZadatak sa visokom neizvesnošću → konkurentske hipoteze\nZadatak osetljiv na uokvirivanje → Invarijantnost upita\nHipoteza sa visokim posledicama → falsifikacija i validacija u domenu\u003C\u002Fblockquote>\n\u003Cp>Ovo transformiše metodologiju iz fiksnog šablona upita u adaptivnu politiku zaključivanja.\u003C\u002Fp>\n\u003Cp>Sistem ne zaključuje uvek maksimalno. Zaključuje onoliko rigorozno koliko zadatak zahteva.\u003C\u002Fp>\n\u003Ch2>Praktičan opšti okvir\u003C\u002Fh2>\n\u003Cp>Kompletan proces može se sumirati na sledeći način:\u003C\u002Fp>\n\u003Col>\u003Cli>\u003Cb>Normalizuj problem.\u003C\u002Fb> Ukloni ugrađene zaključke iz definicije zadatka gde je to prikladno.\u003C\u002Fli>\u003Cli>\u003Cb>Identifikuj dokaze.\u003C\u002Fb> Razdvoji opažanja od interpretacija.\u003C\u002Fli>\u003Cli>\u003Cb>Izloži pretpostavke.\u003C\u002Fb> Zabeleži tvrdnje koje još nisu utvrđene.\u003C\u002Fli>\u003Cli>\u003Cb>Generiši ozbiljne alternative.\u003C\u002Fb> Ne dozvoli da se prva hipoteza takmiči samo protiv slabih slamnatih argumenata.\u003C\u002Fli>\u003Cli>\u003Cb>Sačuvaj poreklo.\u003C\u002Fb> Održi tvrdnje sledljivim do njihovih izvora.\u003C\u002Fli>\u003Cli>\u003Cb>Izvedi očekivanja.\u003C\u002Fb> Definiši šta svaka hipoteza predviđa.\u003C\u002Fli>\u003Cli>\u003Cb>Traži protivdokaze.\u003C\u002Fb> Aktivno traži opažanja koja preferirana hipoteza slabo objašnjava.\u003C\u002Fli>\u003Cli>\u003Cb>Izvrši diskriminatorne testove.\u003C\u002Fb> Preferiraj dokaze koji razlikuju objašnjenja.\u003C\u002Fli>\u003Cli>\u003Cb>Testiraj zavisnost od uokvirivanja.\u003C\u002Fb> Primeni Invarijantnost upita kada korisničko uokvirivanje može uticati na rezultat.\u003C\u002Fli>\u003Cli>\u003Cb>Primeni validatore domena.\u003C\u002Fb> Koristi kriterijume specifične za disciplinu relevantne za problem.\u003C\u002Fli>\u003Cli>\u003Cb>Rekalibriši pouzdanje.\u003C\u002Fb> Dozvoli da zaključak oslabi, ostane nerešen ili se promeni.\u003C\u002Fli>\u003Cli>\u003Cb>Sačuvaj sledljivost.\u003C\u002Fb> Učini put od zaključka nazad do dokaza preglednim.\u003C\u002Fli>\u003C\u002Fol>\n\u003Ch2>Šta okvir ne tvrdi\u003C\u002Fh2>\n\u003Cul>\u003Cli>Ne garantuje istinit izlaz.\u003C\u002Fli>\u003Cli>Ne eliminiše halucinacije.\u003C\u002Fli>\u003Cli>Ne čini LLM stručnjakom za domen.\u003C\u002Fli>\u003Cli>Ne dokazuje da su stabilni zaključci tačni.\u003C\u002Fli>\u003Cli>Ne eliminiše pristrasnosti zajedničke svim prolazima zaključivanja.\u003C\u002Fli>\u003Cli>Ne zamenjuje eksperimente, merenja ili primarne izvore.\u003C\u002Fli>\u003Cli>Ne garantuje da su sve relevantne hipoteze generisane.\u003C\u002Fli>\u003Cli>Ne podrazumeva da je više zaključivanja uvek bolje.\u003C\u002Fli>\u003Cli>Ne tvrdi da je kompletan okvir već validiran kao standardizovana akademska metodologija.\u003C\u002Fli>\u003C\u002Ful>\n\u003Cp>Njegova tvrdnja je uža i odbranjivija: analitički AI sistemi mogu se metodološki ojačati kada se pretpostavke, uokvirivanje, konkurentska objašnjenja, protivdokazi i validacija u domenu obrađuju eksplicitno, umesto da se u potpunosti prepuste jednoj neograničenoj generaciji.\u003C\u002Fp>\n\u003Ch2>Četiri članka postaju jedan sistem zaključivanja\u003C\u002Fh2>\n\u003Cp>Prethodni članci sada se mogu razumeti ne kao odvojene tehnike upita, već kao komponente jedne arhitekture zaključivanja.\u003C\u002Fp>\n\u003Ctable class=\"w-full border-collapse\">\u003Ctr>\u003Ctd class=\"border border-gray-300 px-4 py-2\">Članak\u003C\u002Ftd>\u003Ctd class=\"border border-gray-300 px-4 py-2\">Funkcija u okviru\u003C\u002Ftd>\u003C\u002Ftr>\u003Ctr>\u003Ctd class=\"border border-gray-300 px-4 py-2\">\u003Ca href=\"https:\u002F\u002Fstajic.de\u002Fsr\u002Fblog\u002Fbeyond-prompt-engineering-a-methodology-for-more-reliable-ai-reasoning\">Izvan inženjeringa upita\u003C\u002Fa>\u003C\u002Ftd>\u003Ctd class=\"border border-gray-300 px-4 py-2\">Definiše prelaz sa generisanja odgovora na metodološko zaključivanje.\u003C\u002Ftd>\u003C\u002Ftr>\u003Ctr>\u003Ctd class=\"border border-gray-300 px-4 py-2\">\u003Ca href=\"https:\u002F\u002Fstajic.de\u002Fsr\u002Fblog\u002Fthe-prompt-is-part-of-the-bias-how-ai-framing-shapes-reasoning\">Upit je deo pristrasnosti\u003C\u002Fa>\u003C\u002Ftd>\u003Ctd class=\"border border-gray-300 px-4 py-2\">Tretira korisničku formulaciju kao mogući izvor pristrasnosti u zaključivanju.\u003C\u002Ftd>\u003C\u002Ftr>\u003Ctr>\u003Ctd class=\"border border-gray-300 px-4 py-2\">\u003Ca href=\"https:\u002F\u002Fstajic.de\u002Fsr\u002Fblog\u002Fprompt-invariance-does-the-conclusion-survive-the-prompt\">Invarijantnost upita\u003C\u002Fa>\u003C\u002Ftd>\u003Ctd class=\"border border-gray-300 px-4 py-2\">Testira da li zaključak preživljava značajne promene u uokvirivanju.\u003C\u002Ftd>\u003C\u002Ftr>\u003Ctr>\u003Ctd class=\"border border-gray-300 px-4 py-2\">\u003Ca href=\"https:\u002F\u002Fstajic.de\u002Fsr\u002Fblog\u002Ffalsification-for-ai-reasoning-from-answers-to-tested-hypotheses\">Falsifikacija za AI zaključivanje\u003C\u002Fa>\u003C\u002Ftd>\u003Ctd class=\"border border-gray-300 px-4 py-2\">Testira da li hipoteze preživljavaju opovrgavajuće dokaze i ozbiljne alternative.\u003C\u002Ftd>\u003C\u002Ftr>\u003Ctr>\u003Ctd class=\"border border-gray-300 px-4 py-2\">\u003Cb>Od istraživačkog protokola do opšteg okvira za AI zaključivanje\u003C\u002Fb>\u003C\u002Ftd>\u003Ctd class=\"border border-gray-300 px-4 py-2\">Kombinuje kontrole u jezgro nezavisno od domena sa validacijom specifičnom za domen.\u003C\u002Ftd>\u003C\u002Ftr>\u003C\u002Ftable>\n\u003Cp>Zajedno opisuju tranziciju:\u003C\u002Fp>\n\u003Cblockquote class=\"border-l-4 border-gray-300 pl-4 italic\">Upit → odgovor\npostaje\nProblem → dokazi → hipoteze → izazov → validacija → kalibrisani zaključak\u003C\u002Fblockquote>\n\u003Ch2>Centralni princip\u003C\u002Fh2>\n\u003Cp>Metodologija je počela jednostavnim zapažanjem: postavljanje pitanja moćnom modelu za zaključivanje ne znači automatski da će svaka korisna sposobnost zaključivanja dostupna modelu biti iskorišćena.\u003C\u002Fp>\n\u003Cp>Rešenje nije da se maksimalno zaključivanje forsira u svakoj interakciji.\u003C\u002Fp>\n\u003Cp>Rešenje je da se identifikuje koji kontroli zaključivanja su važni za zadatak i da se oni eksplicitno navedu.\u003C\u002Fp>\n\u003Cblockquote class=\"border-l-4 border-gray-300 pl-4 italic\">Dobra AI metodologija ne govori modelu do kog zaključka treba da dođe. Ona definiše šta zaključak mora da preživi pre nego što bude prihvaćen.\u003C\u002Fblockquote>\n\u003Cp>Taj princip je prenosiv.\u003C\u002Fp>\n\u003Cp>U istorijskom istraživanju, zaključak mora da preživi hronologiju i kritiku izvora. U otklanjanju grešaka, mora da preživi reprodukciju i diskriminišuće testove. U arhitekturi, mora da preživi zahteve i operativna ograničenja. U strategiji, mora da preživi tržišne dokaze i kriterijume neuspeha.\u003C\u002Fp>\n\u003Cp>Validatori se menjaju.\u003C\u002Fp>\n\u003Cp>Metodološka disciplina ostaje.\u003C\u002Fp>\n\u003Cblockquote class=\"border-l-4 border-gray-300 pl-4 italic\">Cilj nije model koji uvek misli duže. To je sistem koji zna kada odgovor još nije opravdan.\u003C\u002Fblockquote>\n\u003Cdiv class=\"ce-delimiter cdx-block my-8\">\u003C\u002Fdiv>\n\u003Ch2>Istraživački kontekst\u003C\u002Fh2>\n\u003Cp>Kompletan okvir opisan u ovoj seriji je metodološka sinteza, a ne uspostavljeni standardizovani LLM benchmark. Nekoliko srodnih linija istraživanja ipak podržava njegovu centralnu arhitektonsku premisu: kvalitet zaključivanja može se značajno promeniti kada je rešavanje problema pomoću jezičkog modela organizovano kao iterativni ili višefazni proces, a ne kao jedna generacija.\u003C\u002Fp>\n\u003Cp>ReAct kombinuje zaključivanje sa akcijama i spoljnim zapažanjima i pokazao je prednosti u odgovaranju na pitanja, proveri činjenica i interaktivnim zadacima odlučivanja. Tree of Thoughts istražuje više kandidata za puteve zaključivanja uz evaluaciju i vraćanje unazad, umesto da se odmah opredeli za jedan lanac. Self-Refine je pokazao poboljšanja u raznovrsnim zadacima iteriranjem između generisanja, povratne informacije i rafiniranja. Reflexion je pokazao da jezički agenti mogu da koriste verbalne povratne informacije iz prethodnih pokušaja da poboljšaju kasnije odluke bez ažuriranja težina modela.\u003C\u002Fp>\n\u003Cp>Ovi pristupi ne implementiraju Prompt Invariance niti ovde predloženi okvir usmeren na falsifikaciju. Oni pružaju nezavisne dokaze za širu ideju da je procedura u vreme zaključivanja važna: sposobnost modela i proces kroz koji se ta sposobnost koristi nisu identični.\u003C\u002Fp>\n\u003Cp>Doprinos ove serije je organizovanje tog uvida oko epistemičkih kontrola: razdvajanje dokaza, praćenje pretpostavki, analiza uokvirivanja prompta, konkurentske hipoteze, falsifikacija, poreklo, validacija u domenu i kalibrisana neizvesnost.\u003C\u002Fp>\n\u003Ch3>Odabrane reference\u003C\u002Fh3>\n\u003Cul>\u003Cli>Yao, S. et al. — \u003Ci>ReAct: Synergizing Reasoning and Acting in Language Models\u003C\u002Fi>. ICLR, 2023.\u003C\u002Fli>\u003Cli>Yao, S. et al. — \u003Ci>Tree of Thoughts: Deliberate Problem Solving with Large Language Models\u003C\u002Fi>. NeurIPS, 2023.\u003C\u002Fli>\u003Cli>Madaan, A. et al. — \u003Ci>Self-Refine: Iterative Refinement with Self-Feedback\u003C\u002Fi>. NeurIPS, 2023.\u003C\u002Fli>\u003Cli>Shinn, N. et al. — \u003Ci>Reflexion: Language Agents with Verbal Reinforcement Learning\u003C\u002Fi>. NeurIPS, 2023.\u003C\u002Fli>\u003Cli>Jhaveri, A. R., GX-Chen, A., Sucholutsky, I. & Choi, E. — \u003Ci>Failing to Falsify: Evaluating and Mitigating Confirmation Bias in Language Models\u003C\u002Fi>. 2026.\u003C\u002Fli>\u003Cli>Brucks, M. S. & Toubia, O. — \u003Ci>Prompt Architecture Induces Methodological Artifacts in Large Language Models\u003C\u002Fi>. PLOS ONE, 2025.\u003C\u002Fli>\u003C\u002Ful>\n\u003Cdiv class=\"ce-delimiter cdx-block my-8\">\u003C\u002Fdiv>\n\u003Ch2>Serija o metodologiji AI zaključivanja\u003C\u002Fh2>\n\u003Col>\u003Cli>\u003Ca href=\"https:\u002F\u002Fstajic.de\u002Fsr\u002Fblog\u002Fbeyond-prompt-engineering-a-methodology-for-more-reliable-ai-reasoning\">\u003Cb>Beyond Prompt Engineering: A Methodology for More Reliable AI Reasoning\u003C\u002Fb>\u003C\u002Fa> — zašto sposobnost modela sama po sebi nije dovoljna.\u003C\u002Fli>\u003Cli>\u003Ca href=\"https:\u002F\u002Fstajic.de\u002Fsr\u002Fblog\u002Fthe-prompt-is-part-of-the-bias-how-ai-framing-shapes-reasoning\">\u003Cb>The Prompt Is Part of the Bias\u003C\u002Fb>\u003C\u002Fa> — kako formulacija zadatka može da utiče na okruženje za zaključivanje.\u003C\u002Fli>\u003Cli>\u003Ca href=\"https:\u002F\u002Fstajic.de\u002Fsr\u002Fblog\u002Fprompt-invariance-does-the-conclusion-survive-the-prompt\">\u003Cb>Prompt Invariance: Does the Conclusion Survive the Prompt?\u003C\u002Fb>\u003C\u002Fa> — testiranje stabilnosti zaključka kroz alternativna uokvirivanja.\u003C\u002Fli>\u003Cli>\u003Ca href=\"https:\u002F\u002Fstajic.de\u002Fsr\u002Fblog\u002Ffalsification-for-ai-reasoning-from-answers-to-tested-hypotheses\">\u003Cb>Falsification for AI Reasoning: From Answers to Tested Hypotheses\u003C\u002Fb>\u003C\u002Fa> — konkurentske hipoteze, protivdokazi i diskriminišući testovi.\u003C\u002Fli>\u003Cli>\u003Cb>From Research Protocol to a General AI Reasoning Framework\u003C\u002Fb> — integrisanje metodologije u višekratno upotrebljivo jezgro zaključivanja nezavisno od domena.\u003C\u002Fli>\u003Cli>Praktična primena istog okvira za zaključivanje u gejming AI — uključujući pomoćnike u igrama, autonomne agente, znanje osetljivo na zakrpe i validaciju stanja igre uživo — istražena je u \u003Ca href=\"https:\u002F\u002Ffigure.rocks\u002Fde\u002Fblog\u002Fwhen-gaming-ai-sounds-right-but-isn-t-the-reasoning-problem-behind-game-assistants-and-agents\">When Gaming AI Sounds Right but Isn't: The Reasoning Problem Behind Game Assistants and Agents on figure.rocks.\u003C\u002Fa>\u003C\u002Fli>\u003C\u002Fol>",{"time":212,"blocks":213,"version":1224},1789803592445,[214,220,225,230,238,243,248,254,259,264,269,274,279,284,289,294,299,304,309,315,320,325,330,335,340,345,350,355,360,365,377,382,387,392,397,402,407,412,417,422,427,432,437,442,447,452,457,462,467,472,477,482,493,498,503,508,513,523,528,533,538,543,548,578,583,588,593,598,603,615,620,625,630,634,639,653,657,662,675,679,684,696,700,705,717,722,727,738,743,748,753,758,763,768,773,778,783,788,793,798,803,808,813,818,823,828,860,865,870,875,880,886,892,898,904,912,918,923,929,935,947,953,959,971,977,982,988,993,998,1003,1008,1013,1018,1038,1043,1058,1063,1068,1073,1096,1101,1106,1111,1116,1121,1126,1131,1136,1141,1146,1151,1156,1161,1166,1171,1176,1181,1186,1191,1203,1207,1212],{"id":215,"data":216,"type":218,"tunes":219},"ESemLsLo8j",{"text":217},"Metodologija razvijena kroz ovu seriju započela je istraživačkim problemom: kako AI model može pomoći u istraživanju složenog pitanja, a da pritom ne pojačava samo pretpostavke koje su već prisutne u korisničkom upitu?","paragraph",{},{"id":221,"data":222,"type":218,"tunes":224},"4Y0K3YWc6x",{"text":223},"Taj problem na prvi pogled deluje kao da pripada istorijskom ili akademskom istraživanju. U stvarnosti, on je znatno širi.",{},{"id":226,"data":227,"type":218,"tunes":229},"Tdh0-PzDMD",{"text":228},"Otklanjanje grešaka u softveru, arhitektonske odluke, tehnička dijagnostika, analiza bezbednosti, strategija proizvoda i mnogi oblici podrške pri odlučivanju dele istu osnovnu strukturu. Problem je predstavljen. Neki dokazi su dostupni. Jedno ili više objašnjenja deluju uverljivo. Pretpostavke ulaze u analizu. Sistem mora da utvrdi koji zaključak ima najbolju potporu.",{},{"id":231,"data":232,"type":236,"tunes":237},"y1qR6aL83j",{"text":233,"caption":234,"alignment":235},"Domen se menja. Epistemički problem često ostaje isti.","","left","quote",{},{"id":239,"data":240,"type":218,"tunes":242},"OSYX72lgMi",{"text":241},"Ovaj članak stoga preduzima sledeći korak: pretvaranje istraživačkog protokola razvijenog u prethodnim člancima u opšti okvir zaključivanja za analitički rad uz podršku veštačke inteligencije.",{},{"id":244,"data":245,"type":218,"tunes":247},"0nk7KQPJLF",{"text":246},"Okvir se direktno oslanja na četiri prethodna sloja: \u003Ca href=\"https:\u002F\u002Fstajic.de\u002Fsr\u002Fblog\u002Fbeyond-prompt-engineering-a-methodology-for-more-reliable-ai-reasoning\">Beyond Prompt Engineering: A Methodology for More Reliable AI Reasoning\u003C\u002Fa> definiše opšti metodološki problem; \u003Ca href=\"https:\u002F\u002Fstajic.de\u002Fsr\u002Fblog\u002Fthe-prompt-is-part-of-the-bias-how-ai-framing-shapes-reasoning\">The Prompt Is Part of the Bias\u003C\u002Fa> ispituje uokviravanje i zavisnost od upita; \u003Ca href=\"https:\u002F\u002Fstajic.de\u002Fsr\u002Fblog\u002Fprompt-invariance-does-the-conclusion-survive-the-prompt\">Prompt Invariance: Does the Conclusion Survive the Prompt?\u003C\u002Fa> uvodi test robusnosti protiv uokviravanja; i \u003Ca href=\"https:\u002F\u002Fstajic.de\u002Fsr\u002Fblog\u002Ffalsification-for-ai-reasoning-from-answers-to-tested-hypotheses\">Falsification for AI Reasoning: From Answers to Tested Hypotheses\u003C\u002Fa> dodaje sistematsku diskonfirmaciju i konkurentske hipoteze.",{},{"id":249,"data":250,"type":42,"tunes":253},"8u01PX2pJ_",{"text":251,"level":252},"Ključna generalizacija",2,{},{"id":255,"data":256,"type":218,"tunes":258},"KX-3wYIvfo",{"text":257},"Generalizacija se ne sastoji u tome da svaki zadatak treba tretirati kao istorijsko istraživanje.",{},{"id":260,"data":261,"type":218,"tunes":263},"Qlnajp4NIg",{"text":262},"To bi bila kategorijalna greška.",{},{"id":265,"data":266,"type":218,"tunes":268},"K_gM2XnB_E",{"text":267},"Istorijsko istraživanje zavisi od hronologije, porekla, prenosa, kritike izvora i arhivske potpunosti. Otklanjanje softverskih grešaka zavisi od logova, konfiguracije, ponovljivosti, stanja izvršavanja i kontrolisanih testova. Arhitektura zavisi od zahteva, ograničenja, interfejsa, atributa kvaliteta i operativnih kompromisa.",{},{"id":270,"data":271,"type":218,"tunes":273},"EQnEh291Ja",{"text":272},"Ono što se može generalizovati jeste proces zaključivanja koji okružuje te domenski specifične oblike dokaza.",{},{"id":275,"data":276,"type":236,"tunes":278},"041iVGE35A",{"text":277,"caption":234,"alignment":235},"Okvir je u svojoj suštini nezavisan od domena, ali nikada nije lišen domena.",{},{"id":280,"data":281,"type":218,"tunes":283},"GsCAv2bn5D",{"text":282},"Ova razlika je fundamentalna. Univerzalna metodologija zaključivanja ne može zameniti stručnost. Umesto toga, ona može definisati kako treba postupati sa dokazima, pretpostavkama, hipotezama, kontradikcijama i neizvesnošću pre nego što domenska stručnost donese konačni sud.",{},{"id":285,"data":286,"type":42,"tunes":288},"iGG8FYrJ2O",{"text":287,"level":252},"Jezgro zaključivanja nezavisno od domena",{},{"id":290,"data":291,"type":218,"tunes":293},"GRuYGmzxLe",{"text":292},"Kroz različite analitičke domene može se primeniti isti redosled visokog nivoa:",{},{"id":295,"data":296,"type":236,"tunes":298},"lgjHVx7HZI",{"text":297,"caption":234,"alignment":235},"Problem → dekompozicija → dokazi → pretpostavke → konkurentske hipoteze → predviđanja → protividokazi → diskriminišući testovi → domenska validacija → rekalibracija poverenja → zaključak",{},{"id":300,"data":301,"type":218,"tunes":303},"AKTY6_aOdu",{"text":302},"Ovaj proces namerno sprečava da prvo koherentno objašnjenje modela po automatizmu postane konačan odgovor.",{},{"id":305,"data":306,"type":218,"tunes":308},"eipK0zk3WD",{"text":307},"Umesto toga, prvo objašnjenje postaje kandidat.",{},{"id":310,"data":311,"type":42,"tunes":314},"yU78N3Sd2e",{"text":312,"level":313},"1. Definišite stvarni problem",3,{},{"id":316,"data":317,"type":218,"tunes":319},"fDuyj6nSv-",{"text":318},"Pre generisanja rešenja, sistem bi trebalo da utvrdi šta se zapravo traži.",{},{"id":321,"data":322,"type":218,"tunes":324},"sctkg30ATQ",{"text":323},"Korisnički zahtevi često sadrže i problem i predloženu dijagnozu:",{},{"id":326,"data":327,"type":236,"tunes":329},"HvF-G0rwXA",{"text":328,"caption":234,"alignment":235},"Zašto nginx izaziva neuspehe povezivanja na moj API?",{},{"id":331,"data":332,"type":218,"tunes":334},"mFKfHfGIDV",{"text":333},"Stvarni problem, međutim, može biti:",{},{"id":336,"data":337,"type":236,"tunes":339},"CbLWis4-Bu",{"text":338,"caption":234,"alignment":235},"Šta izaziva neuspehe povezivanja na API?",{},{"id":341,"data":342,"type":218,"tunes":344},"cMcvbsDaMC",{"text":343},"Razlika je lingvistički mala, a metodološki ogromna.",{},{"id":346,"data":347,"type":218,"tunes":349},"E_kQoBwBzK",{"text":348},"Prva formulacija ugrađuje uzročnu hipotezu. Druga izlaže tu hipotezu konkurenciji.",{},{"id":351,"data":352,"type":218,"tunes":354},"M86BF9OUO9",{"text":353},"Ovo je upravo problem uokvirivanja ispitan u tekstu \u003Ca href=\"https:\u002F\u002Fstajic.de\u002Fsr\u002Fblog\u002Fthe-prompt-is-part-of-the-bias-how-ai-framing-shapes-reasoning\">The Prompt Is Part of the Bias\u003C\u002Fa>.",{},{"id":356,"data":357,"type":42,"tunes":359},"8jN_GEWnLD",{"text":358,"level":313},"2. Razdvojte činjenice, pretpostavke i nepoznate stavke",{},{"id":361,"data":362,"type":218,"tunes":364},"4-45ArOJIC",{"text":363},"Pouzdan proces rezonovanja mora sprečiti neprimetno spajanje zapažanja i interpretacija.",{},{"id":366,"data":367,"type":375,"tunes":376},"gn7a0JRFIn",{"meta":368,"items":369,"style":374},{},[370,371,372,373],"\u003Cb>Činjenica:\u003C\u002Fb> direktno potkrepljena dostupnim dokazima.","\u003Cb>Interpretacija:\u003C\u002Fb> objašnjenje izvedeno iz činjenica.","\u003Cb>Pretpostavka:\u003C\u002Fb> tvrdnja neophodna za trenutni tok rezonovanja, ali nije nezavisno utvrđena.","\u003Cb>Nepoznato:\u003C\u002Fb> informacija neophodna za pouzdanije razlikovanje, ali trenutno nedostupna.","unordered","list",{},{"id":378,"data":379,"type":218,"tunes":381},"7gqnnnAyNH",{"text":380},"Ova klasifikacija može delovati elementarno, ali rešava uobičajeni obrazac greške u analizama koje generiše veštačka inteligencija: pretpostavke bivaju ugrađene u tečan tekst i kasnije se ponovo pojavljuju kao da su već utvrđene.",{},{"id":383,"data":384,"type":236,"tunes":386},"wakPN0U08S",{"text":385,"caption":234,"alignment":235},"Sledivost počinje sprečavanjem zaključka da se preruši u dokaz.",{},{"id":388,"data":389,"type":42,"tunes":391},"UPatPWjaiP",{"text":390,"level":313},"3. Generišite konkurentna objašnjenja",{},{"id":393,"data":394,"type":218,"tunes":396},"zuRICKxeRX",{"text":395},"Kandidat za objašnjenje ne bi trebalo da se ocenjuje izolovano kada postoje verodostojne alternative.",{},{"id":398,"data":399,"type":218,"tunes":401},"AvfAw-zrKJ",{"text":400},"Sistem bi stoga trebalo da generiše nekoliko verovatnih hipoteza pre nego što se opredeli za jednu.",{},{"id":403,"data":404,"type":218,"tunes":406},"5JVkk_l5m9",{"text":405},"Svrha nije veštački brejnstorming. Alternative bi trebalo da predstavljaju suštinski različite mehanizme sposobne da objasne zapažanja.",{},{"id":408,"data":409,"type":236,"tunes":411},"6ZI39jnt9v",{"text":410,"caption":234,"alignment":235},"Preferirana hipoteza nije pobedila ako nijednom ozbiljnom konkurentu nije bilo dozvoljeno da uđe u testiranje.",{},{"id":413,"data":414,"type":218,"tunes":416},"WGpU3HSfMr",{"text":415},"Ovaj koncept je centralan za \u003Ca href=\"https:\u002F\u002Fstajic.de\u002Fsr\u002Fblog\u002Ffalsification-for-ai-reasoning-from-answers-to-tested-hypotheses\">Falsifikaciju za AI rezonovanje\u003C\u002Fa>, gde se hipoteze procenjuju kroz očekivana zapažanja, kontra-dokaze i diskriminativne testove, a ne kroz nagomilanu prozu podrške.",{},{"id":418,"data":419,"type":42,"tunes":421},"mUW1lRBq9a",{"text":420,"level":313},"4. Očuvajte poreklo dokaza",{},{"id":423,"data":424,"type":218,"tunes":426},"m2TM2yNjau",{"text":425},"Dokazi treba da ostanu sledljivi do svog izvora.",{},{"id":428,"data":429,"type":236,"tunes":431},"3w2fzBzVH4",{"text":430,"caption":234,"alignment":235},"Zaključak → tumačenje → zapažanje → izvor",{},{"id":433,"data":434,"type":218,"tunes":436},"rkoICmxwZT",{"text":435},"Linija u logu, zvanična tehnička specifikacija, eksperiment, primarni istorijski dokument, stručno tumačenje i sažetak generisan od strane veštačke inteligencije nemaju identičan dokazni status.",{},{"id":438,"data":439,"type":218,"tunes":441},"8ut4cK6e27",{"text":440},"Okvir stoga ne skladišti samo informacije. On treba da sačuva dovoljno metapodataka kako bi se znalo odakle tvrdnja potiče i koliko je konačni zaključak udaljen od prvobitnog zapažanja.",{},{"id":443,"data":444,"type":42,"tunes":446},"hZ6BZYfcIo",{"text":445,"level":313},"5. Izvedite predviđanja pre objašnjavanja rezultata",{},{"id":448,"data":449,"type":218,"tunes":451},"aisk7gG2yj",{"text":450},"Kada hipoteze postoje, svaka bi trebalo da generiše očekivanja.",{},{"id":453,"data":454,"type":218,"tunes":456},"IBsZFlw9pf",{"text":455},"Ako je hipoteza tačna, šta bi trebalo da uočimo?",{},{"id":458,"data":459,"type":218,"tunes":461},"Kykmkn9Dtx",{"text":460},"Ako je alternativa tačna, šta bi trebalo da bude drugačije?",{},{"id":463,"data":464,"type":218,"tunes":466},"iS8nPIkYVU",{"text":465},"Izvođenje očekivanja pre tumačenja svih dostupnih dokaza pomaže da se spreči naknadno uklapanje svakog zapažanja u preferirani narativ.",{},{"id":468,"data":469,"type":42,"tunes":471},"1rrToFbYF5",{"text":470,"level":313},"6. Potraga za kontra-dokazima",{},{"id":473,"data":474,"type":218,"tunes":476},"NSjS51xlU6",{"text":475},"Generativni model je prirodno efikasan u konstruisanju koherentne podrške za verovatnu ideju. Zbog toga je opovrgavanje posebno važno.",{},{"id":478,"data":479,"type":218,"tunes":481},"jPSoTMS13f",{"text":480},"Sistem bi trebalo izričito da pita:",{},{"id":483,"data":484,"type":375,"tunes":492},"DfULqhWZpB",{"meta":485,"items":486,"style":374},{},[487,488,489,490,491],"Koje bi zapažanje značajno oslabilo ovo objašnjenje?","Koje dokaze hipoteza slabo objašnjava?","Koja alternativa objašnjava ista zapažanja sa manje pretpostavki?","Koja očekivana zapažanja nedostaju?","Da li bi se prividna podrška mogla objasniti nekim drugim mehanizmom?",{},{"id":494,"data":495,"type":218,"tunes":497},"rM5aFAv4Ch",{"text":496},"Ovaj princip je detaljno razrađen u tekstu \u003Ca href=\"https:\u002F\u002Fstajic.de\u002Fsr\u002Fblog\u002Ffalsification-for-ai-reasoning-from-answers-to-tested-hypotheses\">Falsifikacija za AI rezonovanje: od odgovora do testiranih hipoteza\u003C\u002Fa>.",{},{"id":499,"data":500,"type":42,"tunes":502},"sWwUSTmVGW",{"text":501,"level":313},"7. Testirajte zavisnost od prompta",{},{"id":504,"data":505,"type":218,"tunes":507},"wvQwo5fB4W",{"text":506},"Samo testiranje dokaza ne otkriva da li analiza modela ostaje preterano zavisna od formulacije korisnika.",{},{"id":509,"data":510,"type":218,"tunes":512},"Z1AJX_Zx0J",{"text":511},"Za dovoljno važne probleme, zaključak se stoga može ponovo testirati pod alternativnim legitimnim okvirima.",{},{"id":514,"data":515,"type":375,"tunes":522},"R9CxBqiZy5",{"meta":516,"items":517,"style":374},{},[518,519,520,521],"\u003Cb>Originalno:\u003C\u002Fb> sačuvati formulaciju korisnika.","\u003Cb>Slepo:\u003C\u002Fb> ukloniti preferirani zaključak.","\u003Cb>Invertovano:\u003C\u002Fb> staviti u prvi plan najjaču alternativu.","\u003Cb>Adversarijalno:\u003C\u002Fb> namerno tražiti najjači izazov zasnovan na dokazima.",{},{"id":524,"data":525,"type":218,"tunes":527},"Z6ReUs6ThD",{"text":526},"Ovo je metod invarijantnosti upita predstavljen u tekstu \u003Ca href=\"https:\u002F\u002Fstajic.de\u002Fsr\u002Fblog\u002Fprompt-invariance-does-the-conclusion-survive-the-prompt\">Prompt Invariance: Does the Conclusion Survive the Prompt?\u003C\u002Fa>.",{},{"id":529,"data":530,"type":218,"tunes":532},"i72azJfoHd",{"text":531},"Njegova funkcija nije da dokaže da je stabilan odgovor tačan. Njegova funkcija je da razotkrije zaključke koji se menjaju prvenstveno zato što se promenio okvir.",{},{"id":534,"data":535,"type":42,"tunes":537},"PQJr3I5KR6",{"text":536,"level":313},"8. Primenite validatore specifične za domen",{},{"id":539,"data":540,"type":218,"tunes":542},"30EnJRKLgj",{"text":541},"Ovde opšti okvir namerno prestaje da bude univerzalan.",{},{"id":544,"data":545,"type":218,"tunes":547},"HfkiK88IcE",{"text":546},"Svaki domen zahteva sopstvena pravila za utvrđivanje da li je objašnjenje zaista verodostojno.",{},{"id":549,"data":550,"type":576,"tunes":577},"zinQCXwjug",{"content":551,"stretched":43,"withHeadings":14},[552,555,558,561,564,567,570,573],[553,554],"Domen","Tipični validatori",[556,557],"Istorijsko istraživanje","Hronologija, provenijencija, geografska verovatnoća, kanali prenosa, primarni izvori, istoriografija",[559,560],"Otklanjanje grešaka u softveru","Dnevnici (logs), reprodukcija, stanje izvršavanja, konfiguracija, kontrolisane promene, korelacija grešaka",[562,563],"Softverska arhitektura","Zahtevi, ograničenja, skalabilnost, održivost, bezbednost, operativnost, troškovi",[565,566],"Analiza bezbednosti","Telemetrija, putanja napada, dozvole, uočljivi indikatori, ponovljivost, model pretnji",[568,569],"Strategija proizvoda","Dokazi od korisnika, ponašanje pri određivanju cena, konverzija, alternative, tržišna ograničenja, kriterijumi neuspeha",[571,572],"Upravljanje projektima","Obim, zavisnosti, resursi, rizici, kriterijumi prihvatanja, ključne tačke, ograničenja zainteresovanih strana",[574,575],"Naučna analiza","Dizajn eksperimenta, kvalitet merenja, kontrole, ponovljivost, statistički dokazi, alternativna objašnjenja","table",{},{"id":579,"data":580,"type":218,"tunes":582},"uc0RyUCFOo",{"text":581},"Okvir kontroliše kako se tvrdnje obrađuju. Validatori domena određuju da li te tvrdnje preživljavaju kontakt sa stvarnošću.",{},{"id":584,"data":585,"type":42,"tunes":587},"lAvhdCVN32",{"text":586,"level":313},"9. Rekalibrišite nivo pouzdanosti",{},{"id":589,"data":590,"type":218,"tunes":592},"h5PWNd0iA4",{"text":591},"Konačni zaključak ne bi trebalo automatski da zadrži nivo pouzdanosti početnog odgovora.",{},{"id":594,"data":595,"type":218,"tunes":597},"PcokJnli0n",{"text":596},"Pouzdanost treba ponovo izračunati nakon što se razmotre alternative, protivdokazi, varijacije upita i validacija domena.",{},{"id":599,"data":600,"type":218,"tunes":602},"4-JaOGRNdJ",{"text":601},"Korisni ishod stoga može ostati neizvestan.",{},{"id":604,"data":605,"type":375,"tunes":614},"I6F7TJy23-",{"meta":606,"items":607,"style":374},{},[608,609,610,611,612,613],"snažno podržano;","privremeno podržano;","slabo podržano;","nedovoljno determinisano;","značajno opovrgnuto;","nije proverljivo sa trenutnim dokazima.",{},{"id":616,"data":617,"type":236,"tunes":619},"ozNw_amgCI",{"text":618,"caption":234,"alignment":235},"Neizvesnost nije neuspeh rezonovanja kada su sami dokazi neizvesni.",{},{"id":621,"data":622,"type":42,"tunes":624},"LlyeIOmVPC",{"text":623,"level":252},"Isti okvir kroz različite domene",{},{"id":626,"data":627,"type":218,"tunes":629},"dmsjTGPTp7",{"text":628},"Najlakši način da se razume opšti okvir jeste posmatranje kako se ista arhitektura rezonovanja ponaša kada se domen promeni.",{},{"id":631,"data":632,"type":42,"tunes":633},"cU0_n6SCY3",{"text":556,"level":313},{},{"id":635,"data":636,"type":218,"tunes":638},"es1dtKJQlZ",{"text":637},"Problem: utvrditi da li sličnosti između dve intelektualne tradicije ukazuju na istorijski prenos.",{},{"id":640,"data":641,"type":375,"tunes":652},"Nzl1cThLXK",{"meta":642,"items":643,"style":374},{},[644,645,646,647,648,649,650,651],"Odvojiti dokumentovane sličnosti od interpretativnih paralela.","Uporediti direktan prenos, indirektno nasleđe, konvergenciju i retrospektivnu interpretaciju.","Proveriti hronologiju i geografski kontakt.","Potražiti dokumentarne ili terminološke tragove.","Identifikovati dokaze koji se očekuju u slučaju direktnog prenosa.","Potražiti ranije nezavisne primere koji slabe tu hipotezu.","Preformulisati pitanje bez preferirane genealogije.","Donositi zaključke samo na nivou pouzdanosti koji podržavaju sačuvani dokazi.",{},{"id":654,"data":655,"type":42,"tunes":656},"RDf52TTR1X",{"text":559,"level":313},{},{"id":658,"data":659,"type":218,"tunes":661},"V_aQmUWc8S",{"text":660},"Problem: utvrditi zašto API veza povremeno otkazuje.",{},{"id":663,"data":664,"type":375,"tunes":674},"YOUlOo_WTe",{"meta":665,"items":666,"style":374},{},[667,668,669,670,671,672,673],"Odvojiti uočene greške od uzroka na koje sumnja programer.","Generisati hipoteze vezane za proksi, aplikaciju, bazu podataka, mrežu i klijenta.","Utvrditi koje zapise i ponašanje u toku izvršavanja svaka hipoteza predviđa.","Izvršiti testove koji mogu napraviti razliku među njima.","Pokušati reprodukovanje greške uz postepeno eliminisanje slojeva.","Odbaciti objašnjenja koja su u suprotnosti sa kontrolisanim posmatranjima.","Identifikovati prvi potvrđeni mehanizam otkaza umesto najuverljivijeg narativa.",{},{"id":676,"data":677,"type":42,"tunes":678},"tG7q0LkXG8",{"text":562,"level":313},{},{"id":680,"data":681,"type":218,"tunes":683},"Siq7TARR4r",{"text":682},"Problem: izabrati arhitekturu za novu platformu.",{},{"id":685,"data":686,"type":375,"tunes":695},"PYBaGFi2Ol",{"meta":687,"items":688,"style":374},{},[689,690,691,692,693,694],"Odvojiti zahteve od implementacionih preferencija.","Uporediti modularni monolit, servise i hibridne alternative.","Testirati svaku opciju u odnosu na skalabilnost, strukturu tima, razmeštanje, operativnu složenost i troškove.","Identifikovati koji zahtev zaista iziskuje arhitektonsku složenost.","Potražiti jednostavnija rešenja sposobna da zadovolje ista ograničenja.","Tretirati tehnološku preferenciju kao pretpostavku, a ne kao zahtev.",{},{"id":697,"data":698,"type":42,"tunes":699},"-kikosSmX1",{"text":568,"level":313},{},{"id":701,"data":702,"type":218,"tunes":704},"RW8gaj5yQo",{"text":703},"Problem: utvrditi da li određenu mogućnost proizvoda treba komercijalizovati.",{},{"id":706,"data":707,"type":375,"tunes":716},"JZBAxRuw9D",{"meta":708,"items":709,"style":374},{},[710,711,712,713,714,715],"Odvojiti tehničku izvodljivost od dokazane potražnje.","Definisati alternativne probleme korisnika i alternativna rešenja.","Identifikovati vidljivo ponašanje tržišta predviđeno poslovnom tezom.","Definisati kriterijume neuspeha pre tumačenja rezultata.","Potražiti dokaze o tome da korisnici rešavaju problem na drugačiji način.","Razlikovati interesovanje, spremnost na testiranje i spremnost na plaćanje.",{},{"id":718,"data":719,"type":42,"tunes":721},"NfuFu8q13_",{"text":720,"level":313},"Analiza projekta i isporuke",{},{"id":723,"data":724,"type":218,"tunes":726},"GaM4feM9nZ",{"text":725},"Problem: utvrditi zašto projekat ne ostvaruje planirane rezultate.",{},{"id":728,"data":729,"type":375,"tunes":737},"-G7BLfDmdU",{"meta":730,"items":731,"style":374},{},[732,733,734,735,736],"Odvojiti simptome kao što je kašnjenje od njihovih pretpostavljenih uzroka.","Uporediti obim, zavisnosti, kapacitet, zahteve, upravljanje i tehnički rizik.","Pratiti dokaze kroz planove, odluke, izmene i kriterijume prihvatanja.","Proveriti da li korektivna mera rešava stvarni mehanizam, a ne samo vidljivi simptom.","Ponovo proceniti hipotezu projekta kako se pojavljuju novi dokazi o isporuci.",{},{"id":739,"data":740,"type":42,"tunes":742},"5iAhrZD3wL",{"text":741,"level":252},"Zašto je ovo više od inženjeringa upita",{},{"id":744,"data":745,"type":218,"tunes":747},"dm4MhgnM3L",{"text":746},"Inženjering upita menja instrukciju koja se daje modelu.",{},{"id":749,"data":750,"type":218,"tunes":752},"87q5CSIEmb",{"text":751},"Okvir za rezonovanje menja proces kroz koji je odgovoru dopušteno da postane zaključak.",{},{"id":754,"data":755,"type":236,"tunes":757},"7C_ZI5IHle",{"text":756,"caption":234,"alignment":235},"Inženjering upita optimizuje poziv. Okvir za rezonovanje upravlja procesom zaključivanja.",{},{"id":759,"data":760,"type":218,"tunes":762},"SFq4pwo4Gx",{"text":761},"Ova razlika postaje važna kako se AI sistemi pomeraju od interfejsa za ćaskanje ka agentima, RAG pipeline-ovima, automatizovanom istraživanju, razvojnim alatima i sistemima za podršku odlučivanju.",{},{"id":764,"data":765,"type":218,"tunes":767},"WItU2n6hS2",{"text":766},"Jedan pažljivo dizajniran upit može poboljšati jedan poziv modela. Okvir za rezonovanje može definisati kako više poziva, pronađeni dokazi, rezultati alata i faze validacije interaguju pre nego što sistem donese odgovor.",{},{"id":769,"data":770,"type":42,"tunes":772},"99vYubKOKi",{"text":771,"level":252},"Okvir je povezan sa — ali različit od — postojećih metoda rezonovanja LLM-a",{},{"id":774,"data":775,"type":218,"tunes":777},"y8Zf5V4OPJ",{"text":776},"Širi istraživački pejzaž već sadrži nekoliko važnih pristupa koji pokazuju da se performanse LLM-a mogu poboljšati kada je zaključivanje strukturirano kao proces, a ne kao jedna generacija.",{},{"id":779,"data":780,"type":218,"tunes":782},"gEIVPfVGEd",{"text":781},"\u003Cb>ReAct\u003C\u002Fb> kombinuje rezonovanje sa akcijama i zapažanjima iz spoljašnjeg okruženja. Umesto da se oslanja isključivo na interno znanje modela, model može da deluje, posmatra nove informacije i ažurira svoj sledeći korak.",{},{"id":784,"data":785,"type":218,"tunes":787},"kKS8VaCYmR",{"text":786},"\u003Cb>Self-Refine\u003C\u002Fb> koristi iterativnu generaciju, povratne informacije i rafiniranje, pokazujući da se početni izlaz LLM-a često može poboljšati kroz eksplicitne samopovratne informacije bez dodatne obuke modela.",{},{"id":789,"data":790,"type":218,"tunes":792},"Puh4hZgRfK",{"text":791},"\u003Cb>Reflexion\u003C\u002Fb> koristi verbalne povratne informacije i epizodičnu memoriju kako bi omogućio jezičkim agentima da uče iz prethodnih pokušaja i modifikuju kasnije ponašanje.",{},{"id":794,"data":795,"type":218,"tunes":797},"nAB7uNC8AX",{"text":796},"\u003Cb>Tree of Thoughts\u003C\u002Fb> istražuje više putanja rezonovanja umesto da se odmah opredeli za jedan lanac s leva na desno, omogućavajući pretragu, evaluaciju i vraćanje unazad preko kandidatskih pristupa.",{},{"id":799,"data":800,"type":218,"tunes":802},"B5Q7GKEqmh",{"text":801},"Ove metode se značajno razlikuju po nameni i implementaciji, ali uspostavljaju važnu opštu poentu: struktura u vreme zaključivanja može materijalno promeniti performanse modela.",{},{"id":804,"data":805,"type":218,"tunes":807},"Hj82H7bI5T",{"text":806},"Okvir predložen u ovoj seriji bavi se drugačijim primarnim ciljem.",{},{"id":809,"data":810,"type":236,"tunes":812},"q8bQMqN2Ir",{"text":811,"caption":234,"alignment":235},"Cilj nije jednostavno naterati model da duže traži, više razmišlja ili generiše bolji odgovor. Cilj je učiniti put od dokaza do zaključka otpornijim na uokvirivanje, potvrđivanje i nepotkrepljene pretpostavke.",{},{"id":814,"data":815,"type":218,"tunes":817},"d1bIpPAnpB",{"text":816},"Nepromenljivost upita, testiranje usmereno na falsifikaciju i validacija specifična za domen stoga služe kao epistemičke kontrole, a ne kao generičke tehnike proširenja zaključivanja.",{},{"id":819,"data":820,"type":42,"tunes":822},"eXXYr_pAms",{"text":821,"level":252},"Slojeviti pogled na kvalitet AI rezonovanja",{},{"id":824,"data":825,"type":218,"tunes":827},"DqLa7MmX58",{"text":826},"Rezultujući sistem se može razumeti kao nekoliko slojeva.",{},{"id":829,"data":830,"type":576,"tunes":859},"0NzL-zFxBY",{"content":831,"stretched":43,"withHeadings":14},[832,835,838,841,844,847,850,853,856],[833,834],"Sloj","Pitanje",[836,837],"Razumevanje zadatka","Koji problem zapravo rešavamo?",[839,840],"Dokazi","Šta zapravo znamo?",[842,843],"Pretpostavke","Šta trenutno uzimamo zdravo za gotovo?",[845,846],"Hipoteze","Koji verodostojni mehanizmi bi mogli objasniti dokaze?",[848,849],"Falsifikacija","Koji dokazi bi mogli oslabiti svako objašnjenje?",[851,852],"Nepromenljivost upita","Da li zaključak prekomerno zavisi od uokvirivanja?",[854,855],"Validacija domena","Da li objašnjenje zadovoljava pravila relevantne discipline?",[857,858],"Kalibracija pouzdanja","Koliko snažno treba verovati preostalom zaključku?",{},{"id":861,"data":862,"type":218,"tunes":864},"xm0-PFgN-i",{"text":863},"Odgovor može da padne na bilo kom sloju.",{},{"id":866,"data":867,"type":218,"tunes":869},"eCszY4dwX1",{"text":868},"Može tačno da odgovori na pogrešan problem. Može ispravno da rezonuje na osnovu lažnih dokaza. Može da pronađe tačne činjenice, ali da izabere pogrešno kauzalno objašnjenje. Može da proizvede snažno objašnjenje koje nestaje kada se upit preformuliše. Ili može da preživi sve opšte provere rezonovanja i dalje prekrši ograničenje specifično za domen.",{},{"id":871,"data":872,"type":236,"tunes":874},"hSmlX6vujv",{"text":873,"caption":234,"alignment":235},"Ispravnost nije jedna osobina. Ona je rezultat nekoliko zavisnosti koje istovremeno opstaju.",{},{"id":876,"data":877,"type":42,"tunes":879},"EydNhsxtzy",{"text":878,"level":252},"Kvalitet zaključivanja naspram sposobnosti modela",{},{"id":881,"data":882,"type":218,"tunes":885},"s4Km9d-Rxy",{"text":883,"type":218,"tunes":884},"Ovo nas vraća na jednu od centralnih ideja serije.",{},{},{"id":887,"data":888,"type":218,"tunes":891},"39JzRxeSgF",{"text":889,"type":218,"tunes":890},"Jači model ne podrazumeva automatski da će svaki poziv iskoristiti njegove sposobnosti na najrigorozniji mogući način.",{},{},{"id":893,"data":894,"type":218,"tunes":897},"KEw35qMKGc",{"text":895,"type":218,"tunes":896},"Model može posedovati sposobnost da generiše alternative, pregleda zapise, pretražuje izvore, dovodi u pitanje pretpostavke i revidira zaključak. Da li se sve te operacije zaista odvijaju zavisi od zadatka, podsticanja, dostupnih alata, konteksta i okolne sistemske arhitekture.",{},{},{"id":899,"data":900,"type":218,"tunes":903},"qijWfW7iDp",{"text":901,"type":218,"tunes":902},"Okvir stoga razdvaja dva pitanja:",{},{},{"id":905,"data":906,"type":375,"tunes":911},"9TvQxkmlHA",{"meta":907,"items":908,"style":374},{},[909,910],"\u003Cb>Sposobnost:\u003C\u002Fb> šta model može da uradi?","\u003Cb>Metodološka disciplina:\u003C\u002Fb> koje od tih sposobnosti moraju biti primenjene pre nego što sistem prihvati zaključak?",{},{"id":913,"data":914,"type":218,"tunes":917},"rmrusg3t3h",{"text":915,"type":218,"tunes":916},"Ova razlika bila je polazna tačka za \u003Ca href=\"https:\u002F\u002Fstajic.de\u002Fsr\u002Fblog\u002Fbeyond-prompt-engineering-a-methodology-for-more-reliable-ai-reasoning\">Beyond Prompt Engineering\u003C\u002Fa> i postaje još važnija kada se metodologija generalizuje izvan istraživanja.",{},{},{"id":919,"data":920,"type":42,"tunes":922},"W321yYXV97",{"text":921,"level":252},"Nije svaki zadatak potreban pun okvir",{},{"id":924,"data":925,"type":218,"tunes":928},"QwEqB6r20x",{"text":926,"type":218,"tunes":927},"Okvir ne treba da postane obavezni dodatni teret za svaku interakciju sa AI.",{},{},{"id":930,"data":931,"type":218,"tunes":934},"XxxtP1e8oN",{"text":932,"type":218,"tunes":933},"Mnogi zadaci imaju nisku epistemičku složenost:",{},{},{"id":936,"data":937,"type":375,"tunes":946},"yJw3vKdi9z",{"meta":938,"items":939,"style":374},{},[940,941,942,943,944,945],"formatiraj ovaj JSON;","prevedi ovu rečenicu;","konvertuj ove jedinice;","prepiši ovu poruku;","izvuci ova polja;","sažmi ovaj dostavljeni tekst.",{},{"id":948,"data":949,"type":218,"tunes":952},"c_3UzzXOlF",{"text":950,"type":218,"tunes":951},"Pokretanje četiri varijante podsticaja, tri konkurentske hipoteze i faze falsifikacije za takve zadatke dodalo bi trošak bez proporcionalne vrednosti.",{},{},{"id":954,"data":955,"type":218,"tunes":958},"89oDA_RK5n",{"text":956,"type":218,"tunes":957},"Puna metoda postaje vrednija kada:",{},{},{"id":960,"data":961,"type":375,"tunes":970},"T9axudVitU",{"meta":962,"items":963,"style":374},{},[964,965,966,967,968,969],"odgovor zavisi od interpretacije, a ne od direktnog pronalaženja;","korisnik već ima preferirano objašnjenje;","više kauzalnih mehanizama je verodostojno;","dokazi su nepotpuni ili kontradiktorni;","odluka ima značajne tehničke, finansijske ili istraživačke posledice;","očekuje se da AI sistem deluje autonomno na osnovu zaključka.",{},{"id":972,"data":973,"type":218,"tunes":976},"voWYwXjAQH",{"text":974,"type":218,"tunes":975},"Metodološka dubina stoga treba da se skalira sa epistemičkim rizikom.",{},{},{"id":978,"data":979,"type":42,"tunes":981},"j_hLO6152C",{"text":980,"level":252},"Od statičnog podsticaja do adaptivne politike zaključivanja",{},{"id":983,"data":984,"type":218,"tunes":987},"qS-EwzQq6d",{"text":985,"type":218,"tunes":986},"Kada se generalizuje, okvir više ne mora da postoji kao jedan ogroman podsticaj.",{},{},{"id":989,"data":990,"type":218,"tunes":992},"hU3rHmIWSs",{"text":991},"Praktičan sistem može dinamički da odluči koje kontrole zadatak zahteva.",{},{"id":994,"data":995,"type":236,"tunes":997},"syItl8JEDM",{"text":996,"caption":234,"alignment":235},"Jednostavan zadatak → direktan odgovor\nAnalitički zadatak → strukturisana provera dokaza\nZadatak sa visokom neizvesnošću → konkurentske hipoteze\nZadatak osetljiv na uokvirivanje → Invarijantnost upita\nHipoteza sa visokim posledicama → falsifikacija i validacija u domenu",{},{"id":999,"data":1000,"type":218,"tunes":1002},"Kl_PJTQeZR",{"text":1001},"Ovo transformiše metodologiju iz fiksnog šablona upita u adaptivnu politiku zaključivanja.",{},{"id":1004,"data":1005,"type":218,"tunes":1007},"x8-TRyTmoY",{"text":1006},"Sistem ne zaključuje uvek maksimalno. Zaključuje onoliko rigorozno koliko zadatak zahteva.",{},{"id":1009,"data":1010,"type":42,"tunes":1012},"HN_GUpAvOU",{"text":1011,"level":252},"Praktičan opšti okvir",{},{"id":1014,"data":1015,"type":218,"tunes":1017},"uJr0pPsy7a",{"text":1016},"Kompletan proces može se sumirati na sledeći način:",{},{"id":1019,"data":1020,"type":375,"tunes":1037},"hWyrOdZeoR",{"meta":1021,"items":1023,"style":1036},{"counterType":1022},"numeric",[1024,1025,1026,1027,1028,1029,1030,1031,1032,1033,1034,1035],"\u003Cb>Normalizuj problem.\u003C\u002Fb> Ukloni ugrađene zaključke iz definicije zadatka gde je to prikladno.","\u003Cb>Identifikuj dokaze.\u003C\u002Fb> Razdvoji opažanja od interpretacija.","\u003Cb>Izloži pretpostavke.\u003C\u002Fb> Zabeleži tvrdnje koje još nisu utvrđene.","\u003Cb>Generiši ozbiljne alternative.\u003C\u002Fb> Ne dozvoli da se prva hipoteza takmiči samo protiv slabih slamnatih argumenata.","\u003Cb>Sačuvaj poreklo.\u003C\u002Fb> Održi tvrdnje sledljivim do njihovih izvora.","\u003Cb>Izvedi očekivanja.\u003C\u002Fb> Definiši šta svaka hipoteza predviđa.","\u003Cb>Traži protivdokaze.\u003C\u002Fb> Aktivno traži opažanja koja preferirana hipoteza slabo objašnjava.","\u003Cb>Izvrši diskriminatorne testove.\u003C\u002Fb> Preferiraj dokaze koji razlikuju objašnjenja.","\u003Cb>Testiraj zavisnost od uokvirivanja.\u003C\u002Fb> Primeni Invarijantnost upita kada korisničko uokvirivanje može uticati na rezultat.","\u003Cb>Primeni validatore domena.\u003C\u002Fb> Koristi kriterijume specifične za disciplinu relevantne za problem.","\u003Cb>Rekalibriši pouzdanje.\u003C\u002Fb> Dozvoli da zaključak oslabi, ostane nerešen ili se promeni.","\u003Cb>Sačuvaj sledljivost.\u003C\u002Fb> Učini put od zaključka nazad do dokaza preglednim.","ordered",{},{"id":1039,"data":1040,"type":42,"tunes":1042},"j37GdesnVv",{"text":1041,"level":252},"Šta okvir ne tvrdi",{},{"id":1044,"data":1045,"type":375,"tunes":1057},"we5tXuRA71",{"meta":1046,"items":1047,"style":374},{},[1048,1049,1050,1051,1052,1053,1054,1055,1056],"Ne garantuje istinit izlaz.","Ne eliminiše halucinacije.","Ne čini LLM stručnjakom za domen.","Ne dokazuje da su stabilni zaključci tačni.","Ne eliminiše pristrasnosti zajedničke svim prolazima zaključivanja.","Ne zamenjuje eksperimente, merenja ili primarne izvore.","Ne garantuje da su sve relevantne hipoteze generisane.","Ne podrazumeva da je više zaključivanja uvek bolje.","Ne tvrdi da je kompletan okvir već validiran kao standardizovana akademska metodologija.",{},{"id":1059,"data":1060,"type":218,"tunes":1062},"71CdxmGzXp",{"text":1061},"Njegova tvrdnja je uža i odbranjivija: analitički AI sistemi mogu se metodološki ojačati kada se pretpostavke, uokvirivanje, konkurentska objašnjenja, protivdokazi i validacija u domenu obrađuju eksplicitno, umesto da se u potpunosti prepuste jednoj neograničenoj generaciji.",{},{"id":1064,"data":1065,"type":42,"tunes":1067},"aGiLyuXQib",{"text":1066,"level":252},"Četiri članka postaju jedan sistem zaključivanja",{},{"id":1069,"data":1070,"type":218,"tunes":1072},"6k1TB7nciF",{"text":1071},"Prethodni članci sada se mogu razumeti ne kao odvojene tehnike upita, već kao komponente jedne arhitekture zaključivanja.",{},{"id":1074,"data":1075,"type":576,"tunes":1095},"1wFRu7dDCM",{"content":1076,"stretched":43,"withHeadings":14},[1077,1080,1083,1086,1089,1092],[1078,1079],"Članak","Funkcija u okviru",[1081,1082],"\u003Ca href=\"https:\u002F\u002Fstajic.de\u002Fsr\u002Fblog\u002Fbeyond-prompt-engineering-a-methodology-for-more-reliable-ai-reasoning\">Izvan inženjeringa upita\u003C\u002Fa>","Definiše prelaz sa generisanja odgovora na metodološko zaključivanje.",[1084,1085],"\u003Ca href=\"https:\u002F\u002Fstajic.de\u002Fsr\u002Fblog\u002Fthe-prompt-is-part-of-the-bias-how-ai-framing-shapes-reasoning\">Upit je deo pristrasnosti\u003C\u002Fa>","Tretira korisničku formulaciju kao mogući izvor pristrasnosti u zaključivanju.",[1087,1088],"\u003Ca href=\"https:\u002F\u002Fstajic.de\u002Fsr\u002Fblog\u002Fprompt-invariance-does-the-conclusion-survive-the-prompt\">Invarijantnost upita\u003C\u002Fa>","Testira da li zaključak preživljava značajne promene u uokvirivanju.",[1090,1091],"\u003Ca href=\"https:\u002F\u002Fstajic.de\u002Fsr\u002Fblog\u002Ffalsification-for-ai-reasoning-from-answers-to-tested-hypotheses\">Falsifikacija za AI zaključivanje\u003C\u002Fa>","Testira da li hipoteze preživljavaju opovrgavajuće dokaze i ozbiljne alternative.",[1093,1094],"\u003Cb>Od istraživačkog protokola do opšteg okvira za AI zaključivanje\u003C\u002Fb>","Kombinuje kontrole u jezgro nezavisno od domena sa validacijom specifičnom za domen.",{},{"id":1097,"data":1098,"type":218,"tunes":1100},"zTMfJ0udpF",{"text":1099},"Zajedno opisuju tranziciju:",{},{"id":1102,"data":1103,"type":236,"tunes":1105},"zGNT1eU0mo",{"text":1104,"caption":234,"alignment":235},"Upit → odgovor\npostaje\nProblem → dokazi → hipoteze → izazov → validacija → kalibrisani zaključak",{},{"id":1107,"data":1108,"type":42,"tunes":1110},"SAvL1hIiBl",{"text":1109,"level":252},"Centralni princip",{},{"id":1112,"data":1113,"type":218,"tunes":1115},"NGbjz4ORtZ",{"text":1114},"Metodologija je počela jednostavnim zapažanjem: postavljanje pitanja moćnom modelu za zaključivanje ne znači automatski da će svaka korisna sposobnost zaključivanja dostupna modelu biti iskorišćena.",{},{"id":1117,"data":1118,"type":218,"tunes":1120},"kyuBsra2Ng",{"text":1119},"Rešenje nije da se maksimalno zaključivanje forsira u svakoj interakciji.",{},{"id":1122,"data":1123,"type":218,"tunes":1125},"JWk6uFnvRu",{"text":1124},"Rešenje je da se identifikuje koji kontroli zaključivanja su važni za zadatak i da se oni eksplicitno navedu.",{},{"id":1127,"data":1128,"type":236,"tunes":1130},"LMbGMcE3ij",{"text":1129,"caption":234,"alignment":235},"Dobra AI metodologija ne govori modelu do kog zaključka treba da dođe. Ona definiše šta zaključak mora da preživi pre nego što bude prihvaćen.",{},{"id":1132,"data":1133,"type":218,"tunes":1135},"1cjcDV1NCT",{"text":1134},"Taj princip je prenosiv.",{},{"id":1137,"data":1138,"type":218,"tunes":1140},"TnWIF9A5Le",{"text":1139},"U istorijskom istraživanju, zaključak mora da preživi hronologiju i kritiku izvora. U otklanjanju grešaka, mora da preživi reprodukciju i diskriminišuće testove. U arhitekturi, mora da preživi zahteve i operativna ograničenja. U strategiji, mora da preživi tržišne dokaze i kriterijume neuspeha.",{},{"id":1142,"data":1143,"type":218,"tunes":1145},"fTl3br5YpI",{"text":1144},"Validatori se menjaju.",{},{"id":1147,"data":1148,"type":218,"tunes":1150},"50R9xCfQF6",{"text":1149},"Metodološka disciplina ostaje.",{},{"id":1152,"data":1153,"type":236,"tunes":1155},"S9NuFbc7vE",{"text":1154,"caption":234,"alignment":235},"Cilj nije model koji uvek misli duže. To je sistem koji zna kada odgovor još nije opravdan.",{},{"id":1157,"data":1158,"type":1159,"tunes":1160},"tyPyGw9pPq",{},"delimiter",{},{"id":1162,"data":1163,"type":42,"tunes":1165},"w8KrYoGhp7",{"text":1164,"level":252},"Istraživački kontekst",{},{"id":1167,"data":1168,"type":218,"tunes":1170},"DbP2a862nC",{"text":1169},"Kompletan okvir opisan u ovoj seriji je metodološka sinteza, a ne uspostavljeni standardizovani LLM benchmark. Nekoliko srodnih linija istraživanja ipak podržava njegovu centralnu arhitektonsku premisu: kvalitet zaključivanja može se značajno promeniti kada je rešavanje problema pomoću jezičkog modela organizovano kao iterativni ili višefazni proces, a ne kao jedna generacija.",{},{"id":1172,"data":1173,"type":218,"tunes":1175},"URbMK6Bkg2",{"text":1174},"ReAct kombinuje zaključivanje sa akcijama i spoljnim zapažanjima i pokazao je prednosti u odgovaranju na pitanja, proveri činjenica i interaktivnim zadacima odlučivanja. Tree of Thoughts istražuje više kandidata za puteve zaključivanja uz evaluaciju i vraćanje unazad, umesto da se odmah opredeli za jedan lanac. Self-Refine je pokazao poboljšanja u raznovrsnim zadacima iteriranjem između generisanja, povratne informacije i rafiniranja. Reflexion je pokazao da jezički agenti mogu da koriste verbalne povratne informacije iz prethodnih pokušaja da poboljšaju kasnije odluke bez ažuriranja težina modela.",{},{"id":1177,"data":1178,"type":218,"tunes":1180},"zlf21zoZ1q",{"text":1179},"Ovi pristupi ne implementiraju Prompt Invariance niti ovde predloženi okvir usmeren na falsifikaciju. Oni pružaju nezavisne dokaze za širu ideju da je procedura u vreme zaključivanja važna: sposobnost modela i proces kroz koji se ta sposobnost koristi nisu identični.",{},{"id":1182,"data":1183,"type":218,"tunes":1185},"LeMaefilCi",{"text":1184},"Doprinos ove serije je organizovanje tog uvida oko epistemičkih kontrola: razdvajanje dokaza, praćenje pretpostavki, analiza uokvirivanja prompta, konkurentske hipoteze, falsifikacija, poreklo, validacija u domenu i kalibrisana neizvesnost.",{},{"id":1187,"data":1188,"type":42,"tunes":1190},"Py8YVojnNq",{"text":1189,"level":313},"Odabrane reference",{},{"id":1192,"data":1193,"type":375,"tunes":1202},"Cg1YGO8RNK",{"meta":1194,"items":1195,"style":374},{},[1196,1197,1198,1199,1200,1201],"Yao, S. et al. — \u003Ci>ReAct: Synergizing Reasoning and Acting in Language Models\u003C\u002Fi>. ICLR, 2023.","Yao, S. et al. — \u003Ci>Tree of Thoughts: Deliberate Problem Solving with Large Language Models\u003C\u002Fi>. NeurIPS, 2023.","Madaan, A. et al. — \u003Ci>Self-Refine: Iterative Refinement with Self-Feedback\u003C\u002Fi>. NeurIPS, 2023.","Shinn, N. et al. — \u003Ci>Reflexion: Language Agents with Verbal Reinforcement Learning\u003C\u002Fi>. NeurIPS, 2023.","Jhaveri, A. R., GX-Chen, A., Sucholutsky, I. & Choi, E. — \u003Ci>Failing to Falsify: Evaluating and Mitigating Confirmation Bias in Language Models\u003C\u002Fi>. 2026.","Brucks, M. S. & Toubia, O. — \u003Ci>Prompt Architecture Induces Methodological Artifacts in Large Language Models\u003C\u002Fi>. PLOS ONE, 2025.",{},{"id":1204,"data":1205,"type":1159,"tunes":1206},"_6koyQ-al5",{},{},{"id":1208,"data":1209,"type":42,"tunes":1211},"-CU6TfVSPg",{"text":1210,"level":252},"Serija o metodologiji AI zaključivanja",{},{"id":1213,"data":1214,"type":375,"tunes":1223},"LiQ_OM7vV7",{"meta":1215,"items":1216,"style":1036},{"counterType":1022},[1217,1218,1219,1220,1221,1222],"\u003Ca href=\"https:\u002F\u002Fstajic.de\u002Fsr\u002Fblog\u002Fbeyond-prompt-engineering-a-methodology-for-more-reliable-ai-reasoning\">\u003Cb>Beyond Prompt Engineering: A Methodology for More Reliable AI Reasoning\u003C\u002Fb>\u003C\u002Fa> — zašto sposobnost modela sama po sebi nije dovoljna.","\u003Ca href=\"https:\u002F\u002Fstajic.de\u002Fsr\u002Fblog\u002Fthe-prompt-is-part-of-the-bias-how-ai-framing-shapes-reasoning\">\u003Cb>The Prompt Is Part of the Bias\u003C\u002Fb>\u003C\u002Fa> — kako formulacija zadatka može da utiče na okruženje za zaključivanje.","\u003Ca href=\"https:\u002F\u002Fstajic.de\u002Fsr\u002Fblog\u002Fprompt-invariance-does-the-conclusion-survive-the-prompt\">\u003Cb>Prompt Invariance: Does the Conclusion Survive the Prompt?\u003C\u002Fb>\u003C\u002Fa> — testiranje stabilnosti zaključka kroz alternativna uokvirivanja.","\u003Ca href=\"https:\u002F\u002Fstajic.de\u002Fsr\u002Fblog\u002Ffalsification-for-ai-reasoning-from-answers-to-tested-hypotheses\">\u003Cb>Falsification for AI Reasoning: From Answers to Tested Hypotheses\u003C\u002Fb>\u003C\u002Fa> — konkurentske hipoteze, protivdokazi i diskriminišući testovi.","\u003Cb>From Research Protocol to a General AI Reasoning Framework\u003C\u002Fb> — integrisanje metodologije u višekratno upotrebljivo jezgro zaključivanja nezavisno od domena.","Praktična primena istog okvira za zaključivanje u gejming AI — uključujući pomoćnike u igrama, autonomne agente, znanje osetljivo na zakrpe i validaciju stanja igre uživo — istražena je u \u003Ca href=\"https:\u002F\u002Ffigure.rocks\u002Fde\u002Fblog\u002Fwhen-gaming-ai-sounds-right-but-isn-t-the-reasoning-problem-behind-game-assistants-and-agents\">When Gaming AI Sounds Right but Isn't: The Reasoning Problem Behind Game Assistants and Agents on figure.rocks.\u003C\u002Fa>",{},"2.31","Metodologija razvijena za rigorozno istraživanje uz pomoć veštačke inteligencije može se generalizovati daleko izvan samog istraživanja. Odvajanjem dokaza od pretpostavki, testiranjem konkurentnih hipoteza, kontrolisanjem uokvirivanja upita, traženjem protivdokaza i primenom validatora specifičnih za domen, ista arhitektura rasuđivanja može unaprediti otklanjanje grešaka, dizajn softvera, strategiju, tehničku analizu i podršku pri odlučivanju uz pomoć veštačke inteligencije.","\u002Fuploads\u002F2026\u002F09\u002Ffrom-research-protocol-to-a-general-ai-reasoning-framework-1789802635691-hhf78v.webp","from-research-protocol-to-a-general-ai-reasoning-framework-1789802635691-hhf78v","PUBLISHED","2026-09-19T01:16:00.000Z","2026-09-19T07:16:24.024Z","2026-09-19T07:46:41.108Z",{"en":1233,"de":1234,"sr":1235,"es":1236,"fr":1237,"it":1238,"ru":1239,"zh":1240},"\u002Fblog\u002Ffrom-research-protocol-to-a-general-ai-reasoning-framework","\u002Fde\u002Fblog\u002Ffrom-research-protocol-to-a-general-ai-reasoning-framework","\u002Fsr\u002Fblog\u002Ffrom-research-protocol-to-a-general-ai-reasoning-framework","\u002Fes\u002Fblog\u002Ffrom-research-protocol-to-a-general-ai-reasoning-framework","\u002Ffr\u002Fblog\u002Ffrom-research-protocol-to-a-general-ai-reasoning-framework","\u002Fit\u002Fblog\u002Ffrom-research-protocol-to-a-general-ai-reasoning-framework","\u002Fru\u002Fblog\u002Ffrom-research-protocol-to-a-general-ai-reasoning-framework","\u002Fzh\u002Fblog\u002Ffrom-research-protocol-to-a-general-ai-reasoning-framework",[],{"id":1243,"login":1244,"email":234,"displayName":1245},"20","rooth8233","Aleksandar Stajić",[1247,2079],{"lang":1248,"title":1249,"content":1250,"contentJson":1251,"excerpt":2078},"en","From Research Protocol to a General AI Reasoning Framework","{\"time\":1789803202269,\"blocks\":[{\"id\":\"ESemLsLo8j\",\"type\":\"paragraph\",\"data\":{\"text\":\"The methodology developed across this series began with a research problem: how can an AI model help investigate a complex question without simply reinforcing the assumptions already present in the user's prompt?\"},\"tunes\":{}},{\"id\":\"4Y0K3YWc6x\",\"type\":\"paragraph\",\"data\":{\"text\":\"That problem initially appears to belong to historical or academic research. In reality, it is much broader.\"},\"tunes\":{}},{\"id\":\"Tdh0-PzDMD\",\"type\":\"paragraph\",\"data\":{\"text\":\"Software debugging, architecture decisions, technical diagnosis, security analysis, product strategy and many forms of decision support share the same underlying structure. A problem is presented. Some evidence is available. One or more explanations appear plausible. Assumptions enter the analysis. The system must determine which conclusion is best supported.\"},\"tunes\":{}},{\"id\":\"y1qR6aL83j\",\"type\":\"quote\",\"data\":{\"text\":\"The domain changes. The epistemic problem often does not.\",\"caption\":\"\",\"alignment\":\"left\"},\"tunes\":{}},{\"id\":\"OSYX72lgMi\",\"type\":\"paragraph\",\"data\":{\"text\":\"This article therefore takes the next step: converting the research protocol developed in the previous articles into a general reasoning framework for AI-assisted analytical work.\"},\"tunes\":{}},{\"id\":\"0nk7KQPJLF\",\"type\":\"paragraph\",\"data\":{\"text\":\"The framework builds directly on four previous layers: \u003Ca href=\\\"https:\u002F\u002Fstajic.de\u002Fblog\u002Fbeyond-prompt-engineering-a-methodology-for-more-reliable-ai-reasoning\\\">Beyond Prompt Engineering: A Methodology for More Reliable AI Reasoning\u003C\u002Fa> defines the overall methodological problem; \u003Ca href=\\\"https:\u002F\u002Fstajic.de\u002Fblog\u002Fthe-prompt-is-part-of-the-bias-how-ai-framing-shapes-reasoning\\\">The Prompt Is Part of the Bias\u003C\u002Fa> examines framing and prompt dependency; \u003Ca href=\\\"https:\u002F\u002Fstajic.de\u002Fblog\u002Fprompt-invariance-does-the-conclusion-survive-the-prompt\\\">Prompt Invariance: Does the Conclusion Survive the Prompt?\u003C\u002Fa> introduces a robustness test against framing; and \u003Ca href=\\\"https:\u002F\u002Fstajic.de\u002Fblog\u002Ffalsification-for-ai-reasoning-from-answers-to-tested-hypotheses\\\">Falsification for AI Reasoning: From Answers to Tested Hypotheses\u003C\u002Fa> adds systematic disconfirmation and competing hypotheses.\"},\"tunes\":{}},{\"id\":\"8u01PX2pJ_\",\"type\":\"header\",\"data\":{\"text\":\"The Key Generalization\",\"level\":2},\"tunes\":{}},{\"id\":\"KX-3wYIvfo\",\"type\":\"paragraph\",\"data\":{\"text\":\"The generalization is not that every task should be treated like historical research.\"},\"tunes\":{}},{\"id\":\"Qlnajp4NIg\",\"type\":\"paragraph\",\"data\":{\"text\":\"That would be a category error.\"},\"tunes\":{}},{\"id\":\"K_gM2XnB_E\",\"type\":\"paragraph\",\"data\":{\"text\":\"Historical research depends on chronology, provenance, transmission, source criticism and archival completeness. Software debugging depends on logs, configuration, reproducibility, execution state and controlled tests. Architecture depends on requirements, constraints, interfaces, quality attributes and operational trade-offs.\"},\"tunes\":{}},{\"id\":\"EQnEh291Ja\",\"type\":\"paragraph\",\"data\":{\"text\":\"What can be generalized is the reasoning process surrounding those domain-specific forms of evidence.\"},\"tunes\":{}},{\"id\":\"041iVGE35A\",\"type\":\"quote\",\"data\":{\"text\":\"The framework is domain-independent at its core, but never domain-free.\",\"caption\":\"\",\"alignment\":\"left\"},\"tunes\":{}},{\"id\":\"GsCAv2bn5D\",\"type\":\"paragraph\",\"data\":{\"text\":\"This distinction is fundamental. A universal reasoning methodology cannot replace expertise. It can instead define how evidence, assumptions, hypotheses, contradictions and uncertainty should be handled before domain expertise produces the final judgment.\"},\"tunes\":{}},{\"id\":\"iGG8FYrJ2O\",\"type\":\"header\",\"data\":{\"text\":\"A Domain-Independent Reasoning Core\",\"level\":2},\"tunes\":{}},{\"id\":\"GRuYGmzxLe\",\"type\":\"paragraph\",\"data\":{\"text\":\"Across analytical domains, the same high-level sequence can be applied:\"},\"tunes\":{}},{\"id\":\"lgjHVx7HZI\",\"type\":\"quote\",\"data\":{\"text\":\"Problem → decomposition → evidence → assumptions → competing hypotheses → predictions → counter-evidence → discriminating tests → domain validation → confidence recalibration → conclusion\",\"caption\":\"\",\"alignment\":\"left\"},\"tunes\":{}},{\"id\":\"AKTY6_aOdu\",\"type\":\"paragraph\",\"data\":{\"text\":\"The process deliberately prevents the model's first coherent explanation from becoming the final answer by default.\"},\"tunes\":{}},{\"id\":\"eipK0zk3WD\",\"type\":\"paragraph\",\"data\":{\"text\":\"Instead, the first explanation becomes a candidate.\"},\"tunes\":{}},{\"id\":\"yU78N3Sd2e\",\"type\":\"header\",\"data\":{\"text\":\"1. Define the Actual Problem\",\"level\":3},\"tunes\":{}},{\"id\":\"fDuyj6nSv-\",\"type\":\"paragraph\",\"data\":{\"text\":\"Before generating a solution, the system should determine what is actually being asked.\"},\"tunes\":{}},{\"id\":\"sctkg30ATQ\",\"type\":\"paragraph\",\"data\":{\"text\":\"User requests frequently contain both a problem and a proposed diagnosis:\"},\"tunes\":{}},{\"id\":\"HvF-G0rwXA\",\"type\":\"quote\",\"data\":{\"text\":\"Why is nginx causing my API connection failures?\",\"caption\":\"\",\"alignment\":\"left\"},\"tunes\":{}},{\"id\":\"mFKfHfGIDV\",\"type\":\"paragraph\",\"data\":{\"text\":\"The actual problem may instead be:\"},\"tunes\":{}},{\"id\":\"CbLWis4-Bu\",\"type\":\"quote\",\"data\":{\"text\":\"What is causing the API connection failures?\",\"caption\":\"\",\"alignment\":\"left\"},\"tunes\":{}},{\"id\":\"cMcvbsDaMC\",\"type\":\"paragraph\",\"data\":{\"text\":\"The difference is small linguistically and enormous methodologically.\"},\"tunes\":{}},{\"id\":\"E_kQoBwBzK\",\"type\":\"paragraph\",\"data\":{\"text\":\"The first formulation embeds a causal hypothesis. The second exposes that hypothesis to competition.\"},\"tunes\":{}},{\"id\":\"M86BF9OUO9\",\"type\":\"paragraph\",\"data\":{\"text\":\"This is precisely the framing problem examined in \u003Ca href=\\\"https:\u002F\u002Fstajic.de\u002Fblog\u002Fthe-prompt-is-part-of-the-bias-how-ai-framing-shapes-reasoning\\\">The Prompt Is Part of the Bias\u003C\u002Fa>.\"},\"tunes\":{}},{\"id\":\"8jN_GEWnLD\",\"type\":\"header\",\"data\":{\"text\":\"2. Separate Facts, Assumptions and Unknowns\",\"level\":3},\"tunes\":{}},{\"id\":\"4-45ArOJIC\",\"type\":\"paragraph\",\"data\":{\"text\":\"A reliable reasoning process must prevent observations and interpretations from silently merging.\"},\"tunes\":{}},{\"id\":\"gn7a0JRFIn\",\"type\":\"list\",\"data\":{\"style\":\"unordered\",\"meta\":{},\"items\":[\"\u003Cb>Fact:\u003C\u002Fb> directly supported by available evidence.\",\"\u003Cb>Interpretation:\u003C\u002Fb> an explanation inferred from facts.\",\"\u003Cb>Assumption:\u003C\u002Fb> a proposition required by the current reasoning path but not independently established.\",\"\u003Cb>Unknown:\u003C\u002Fb> information necessary for stronger discrimination but currently unavailable.\"]},\"tunes\":{}},{\"id\":\"7gqnnnAyNH\",\"type\":\"paragraph\",\"data\":{\"text\":\"This classification may appear elementary, but it solves a common failure mode in AI-generated analysis: assumptions become embedded in fluent prose and later reappear as if they had already been established.\"},\"tunes\":{}},{\"id\":\"wakPN0U08S\",\"type\":\"quote\",\"data\":{\"text\":\"Traceability begins by preventing inference from disguising itself as evidence.\",\"caption\":\"\",\"alignment\":\"left\"},\"tunes\":{}},{\"id\":\"UPatPWjaiP\",\"type\":\"header\",\"data\":{\"text\":\"3. Generate Competing Explanations\",\"level\":3},\"tunes\":{}},{\"id\":\"zuRICKxeRX\",\"type\":\"paragraph\",\"data\":{\"text\":\"A candidate explanation should not be evaluated in isolation when credible alternatives exist.\"},\"tunes\":{}},{\"id\":\"AvfAw-zrKJ\",\"type\":\"paragraph\",\"data\":{\"text\":\"The system should therefore generate several plausible hypotheses before committing to one.\"},\"tunes\":{}},{\"id\":\"5JVkk_l5m9\",\"type\":\"paragraph\",\"data\":{\"text\":\"The purpose is not artificial brainstorming. Alternatives should represent genuinely different mechanisms capable of explaining the observations.\"},\"tunes\":{}},{\"id\":\"6ZI39jnt9v\",\"type\":\"quote\",\"data\":{\"text\":\"A preferred hypothesis has not won if no serious competitor was allowed to enter the test.\",\"caption\":\"\",\"alignment\":\"left\"},\"tunes\":{}},{\"id\":\"WGpU3HSfMr\",\"type\":\"paragraph\",\"data\":{\"text\":\"This concept is central to \u003Ca href=\\\"https:\u002F\u002Fstajic.de\u002Fblog\u002Ffalsification-for-ai-reasoning-from-answers-to-tested-hypotheses\\\">Falsification for AI Reasoning\u003C\u002Fa>, where hypotheses are evaluated through expected observations, counter-evidence and discriminating tests rather than accumulated supportive prose.\"},\"tunes\":{}},{\"id\":\"mUW1lRBq9a\",\"type\":\"header\",\"data\":{\"text\":\"4. Preserve Evidence Provenance\",\"level\":3},\"tunes\":{}},{\"id\":\"m2TM2yNjau\",\"type\":\"paragraph\",\"data\":{\"text\":\"Evidence should remain traceable to its origin.\"},\"tunes\":{}},{\"id\":\"3w2fzBzVH4\",\"type\":\"quote\",\"data\":{\"text\":\"Conclusion → interpretation → observation → source\",\"caption\":\"\",\"alignment\":\"left\"},\"tunes\":{}},{\"id\":\"rkoICmxwZT\",\"type\":\"paragraph\",\"data\":{\"text\":\"A log line, an official technical specification, an experiment, a primary historical document, an expert interpretation and an AI-generated summary do not have identical evidential status.\"},\"tunes\":{}},{\"id\":\"8ut4cK6e27\",\"type\":\"paragraph\",\"data\":{\"text\":\"The framework therefore does not merely store information. It should preserve enough metadata to know where a claim came from and how far the final conclusion is from the original observation.\"},\"tunes\":{}},{\"id\":\"hZ6BZYfcIo\",\"type\":\"header\",\"data\":{\"text\":\"5. Derive Predictions Before Explaining Results\",\"level\":3},\"tunes\":{}},{\"id\":\"aisk7gG2yj\",\"type\":\"paragraph\",\"data\":{\"text\":\"Once hypotheses exist, each should generate expectations.\"},\"tunes\":{}},{\"id\":\"IBsZFlw9pf\",\"type\":\"paragraph\",\"data\":{\"text\":\"If the hypothesis is correct, what should we observe?\"},\"tunes\":{}},{\"id\":\"Kykmkn9Dtx\",\"type\":\"paragraph\",\"data\":{\"text\":\"If an alternative is correct, what should be different?\"},\"tunes\":{}},{\"id\":\"iS8nPIkYVU\",\"type\":\"paragraph\",\"data\":{\"text\":\"Deriving expectations before interpreting all available evidence helps prevent every observation from being retrofitted into the preferred narrative.\"},\"tunes\":{}},{\"id\":\"1rrToFbYF5\",\"type\":\"header\",\"data\":{\"text\":\"6. Search for Counter-Evidence\",\"level\":3},\"tunes\":{}},{\"id\":\"NSjS51xlU6\",\"type\":\"paragraph\",\"data\":{\"text\":\"A generative model is naturally effective at constructing coherent support for a plausible idea. This makes disconfirmation particularly important.\"},\"tunes\":{}},{\"id\":\"jPSoTMS13f\",\"type\":\"paragraph\",\"data\":{\"text\":\"The system should explicitly ask:\"},\"tunes\":{}},{\"id\":\"DfULqhWZpB\",\"type\":\"list\",\"data\":{\"style\":\"unordered\",\"meta\":{},\"items\":[\"What observation would substantially weaken this explanation?\",\"What evidence does the hypothesis explain poorly?\",\"Which alternative explains the same observations with fewer assumptions?\",\"Which expected observations are missing?\",\"Could the apparent support be explained by another mechanism?\"]},\"tunes\":{}},{\"id\":\"rM5aFAv4Ch\",\"type\":\"paragraph\",\"data\":{\"text\":\"The principle is developed in detail in \u003Ca href=\\\"https:\u002F\u002Fstajic.de\u002Fblog\u002Ffalsification-for-ai-reasoning-from-answers-to-tested-hypotheses\\\">Falsification for AI Reasoning: From Answers to Tested Hypotheses\u003C\u002Fa>.\"},\"tunes\":{}},{\"id\":\"sWwUSTmVGW\",\"type\":\"header\",\"data\":{\"text\":\"7. Test Dependence on the Prompt\",\"level\":3},\"tunes\":{}},{\"id\":\"wvQwo5fB4W\",\"type\":\"paragraph\",\"data\":{\"text\":\"Evidence testing alone does not reveal whether the model's analysis remains excessively dependent on the user's formulation.\"},\"tunes\":{}},{\"id\":\"Z1AJX_Zx0J\",\"type\":\"paragraph\",\"data\":{\"text\":\"For sufficiently important problems, the conclusion can therefore be retested under alternative legitimate framings.\"},\"tunes\":{}},{\"id\":\"R9CxBqiZy5\",\"type\":\"list\",\"data\":{\"style\":\"unordered\",\"meta\":{},\"items\":[\"\u003Cb>Original:\u003C\u002Fb> preserve the user's formulation.\",\"\u003Cb>Blind:\u003C\u002Fb> remove the preferred conclusion.\",\"\u003Cb>Inverted:\u003C\u002Fb> foreground the strongest alternative.\",\"\u003Cb>Adversarial:\u003C\u002Fb> deliberately search for the strongest evidence-based challenge.\"]},\"tunes\":{}},{\"id\":\"Z6ReUs6ThD\",\"type\":\"paragraph\",\"data\":{\"text\":\"This is the Prompt Invariance method introduced in \u003Ca href=\\\"https:\u002F\u002Fstajic.de\u002Fblog\u002Fprompt-invariance-does-the-conclusion-survive-the-prompt\\\">Prompt Invariance: Does the Conclusion Survive the Prompt?\u003C\u002Fa>.\"},\"tunes\":{}},{\"id\":\"i72azJfoHd\",\"type\":\"paragraph\",\"data\":{\"text\":\"Its function is not to prove that a stable answer is true. Its function is to expose conclusions that change primarily because the framing changed.\"},\"tunes\":{}},{\"id\":\"PQJr3I5KR6\",\"type\":\"header\",\"data\":{\"text\":\"8. Apply Domain-Specific Validators\",\"level\":3},\"tunes\":{}},{\"id\":\"30EnJRKLgj\",\"type\":\"paragraph\",\"data\":{\"text\":\"This is where the general framework deliberately stops being universal.\"},\"tunes\":{}},{\"id\":\"HfkiK88IcE\",\"type\":\"paragraph\",\"data\":{\"text\":\"Every domain requires its own rules for determining whether an explanation is actually credible.\"},\"tunes\":{}},{\"id\":\"zinQCXwjug\",\"type\":\"table\",\"data\":{\"withHeadings\":true,\"stretched\":false,\"content\":[[\"Domain\",\"Typical Validators\"],[\"Historical research\",\"Chronology, provenance, geographical plausibility, transmission channels, primary sources, historiography\"],[\"Software debugging\",\"Logs, reproduction, runtime state, configuration, controlled changes, error correlation\"],[\"Software architecture\",\"Requirements, constraints, scalability, maintainability, security, operability, cost\"],[\"Security analysis\",\"Telemetry, attack path, permissions, observable indicators, reproducibility, threat model\"],[\"Product strategy\",\"Customer evidence, pricing behaviour, conversion, alternatives, market constraints, failure criteria\"],[\"Project management\",\"Scope, dependencies, resources, risks, acceptance criteria, milestones, stakeholder constraints\"],[\"Scientific analysis\",\"Experimental design, measurement quality, controls, reproducibility, statistical evidence, alternative explanations\"]]},\"tunes\":{}},{\"id\":\"uc0RyUCFOo\",\"type\":\"paragraph\",\"data\":{\"text\":\"The framework controls how claims are processed. Domain validators determine whether those claims survive contact with reality.\"},\"tunes\":{}},{\"id\":\"lAvhdCVN32\",\"type\":\"header\",\"data\":{\"text\":\"9. Recalibrate Confidence\",\"level\":3},\"tunes\":{}},{\"id\":\"h5PWNd0iA4\",\"type\":\"paragraph\",\"data\":{\"text\":\"The final conclusion should not preserve the confidence level of the initial answer automatically.\"},\"tunes\":{}},{\"id\":\"PcokJnli0n\",\"type\":\"paragraph\",\"data\":{\"text\":\"Confidence should be recalculated after alternatives, counter-evidence, prompt variation and domain validation have been considered.\"},\"tunes\":{}},{\"id\":\"4-JaOGRNdJ\",\"type\":\"paragraph\",\"data\":{\"text\":\"A useful output can therefore remain uncertain.\"},\"tunes\":{}},{\"id\":\"I6F7TJy23-\",\"type\":\"list\",\"data\":{\"style\":\"unordered\",\"meta\":{},\"items\":[\"strongly supported;\",\"provisionally supported;\",\"weakly supported;\",\"underdetermined;\",\"substantially contradicted;\",\"not testable with current evidence.\"]},\"tunes\":{}},{\"id\":\"ozNw_amgCI\",\"type\":\"quote\",\"data\":{\"text\":\"Uncertainty is not a failure of reasoning when the evidence itself is uncertain.\",\"caption\":\"\",\"alignment\":\"left\"},\"tunes\":{}},{\"id\":\"LlyeIOmVPC\",\"type\":\"header\",\"data\":{\"text\":\"The Same Framework Across Different Domains\",\"level\":2},\"tunes\":{}},{\"id\":\"dmsjTGPTp7\",\"type\":\"paragraph\",\"data\":{\"text\":\"The easiest way to understand the general framework is to observe how the same reasoning architecture behaves when the domain changes.\"},\"tunes\":{}},{\"id\":\"cU0_n6SCY3\",\"type\":\"header\",\"data\":{\"text\":\"Historical Research\",\"level\":3},\"tunes\":{}},{\"id\":\"es1dtKJQlZ\",\"type\":\"paragraph\",\"data\":{\"text\":\"Problem: determine whether similarities between two intellectual traditions indicate historical transmission.\"},\"tunes\":{}},{\"id\":\"Nzl1cThLXK\",\"type\":\"list\",\"data\":{\"style\":\"unordered\",\"meta\":{},\"items\":[\"Separate documented similarities from interpretive parallels.\",\"Compare direct transmission, indirect inheritance, convergence and retrospective interpretation.\",\"Test chronology and geographical contact.\",\"Search for documentary or terminological traces.\",\"Identify evidence expected under direct transmission.\",\"Search for earlier independent examples that weaken that hypothesis.\",\"Reframe the question without the preferred genealogy.\",\"Conclude only at the confidence level supported by the surviving evidence.\"]},\"tunes\":{}},{\"id\":\"RDf52TTR1X\",\"type\":\"header\",\"data\":{\"text\":\"Software Debugging\",\"level\":3},\"tunes\":{}},{\"id\":\"V_aQmUWc8S\",\"type\":\"paragraph\",\"data\":{\"text\":\"Problem: determine why an API connection fails intermittently.\"},\"tunes\":{}},{\"id\":\"YOUlOo_WTe\",\"type\":\"list\",\"data\":{\"style\":\"unordered\",\"meta\":{},\"items\":[\"Separate observed failures from the developer's suspected cause.\",\"Generate proxy, application, database, network and client hypotheses.\",\"Determine what logs and runtime behaviour each hypothesis predicts.\",\"Perform tests capable of distinguishing among them.\",\"Attempt to reproduce the failure while eliminating layers.\",\"Reject explanations contradicted by controlled observations.\",\"Identify the first confirmed failure mechanism rather than the most persuasive narrative.\"]},\"tunes\":{}},{\"id\":\"tG7q0LkXG8\",\"type\":\"header\",\"data\":{\"text\":\"Software Architecture\",\"level\":3},\"tunes\":{}},{\"id\":\"Siq7TARR4r\",\"type\":\"paragraph\",\"data\":{\"text\":\"Problem: choose an architecture for a new platform.\"},\"tunes\":{}},{\"id\":\"PYBaGFi2Ol\",\"type\":\"list\",\"data\":{\"style\":\"unordered\",\"meta\":{},\"items\":[\"Separate requirements from implementation preferences.\",\"Compare modular monolith, services and hybrid alternatives.\",\"Test each option against scalability, team structure, deployment, operational complexity and cost.\",\"Identify which requirement genuinely requires architectural complexity.\",\"Search for simpler designs capable of satisfying the same constraints.\",\"Treat technology preference as an assumption rather than a requirement.\"]},\"tunes\":{}},{\"id\":\"-kikosSmX1\",\"type\":\"header\",\"data\":{\"text\":\"Product Strategy\",\"level\":3},\"tunes\":{}},{\"id\":\"RW8gaj5yQo\",\"type\":\"paragraph\",\"data\":{\"text\":\"Problem: determine whether a product capability should be commercialized.\"},\"tunes\":{}},{\"id\":\"JZBAxRuw9D\",\"type\":\"list\",\"data\":{\"style\":\"unordered\",\"meta\":{},\"items\":[\"Separate technical possibility from demonstrated demand.\",\"Define alternative customer problems and alternative solutions.\",\"Identify observable market behaviour predicted by the business thesis.\",\"Define failure criteria before interpreting results.\",\"Search for evidence that customers solve the problem differently.\",\"Distinguish interest, willingness to test and willingness to pay.\"]},\"tunes\":{}},{\"id\":\"NfuFu8q13_\",\"type\":\"header\",\"data\":{\"text\":\"Project and Delivery Analysis\",\"level\":3},\"tunes\":{}},{\"id\":\"GaM4feM9nZ\",\"type\":\"paragraph\",\"data\":{\"text\":\"Problem: determine why a project is missing planned outcomes.\"},\"tunes\":{}},{\"id\":\"-G7BLfDmdU\",\"type\":\"list\",\"data\":{\"style\":\"unordered\",\"meta\":{},\"items\":[\"Separate symptoms such as delay from their assumed causes.\",\"Compare scope, dependencies, capacity, requirements, governance and technical risk.\",\"Trace evidence through plans, decisions, changes and acceptance criteria.\",\"Test whether corrective action addresses the actual mechanism rather than the visible symptom.\",\"Reassess the project hypothesis as new delivery evidence appears.\"]},\"tunes\":{}},{\"id\":\"5iAhrZD3wL\",\"type\":\"header\",\"data\":{\"text\":\"Why This Is More Than Prompt Engineering\",\"level\":2},\"tunes\":{}},{\"id\":\"dm4MhgnM3L\",\"type\":\"paragraph\",\"data\":{\"text\":\"Prompt engineering changes the instruction presented to a model.\"},\"tunes\":{}},{\"id\":\"87q5CSIEmb\",\"type\":\"paragraph\",\"data\":{\"text\":\"A reasoning framework changes the process through which an answer is allowed to become a conclusion.\"},\"tunes\":{}},{\"id\":\"7C_ZI5IHle\",\"type\":\"quote\",\"data\":{\"text\":\"Prompt engineering optimizes an invocation. A reasoning framework governs an inference process.\",\"caption\":\"\",\"alignment\":\"left\"},\"tunes\":{}},{\"id\":\"SFq4pwo4Gx\",\"type\":\"paragraph\",\"data\":{\"text\":\"This difference becomes important as AI systems move from chat interfaces toward agents, RAG pipelines, automated research, development tools and decision-support systems.\"},\"tunes\":{}},{\"id\":\"WItU2n6hS2\",\"type\":\"paragraph\",\"data\":{\"text\":\"A single carefully engineered prompt may improve one model call. A reasoning framework can define how multiple calls, retrieved evidence, tool results and validation stages interact before the system commits to an answer.\"},\"tunes\":{}},{\"id\":\"99vYubKOKi\",\"type\":\"header\",\"data\":{\"text\":\"The Framework Is Related to — but Different from — Existing LLM Reasoning Methods\",\"level\":2},\"tunes\":{}},{\"id\":\"y8Zf5V4OPJ\",\"type\":\"paragraph\",\"data\":{\"text\":\"The broader research landscape already contains several important approaches showing that LLM performance can improve when inference is structured as a process rather than a single generation.\"},\"tunes\":{}},{\"id\":\"gEIVPfVGEd\",\"type\":\"paragraph\",\"data\":{\"text\":\"\u003Cb>ReAct\u003C\u002Fb> combines reasoning with actions and observations from an external environment. Instead of relying exclusively on internal model knowledge, the model can act, observe new information and update its next step.\"},\"tunes\":{}},{\"id\":\"kKS8VaCYmR\",\"type\":\"paragraph\",\"data\":{\"text\":\"\u003Cb>Self-Refine\u003C\u002Fb> uses iterative generation, feedback and refinement, demonstrating that an initial LLM output can often be improved through explicit self-feedback without additional model training.\"},\"tunes\":{}},{\"id\":\"Puh4hZgRfK\",\"type\":\"paragraph\",\"data\":{\"text\":\"\u003Cb>Reflexion\u003C\u002Fb> uses verbal feedback and episodic memory to allow language agents to learn from previous attempts and modify later behaviour.\"},\"tunes\":{}},{\"id\":\"nAB7uNC8AX\",\"type\":\"paragraph\",\"data\":{\"text\":\"\u003Cb>Tree of Thoughts\u003C\u002Fb> explores multiple reasoning paths rather than committing immediately to one left-to-right chain, allowing search, evaluation and backtracking over candidate approaches.\"},\"tunes\":{}},{\"id\":\"B5Q7GKEqmh\",\"type\":\"paragraph\",\"data\":{\"text\":\"These methods differ substantially in purpose and implementation, but they establish an important general point: inference-time structure can materially change model performance.\"},\"tunes\":{}},{\"id\":\"Hj82H7bI5T\",\"type\":\"paragraph\",\"data\":{\"text\":\"The framework proposed in this series addresses a different primary objective.\"},\"tunes\":{}},{\"id\":\"q8bQMqN2Ir\",\"type\":\"quote\",\"data\":{\"text\":\"The goal is not simply to make the model search longer, reflect more or generate a better answer. The goal is to make the path from evidence to conclusion more resistant to framing, confirmation and unsupported assumptions.\",\"caption\":\"\",\"alignment\":\"left\"},\"tunes\":{}},{\"id\":\"d1bIpPAnpB\",\"type\":\"paragraph\",\"data\":{\"text\":\"Prompt Invariance, falsification-oriented testing and domain-specific validation therefore serve as epistemic controls rather than generic inference-expansion techniques.\"},\"tunes\":{}},{\"id\":\"eXXYr_pAms\",\"type\":\"header\",\"data\":{\"text\":\"A Layered View of AI Reasoning Quality\",\"level\":2},\"tunes\":{}},{\"id\":\"DqLa7MmX58\",\"type\":\"paragraph\",\"data\":{\"text\":\"The resulting system can be understood as several layers.\"},\"tunes\":{}},{\"id\":\"0NzL-zFxBY\",\"type\":\"table\",\"data\":{\"withHeadings\":true,\"stretched\":false,\"content\":[[\"Layer\",\"Question\"],[\"Task understanding\",\"What problem are we actually solving?\"],[\"Evidence\",\"What do we actually know?\"],[\"Assumptions\",\"What are we currently taking for granted?\"],[\"Hypotheses\",\"What plausible mechanisms could explain the evidence?\"],[\"Falsification\",\"What evidence could weaken each explanation?\"],[\"Prompt Invariance\",\"Does the conclusion depend excessively on framing?\"],[\"Domain validation\",\"Does the explanation satisfy the rules of the relevant discipline?\"],[\"Confidence calibration\",\"How strongly should the surviving conclusion be trusted?\"]]},\"tunes\":{}},{\"id\":\"xm0-PFgN-i\",\"type\":\"paragraph\",\"data\":{\"text\":\"An answer can fail at any layer.\"},\"tunes\":{}},{\"id\":\"eCszY4dwX1\",\"type\":\"paragraph\",\"data\":{\"text\":\"It can answer the wrong problem correctly. It can reason correctly from false evidence. It can retrieve correct facts but choose the wrong causal explanation. It can produce a strong explanation that disappears when the prompt is reframed. Or it can survive all general reasoning checks and still violate a domain-specific constraint.\"},\"tunes\":{}},{\"id\":\"hSmlX6vujv\",\"type\":\"quote\",\"data\":{\"text\":\"Correctness is not one property. It is the result of several dependencies surviving simultaneously.\",\"caption\":\"\",\"alignment\":\"left\"},\"tunes\":{}},{\"id\":\"EydNhsxtzy\",\"type\":\"header\",\"data\":{\"text\":\"Reasoning Quality Versus Model Capability\",\"level\":2},\"tunes\":{}},{\"id\":\"s4Km9d-Rxy\",\"type\":\"paragraph\",\"data\":{\"text\":\"This leads back to one of the central ideas of the series.\"},\"tunes\":{}},{\"id\":\"39JzRxeSgF\",\"type\":\"paragraph\",\"data\":{\"text\":\"A stronger model does not automatically imply that every invocation will use its capabilities in the most rigorous possible way.\"},\"tunes\":{}},{\"id\":\"KEw35qMKGc\",\"type\":\"paragraph\",\"data\":{\"text\":\"A model may possess the capability to generate alternatives, inspect logs, search sources, challenge assumptions and revise a conclusion. Whether all of those operations actually occur depends on the task, prompting, available tools, context and surrounding system architecture.\"},\"tunes\":{}},{\"id\":\"qijWfW7iDp\",\"type\":\"paragraph\",\"data\":{\"text\":\"The framework therefore separates two questions:\"},\"tunes\":{}},{\"id\":\"9TvQxkmlHA\",\"type\":\"list\",\"data\":{\"style\":\"unordered\",\"meta\":{},\"items\":[\"\u003Cb>Capability:\u003C\u002Fb> what can the model do?\",\"\u003Cb>Methodological discipline:\u003C\u002Fb> which of those capabilities must be exercised before the system accepts a conclusion?\"]},\"tunes\":{}},{\"id\":\"rmrusg3t3h\",\"type\":\"paragraph\",\"data\":{\"text\":\"The distinction was the starting point of \u003Ca href=\\\"https:\u002F\u002Fstajic.de\u002Fblog\u002Fbeyond-prompt-engineering-a-methodology-for-more-reliable-ai-reasoning\\\">Beyond Prompt Engineering\u003C\u002Fa> and becomes even more important once the methodology is generalized beyond research.\"},\"tunes\":{}},{\"id\":\"W321yYXV97\",\"type\":\"header\",\"data\":{\"text\":\"Not Every Task Needs the Full Framework\",\"level\":2},\"tunes\":{}},{\"id\":\"QwEqB6r20x\",\"type\":\"paragraph\",\"data\":{\"text\":\"The framework should not become compulsory overhead for every AI interaction.\"},\"tunes\":{}},{\"id\":\"XxxtP1e8oN\",\"type\":\"paragraph\",\"data\":{\"text\":\"Many tasks have low epistemic complexity:\"},\"tunes\":{}},{\"id\":\"yJw3vKdi9z\",\"type\":\"list\",\"data\":{\"style\":\"unordered\",\"meta\":{},\"items\":[\"format this JSON;\",\"translate this sentence;\",\"convert these units;\",\"rewrite this message;\",\"extract these fields;\",\"summarize this supplied text.\"]},\"tunes\":{}},{\"id\":\"c_3UzzXOlF\",\"type\":\"paragraph\",\"data\":{\"text\":\"Running four prompt variants, three competing hypotheses and a falsification stage for such tasks would add cost without proportional value.\"},\"tunes\":{}},{\"id\":\"89oDA_RK5n\",\"type\":\"paragraph\",\"data\":{\"text\":\"The full method becomes more valuable when:\"},\"tunes\":{}},{\"id\":\"T9axudVitU\",\"type\":\"list\",\"data\":{\"style\":\"unordered\",\"meta\":{},\"items\":[\"the answer depends on interpretation rather than direct retrieval;\",\"the user already has a preferred explanation;\",\"multiple causal mechanisms are plausible;\",\"the evidence is incomplete or contradictory;\",\"the decision has significant technical, financial or research consequences;\",\"the AI system is expected to act autonomously on the conclusion.\"]},\"tunes\":{}},{\"id\":\"voWYwXjAQH\",\"type\":\"paragraph\",\"data\":{\"text\":\"Methodological depth should therefore scale with epistemic risk.\"},\"tunes\":{}},{\"id\":\"j_hLO6152C\",\"type\":\"header\",\"data\":{\"text\":\"From Static Prompt to Adaptive Reasoning Policy\",\"level\":2},\"tunes\":{}},{\"id\":\"qS-EwzQq6d\",\"type\":\"paragraph\",\"data\":{\"text\":\"Once generalized, the framework no longer needs to exist as one enormous prompt.\"},\"tunes\":{}},{\"id\":\"hU3rHmIWSs\",\"type\":\"paragraph\",\"data\":{\"text\":\"A practical system can decide dynamically which controls a task requires.\"},\"tunes\":{}},{\"id\":\"syItl8JEDM\",\"type\":\"quote\",\"data\":{\"text\":\"Simple task → direct answer\\nAnalytical task → structured evidence check\\nHigh-uncertainty task → competing hypotheses\\nFraming-sensitive task → Prompt Invariance\\nHigh-consequence hypothesis → falsification and domain validation\",\"caption\":\"\",\"alignment\":\"left\"},\"tunes\":{}},{\"id\":\"Kl_PJTQeZR\",\"type\":\"paragraph\",\"data\":{\"text\":\"This transforms the methodology from a fixed prompt template into an adaptive reasoning policy.\"},\"tunes\":{}},{\"id\":\"x8-TRyTmoY\",\"type\":\"paragraph\",\"data\":{\"text\":\"The system does not always reason maximally. It reasons as rigorously as the task requires.\"},\"tunes\":{}},{\"id\":\"HN_GUpAvOU\",\"type\":\"header\",\"data\":{\"text\":\"A Practical General Framework\",\"level\":2},\"tunes\":{}},{\"id\":\"uJr0pPsy7a\",\"type\":\"paragraph\",\"data\":{\"text\":\"The complete process can be summarized as follows:\"},\"tunes\":{}},{\"id\":\"hWyrOdZeoR\",\"type\":\"list\",\"data\":{\"style\":\"ordered\",\"meta\":{\"counterType\":\"numeric\"},\"items\":[\"\u003Cb>Normalize the problem.\u003C\u002Fb> Remove embedded conclusions from the task definition where appropriate.\",\"\u003Cb>Identify evidence.\u003C\u002Fb> Separate observations from interpretations.\",\"\u003Cb>Expose assumptions.\u003C\u002Fb> Record propositions that have not yet been established.\",\"\u003Cb>Generate serious alternatives.\u003C\u002Fb> Do not allow the first hypothesis to compete only against weak strawmen.\",\"\u003Cb>Preserve provenance.\u003C\u002Fb> Keep claims traceable to their sources.\",\"\u003Cb>Derive expectations.\u003C\u002Fb> Define what each hypothesis predicts.\",\"\u003Cb>Search for counter-evidence.\u003C\u002Fb> Actively look for observations the preferred hypothesis handles poorly.\",\"\u003Cb>Run discriminating tests.\u003C\u002Fb> Prefer evidence that differentiates among explanations.\",\"\u003Cb>Test framing dependence.\u003C\u002Fb> Apply Prompt Invariance when the user's framing may influence the result.\",\"\u003Cb>Apply domain validators.\u003C\u002Fb> Use the discipline-specific criteria relevant to the problem.\",\"\u003Cb>Recalibrate confidence.\u003C\u002Fb> Allow the conclusion to weaken, remain unresolved or change.\",\"\u003Cb>Preserve traceability.\u003C\u002Fb> Make the path from conclusion back to evidence inspectable.\"]},\"tunes\":{}},{\"id\":\"j37GdesnVv\",\"type\":\"header\",\"data\":{\"text\":\"What the Framework Does Not Claim\",\"level\":2},\"tunes\":{}},{\"id\":\"we5tXuRA71\",\"type\":\"list\",\"data\":{\"style\":\"unordered\",\"meta\":{},\"items\":[\"It does not guarantee truthful output.\",\"It does not eliminate hallucinations.\",\"It does not make an LLM a domain expert.\",\"It does not prove that stable conclusions are correct.\",\"It does not eliminate biases shared across all reasoning passes.\",\"It does not replace experiments, measurements or primary sources.\",\"It does not guarantee that all relevant hypotheses have been generated.\",\"It does not imply that more reasoning is always better.\",\"It does not claim that the complete framework has already been validated as a standardized academic methodology.\"]},\"tunes\":{}},{\"id\":\"71CdxmGzXp\",\"type\":\"paragraph\",\"data\":{\"text\":\"Its claim is narrower and more defensible: analytical AI systems can be made methodologically stronger when assumptions, framing, competing explanations, counter-evidence and domain validation are handled explicitly rather than left entirely to one unconstrained generation.\"},\"tunes\":{}},{\"id\":\"aGiLyuXQib\",\"type\":\"header\",\"data\":{\"text\":\"The Four Articles Become One Reasoning System\",\"level\":2},\"tunes\":{}},{\"id\":\"6k1TB7nciF\",\"type\":\"paragraph\",\"data\":{\"text\":\"The previous articles can now be understood not as separate prompting techniques but as components of one reasoning architecture.\"},\"tunes\":{}},{\"id\":\"1wFRu7dDCM\",\"type\":\"table\",\"data\":{\"withHeadings\":true,\"stretched\":false,\"content\":[[\"Article\",\"Function in the Framework\"],[\"\u003Ca href=\\\"https:\u002F\u002Fstajic.de\u002Fblog\u002Fbeyond-prompt-engineering-a-methodology-for-more-reliable-ai-reasoning\\\">Beyond Prompt Engineering\u003C\u002Fa>\",\"Defines the shift from answer generation to methodological reasoning.\"],[\"\u003Ca href=\\\"https:\u002F\u002Fstajic.de\u002Fblog\u002Fthe-prompt-is-part-of-the-bias-how-ai-framing-shapes-reasoning\\\">The Prompt Is Part of the Bias\u003C\u002Fa>\",\"Treats the user's formulation as a possible source of reasoning bias.\"],[\"\u003Ca href=\\\"https:\u002F\u002Fstajic.de\u002Fblog\u002Fprompt-invariance-does-the-conclusion-survive-the-prompt\\\">Prompt Invariance\u003C\u002Fa>\",\"Tests whether the conclusion survives meaningful changes in framing.\"],[\"\u003Ca href=\\\"https:\u002F\u002Fstajic.de\u002Fblog\u002Ffalsification-for-ai-reasoning-from-answers-to-tested-hypotheses\\\">Falsification for AI Reasoning\u003C\u002Fa>\",\"Tests whether hypotheses survive disconfirming evidence and serious alternatives.\"],[\"\u003Cb>From Research Protocol to a General AI Reasoning Framework\u003C\u002Fb>\",\"Combines the controls into a domain-independent core with domain-specific validation.\"]]},\"tunes\":{}},{\"id\":\"zTMfJ0udpF\",\"type\":\"paragraph\",\"data\":{\"text\":\"Together they describe a transition:\"},\"tunes\":{}},{\"id\":\"zGNT1eU0mo\",\"type\":\"quote\",\"data\":{\"text\":\"Prompt → answer\\nbecomes\\nProblem → evidence → hypotheses → challenge → validation → calibrated conclusion\",\"caption\":\"\",\"alignment\":\"left\"},\"tunes\":{}},{\"id\":\"SAvL1hIiBl\",\"type\":\"header\",\"data\":{\"text\":\"The Central Principle\",\"level\":2},\"tunes\":{}},{\"id\":\"NGbjz4ORtZ\",\"type\":\"paragraph\",\"data\":{\"text\":\"The methodology began with a simple observation: asking a powerful reasoning model a question does not automatically mean that every useful reasoning capability available to the model will be used.\"},\"tunes\":{}},{\"id\":\"kyuBsra2Ng\",\"type\":\"paragraph\",\"data\":{\"text\":\"The solution is not to force maximal reasoning into every interaction.\"},\"tunes\":{}},{\"id\":\"JWk6uFnvRu\",\"type\":\"paragraph\",\"data\":{\"text\":\"The solution is to identify which reasoning controls matter for the task and make them explicit.\"},\"tunes\":{}},{\"id\":\"LMbGMcE3ij\",\"type\":\"quote\",\"data\":{\"text\":\"A good AI methodology does not tell the model what conclusion to reach. It defines what a conclusion must survive before it is accepted.\",\"caption\":\"\",\"alignment\":\"left\"},\"tunes\":{}},{\"id\":\"1cjcDV1NCT\",\"type\":\"paragraph\",\"data\":{\"text\":\"That principle is transferable.\"},\"tunes\":{}},{\"id\":\"TnWIF9A5Le\",\"type\":\"paragraph\",\"data\":{\"text\":\"In historical research, the conclusion must survive chronology and source criticism. In debugging, it must survive reproduction and discriminating tests. In architecture, it must survive requirements and operational constraints. In strategy, it must survive market evidence and failure criteria.\"},\"tunes\":{}},{\"id\":\"fTl3br5YpI\",\"type\":\"paragraph\",\"data\":{\"text\":\"The validators change.\"},\"tunes\":{}},{\"id\":\"50R9xCfQF6\",\"type\":\"paragraph\",\"data\":{\"text\":\"The methodological discipline remains.\"},\"tunes\":{}},{\"id\":\"S9NuFbc7vE\",\"type\":\"quote\",\"data\":{\"text\":\"The objective is not a model that always thinks longer. It is a system that knows when an answer is not yet justified.\",\"caption\":\"\",\"alignment\":\"left\"},\"tunes\":{}},{\"id\":\"tyPyGw9pPq\",\"type\":\"delimiter\",\"data\":{},\"tunes\":{}},{\"id\":\"w8KrYoGhp7\",\"type\":\"header\",\"data\":{\"text\":\"Research Context\",\"level\":2},\"tunes\":{}},{\"id\":\"DbP2a862nC\",\"type\":\"paragraph\",\"data\":{\"text\":\"The complete framework described in this series is a methodological synthesis rather than an established standardized LLM benchmark. Several adjacent lines of research nevertheless support its central architectural premise: inference quality can change substantially when language-model problem solving is organized as an iterative or multi-stage process rather than a single generation.\"},\"tunes\":{}},{\"id\":\"URbMK6Bkg2\",\"type\":\"paragraph\",\"data\":{\"text\":\"ReAct combines reasoning with actions and external observations and demonstrated benefits across question answering, fact verification and interactive decision tasks. Tree of Thoughts explores multiple candidate reasoning paths with evaluation and backtracking rather than committing immediately to one chain. Self-Refine showed improvements across diverse tasks by iterating between generation, feedback and refinement. Reflexion demonstrated that language agents can use verbal feedback from previous attempts to improve later decisions without updating model weights.\"},\"tunes\":{}},{\"id\":\"zlf21zoZ1q\",\"type\":\"paragraph\",\"data\":{\"text\":\"These approaches do not implement Prompt Invariance or the falsification-oriented framework proposed here. They provide independent evidence for the broader idea that inference-time procedure matters: model capability and the process through which that capability is exercised are not identical.\"},\"tunes\":{}},{\"id\":\"LeMaefilCi\",\"type\":\"paragraph\",\"data\":{\"text\":\"The contribution of this series is to organize that insight around epistemic controls: evidence separation, assumption tracking, prompt-framing analysis, competing hypotheses, falsification, provenance, domain validation and calibrated uncertainty.\"},\"tunes\":{}},{\"id\":\"Py8YVojnNq\",\"type\":\"header\",\"data\":{\"text\":\"Selected References\",\"level\":3},\"tunes\":{}},{\"id\":\"Cg1YGO8RNK\",\"type\":\"list\",\"data\":{\"style\":\"unordered\",\"meta\":{},\"items\":[\"Yao, S. et al. — \u003Ci>ReAct: Synergizing Reasoning and Acting in Language Models\u003C\u002Fi>. ICLR, 2023.\",\"Yao, S. et al. — \u003Ci>Tree of Thoughts: Deliberate Problem Solving with Large Language Models\u003C\u002Fi>. NeurIPS, 2023.\",\"Madaan, A. et al. — \u003Ci>Self-Refine: Iterative Refinement with Self-Feedback\u003C\u002Fi>. NeurIPS, 2023.\",\"Shinn, N. et al. — \u003Ci>Reflexion: Language Agents with Verbal Reinforcement Learning\u003C\u002Fi>. NeurIPS, 2023.\",\"Jhaveri, A. R., GX-Chen, A., Sucholutsky, I. & Choi, E. — \u003Ci>Failing to Falsify: Evaluating and Mitigating Confirmation Bias in Language Models\u003C\u002Fi>. 2026.\",\"Brucks, M. S. & Toubia, O. — \u003Ci>Prompt Architecture Induces Methodological Artifacts in Large Language Models\u003C\u002Fi>. PLOS ONE, 2025.\"]},\"tunes\":{}},{\"id\":\"_6koyQ-al5\",\"type\":\"delimiter\",\"data\":{},\"tunes\":{}},{\"id\":\"-CU6TfVSPg\",\"type\":\"header\",\"data\":{\"text\":\"The AI Reasoning Methodology Series\",\"level\":2},\"tunes\":{}},{\"id\":\"LiQ_OM7vV7\",\"type\":\"list\",\"data\":{\"style\":\"ordered\",\"meta\":{\"counterType\":\"numeric\"},\"items\":[\"\u003Ca href=\\\"https:\u002F\u002Fstajic.de\u002Fblog\u002Fbeyond-prompt-engineering-a-methodology-for-more-reliable-ai-reasoning\\\">\u003Cb>Beyond Prompt Engineering: A Methodology for More Reliable AI Reasoning\u003C\u002Fb>\u003C\u002Fa> — why model capability alone is not enough.\",\"\u003Ca href=\\\"https:\u002F\u002Fstajic.de\u002Fblog\u002Fthe-prompt-is-part-of-the-bias-how-ai-framing-shapes-reasoning\\\">\u003Cb>The Prompt Is Part of the Bias\u003C\u002Fb>\u003C\u002Fa> — how task formulation can influence the reasoning environment.\",\"\u003Ca href=\\\"https:\u002F\u002Fstajic.de\u002Fblog\u002Fprompt-invariance-does-the-conclusion-survive-the-prompt\\\">\u003Cb>Prompt Invariance: Does the Conclusion Survive the Prompt?\u003C\u002Fb>\u003C\u002Fa> — testing conclusion stability across alternative framings.\",\"\u003Ca href=\\\"https:\u002F\u002Fstajic.de\u002Fblog\u002Ffalsification-for-ai-reasoning-from-answers-to-tested-hypotheses\\\">\u003Cb>Falsification for AI Reasoning: From Answers to Tested Hypotheses\u003C\u002Fb>\u003C\u002Fa> — competing hypotheses, counter-evidence and discriminating tests.\",\"\u003Cb>From Research Protocol to a General AI Reasoning Framework\u003C\u002Fb> — integrating the methodology into a reusable domain-independent reasoning core.\",\"A practical application of the same reasoning framework in gaming AI — including game assistants, autonomous agents, patch-sensitive knowledge and live game-state validation — is explored in \u003Ca href=\\\"https:\u002F\u002Ffigure.rocks\u002Fde\u002Fblog\u002Fwhen-gaming-ai-sounds-right-but-isn-t-the-reasoning-problem-behind-game-assistants-and-agents\\\">When Gaming AI Sounds Right but Isn't: The Reasoning Problem Behind Game Assistants and Agents on figure.rocks.\u003C\u002Fa>\"]},\"tunes\":{}}],\"version\":\"2.31.6\"}",{"time":1252,"blocks":1253,"version":2077},1789803202269,[1254,1258,1262,1266,1270,1274,1278,1282,1286,1290,1294,1298,1302,1306,1310,1314,1318,1322,1326,1330,1334,1338,1342,1346,1350,1354,1358,1362,1366,1370,1379,1383,1387,1391,1395,1399,1403,1407,1411,1415,1419,1423,1427,1431,1435,1439,1443,1447,1451,1455,1459,1463,1473,1477,1481,1485,1489,1498,1502,1506,1510,1514,1518,1546,1550,1554,1558,1562,1566,1577,1581,1585,1589,1593,1597,1610,1614,1618,1630,1634,1638,1649,1653,1657,1668,1672,1676,1686,1690,1694,1698,1702,1706,1710,1714,1718,1722,1726,1730,1734,1738,1742,1746,1750,1754,1758,1789,1793,1797,1801,1805,1809,1813,1817,1821,1828,1832,1836,1840,1844,1855,1859,1863,1874,1878,1882,1886,1890,1894,1898,1902,1906,1910,1927,1931,1945,1949,1953,1957,1979,1983,1987,1991,1995,1999,2003,2007,2011,2015,2019,2023,2027,2030,2034,2038,2042,2046,2050,2054,2059,2062,2066],{"id":215,"data":1255,"type":218,"tunes":1257},{"text":1256},"The methodology developed across this series began with a research problem: how can an AI model help investigate a complex question without simply reinforcing the assumptions already present in the user's prompt?",{},{"id":221,"data":1259,"type":218,"tunes":1261},{"text":1260},"That problem initially appears to belong to historical or academic research. In reality, it is much broader.",{},{"id":226,"data":1263,"type":218,"tunes":1265},{"text":1264},"Software debugging, architecture decisions, technical diagnosis, security analysis, product strategy and many forms of decision support share the same underlying structure. A problem is presented. Some evidence is available. One or more explanations appear plausible. Assumptions enter the analysis. The system must determine which conclusion is best supported.",{},{"id":231,"data":1267,"type":236,"tunes":1269},{"text":1268,"caption":234,"alignment":235},"The domain changes. The epistemic problem often does not.",{},{"id":239,"data":1271,"type":218,"tunes":1273},{"text":1272},"This article therefore takes the next step: converting the research protocol developed in the previous articles into a general reasoning framework for AI-assisted analytical work.",{},{"id":244,"data":1275,"type":218,"tunes":1277},{"text":1276},"The framework builds directly on four previous layers: \u003Ca href=\"https:\u002F\u002Fstajic.de\u002Fblog\u002Fbeyond-prompt-engineering-a-methodology-for-more-reliable-ai-reasoning\">Beyond Prompt Engineering: A Methodology for More Reliable AI Reasoning\u003C\u002Fa> defines the overall methodological problem; \u003Ca href=\"https:\u002F\u002Fstajic.de\u002Fblog\u002Fthe-prompt-is-part-of-the-bias-how-ai-framing-shapes-reasoning\">The Prompt Is Part of the Bias\u003C\u002Fa> examines framing and prompt dependency; \u003Ca href=\"https:\u002F\u002Fstajic.de\u002Fblog\u002Fprompt-invariance-does-the-conclusion-survive-the-prompt\">Prompt Invariance: Does the Conclusion Survive the Prompt?\u003C\u002Fa> introduces a robustness test against framing; and \u003Ca href=\"https:\u002F\u002Fstajic.de\u002Fblog\u002Ffalsification-for-ai-reasoning-from-answers-to-tested-hypotheses\">Falsification for AI Reasoning: From Answers to Tested Hypotheses\u003C\u002Fa> adds systematic disconfirmation and competing hypotheses.",{},{"id":249,"data":1279,"type":42,"tunes":1281},{"text":1280,"level":252},"The Key Generalization",{},{"id":255,"data":1283,"type":218,"tunes":1285},{"text":1284},"The generalization is not that every task should be treated like historical research.",{},{"id":260,"data":1287,"type":218,"tunes":1289},{"text":1288},"That would be a category error.",{},{"id":265,"data":1291,"type":218,"tunes":1293},{"text":1292},"Historical research depends on chronology, provenance, transmission, source criticism and archival completeness. Software debugging depends on logs, configuration, reproducibility, execution state and controlled tests. Architecture depends on requirements, constraints, interfaces, quality attributes and operational trade-offs.",{},{"id":270,"data":1295,"type":218,"tunes":1297},{"text":1296},"What can be generalized is the reasoning process surrounding those domain-specific forms of evidence.",{},{"id":275,"data":1299,"type":236,"tunes":1301},{"text":1300,"caption":234,"alignment":235},"The framework is domain-independent at its core, but never domain-free.",{},{"id":280,"data":1303,"type":218,"tunes":1305},{"text":1304},"This distinction is fundamental. A universal reasoning methodology cannot replace expertise. It can instead define how evidence, assumptions, hypotheses, contradictions and uncertainty should be handled before domain expertise produces the final judgment.",{},{"id":285,"data":1307,"type":42,"tunes":1309},{"text":1308,"level":252},"A Domain-Independent Reasoning Core",{},{"id":290,"data":1311,"type":218,"tunes":1313},{"text":1312},"Across analytical domains, the same high-level sequence can be applied:",{},{"id":295,"data":1315,"type":236,"tunes":1317},{"text":1316,"caption":234,"alignment":235},"Problem → decomposition → evidence → assumptions → competing hypotheses → predictions → counter-evidence → discriminating tests → domain validation → confidence recalibration → conclusion",{},{"id":300,"data":1319,"type":218,"tunes":1321},{"text":1320},"The process deliberately prevents the model's first coherent explanation from becoming the final answer by default.",{},{"id":305,"data":1323,"type":218,"tunes":1325},{"text":1324},"Instead, the first explanation becomes a candidate.",{},{"id":310,"data":1327,"type":42,"tunes":1329},{"text":1328,"level":313},"1. Define the Actual Problem",{},{"id":316,"data":1331,"type":218,"tunes":1333},{"text":1332},"Before generating a solution, the system should determine what is actually being asked.",{},{"id":321,"data":1335,"type":218,"tunes":1337},{"text":1336},"User requests frequently contain both a problem and a proposed diagnosis:",{},{"id":326,"data":1339,"type":236,"tunes":1341},{"text":1340,"caption":234,"alignment":235},"Why is nginx causing my API connection failures?",{},{"id":331,"data":1343,"type":218,"tunes":1345},{"text":1344},"The actual problem may instead be:",{},{"id":336,"data":1347,"type":236,"tunes":1349},{"text":1348,"caption":234,"alignment":235},"What is causing the API connection failures?",{},{"id":341,"data":1351,"type":218,"tunes":1353},{"text":1352},"The difference is small linguistically and enormous methodologically.",{},{"id":346,"data":1355,"type":218,"tunes":1357},{"text":1356},"The first formulation embeds a causal hypothesis. The second exposes that hypothesis to competition.",{},{"id":351,"data":1359,"type":218,"tunes":1361},{"text":1360},"This is precisely the framing problem examined in \u003Ca href=\"https:\u002F\u002Fstajic.de\u002Fblog\u002Fthe-prompt-is-part-of-the-bias-how-ai-framing-shapes-reasoning\">The Prompt Is Part of the Bias\u003C\u002Fa>.",{},{"id":356,"data":1363,"type":42,"tunes":1365},{"text":1364,"level":313},"2. Separate Facts, Assumptions and Unknowns",{},{"id":361,"data":1367,"type":218,"tunes":1369},{"text":1368},"A reliable reasoning process must prevent observations and interpretations from silently merging.",{},{"id":366,"data":1371,"type":375,"tunes":1378},{"meta":1372,"items":1373,"style":374},{},[1374,1375,1376,1377],"\u003Cb>Fact:\u003C\u002Fb> directly supported by available evidence.","\u003Cb>Interpretation:\u003C\u002Fb> an explanation inferred from facts.","\u003Cb>Assumption:\u003C\u002Fb> a proposition required by the current reasoning path but not independently established.","\u003Cb>Unknown:\u003C\u002Fb> information necessary for stronger discrimination but currently unavailable.",{},{"id":378,"data":1380,"type":218,"tunes":1382},{"text":1381},"This classification may appear elementary, but it solves a common failure mode in AI-generated analysis: assumptions become embedded in fluent prose and later reappear as if they had already been established.",{},{"id":383,"data":1384,"type":236,"tunes":1386},{"text":1385,"caption":234,"alignment":235},"Traceability begins by preventing inference from disguising itself as evidence.",{},{"id":388,"data":1388,"type":42,"tunes":1390},{"text":1389,"level":313},"3. Generate Competing Explanations",{},{"id":393,"data":1392,"type":218,"tunes":1394},{"text":1393},"A candidate explanation should not be evaluated in isolation when credible alternatives exist.",{},{"id":398,"data":1396,"type":218,"tunes":1398},{"text":1397},"The system should therefore generate several plausible hypotheses before committing to one.",{},{"id":403,"data":1400,"type":218,"tunes":1402},{"text":1401},"The purpose is not artificial brainstorming. Alternatives should represent genuinely different mechanisms capable of explaining the observations.",{},{"id":408,"data":1404,"type":236,"tunes":1406},{"text":1405,"caption":234,"alignment":235},"A preferred hypothesis has not won if no serious competitor was allowed to enter the test.",{},{"id":413,"data":1408,"type":218,"tunes":1410},{"text":1409},"This concept is central to \u003Ca href=\"https:\u002F\u002Fstajic.de\u002Fblog\u002Ffalsification-for-ai-reasoning-from-answers-to-tested-hypotheses\">Falsification for AI Reasoning\u003C\u002Fa>, where hypotheses are evaluated through expected observations, counter-evidence and discriminating tests rather than accumulated supportive prose.",{},{"id":418,"data":1412,"type":42,"tunes":1414},{"text":1413,"level":313},"4. Preserve Evidence Provenance",{},{"id":423,"data":1416,"type":218,"tunes":1418},{"text":1417},"Evidence should remain traceable to its origin.",{},{"id":428,"data":1420,"type":236,"tunes":1422},{"text":1421,"caption":234,"alignment":235},"Conclusion → interpretation → observation → source",{},{"id":433,"data":1424,"type":218,"tunes":1426},{"text":1425},"A log line, an official technical specification, an experiment, a primary historical document, an expert interpretation and an AI-generated summary do not have identical evidential status.",{},{"id":438,"data":1428,"type":218,"tunes":1430},{"text":1429},"The framework therefore does not merely store information. It should preserve enough metadata to know where a claim came from and how far the final conclusion is from the original observation.",{},{"id":443,"data":1432,"type":42,"tunes":1434},{"text":1433,"level":313},"5. Derive Predictions Before Explaining Results",{},{"id":448,"data":1436,"type":218,"tunes":1438},{"text":1437},"Once hypotheses exist, each should generate expectations.",{},{"id":453,"data":1440,"type":218,"tunes":1442},{"text":1441},"If the hypothesis is correct, what should we observe?",{},{"id":458,"data":1444,"type":218,"tunes":1446},{"text":1445},"If an alternative is correct, what should be different?",{},{"id":463,"data":1448,"type":218,"tunes":1450},{"text":1449},"Deriving expectations before interpreting all available evidence helps prevent every observation from being retrofitted into the preferred narrative.",{},{"id":468,"data":1452,"type":42,"tunes":1454},{"text":1453,"level":313},"6. Search for Counter-Evidence",{},{"id":473,"data":1456,"type":218,"tunes":1458},{"text":1457},"A generative model is naturally effective at constructing coherent support for a plausible idea. This makes disconfirmation particularly important.",{},{"id":478,"data":1460,"type":218,"tunes":1462},{"text":1461},"The system should explicitly ask:",{},{"id":483,"data":1464,"type":375,"tunes":1472},{"meta":1465,"items":1466,"style":374},{},[1467,1468,1469,1470,1471],"What observation would substantially weaken this explanation?","What evidence does the hypothesis explain poorly?","Which alternative explains the same observations with fewer assumptions?","Which expected observations are missing?","Could the apparent support be explained by another mechanism?",{},{"id":494,"data":1474,"type":218,"tunes":1476},{"text":1475},"The principle is developed in detail in \u003Ca href=\"https:\u002F\u002Fstajic.de\u002Fblog\u002Ffalsification-for-ai-reasoning-from-answers-to-tested-hypotheses\">Falsification for AI Reasoning: From Answers to Tested Hypotheses\u003C\u002Fa>.",{},{"id":499,"data":1478,"type":42,"tunes":1480},{"text":1479,"level":313},"7. Test Dependence on the Prompt",{},{"id":504,"data":1482,"type":218,"tunes":1484},{"text":1483},"Evidence testing alone does not reveal whether the model's analysis remains excessively dependent on the user's formulation.",{},{"id":509,"data":1486,"type":218,"tunes":1488},{"text":1487},"For sufficiently important problems, the conclusion can therefore be retested under alternative legitimate framings.",{},{"id":514,"data":1490,"type":375,"tunes":1497},{"meta":1491,"items":1492,"style":374},{},[1493,1494,1495,1496],"\u003Cb>Original:\u003C\u002Fb> preserve the user's formulation.","\u003Cb>Blind:\u003C\u002Fb> remove the preferred conclusion.","\u003Cb>Inverted:\u003C\u002Fb> foreground the strongest alternative.","\u003Cb>Adversarial:\u003C\u002Fb> deliberately search for the strongest evidence-based challenge.",{},{"id":524,"data":1499,"type":218,"tunes":1501},{"text":1500},"This is the Prompt Invariance method introduced in \u003Ca href=\"https:\u002F\u002Fstajic.de\u002Fblog\u002Fprompt-invariance-does-the-conclusion-survive-the-prompt\">Prompt Invariance: Does the Conclusion Survive the Prompt?\u003C\u002Fa>.",{},{"id":529,"data":1503,"type":218,"tunes":1505},{"text":1504},"Its function is not to prove that a stable answer is true. Its function is to expose conclusions that change primarily because the framing changed.",{},{"id":534,"data":1507,"type":42,"tunes":1509},{"text":1508,"level":313},"8. Apply Domain-Specific Validators",{},{"id":539,"data":1511,"type":218,"tunes":1513},{"text":1512},"This is where the general framework deliberately stops being universal.",{},{"id":544,"data":1515,"type":218,"tunes":1517},{"text":1516},"Every domain requires its own rules for determining whether an explanation is actually credible.",{},{"id":549,"data":1519,"type":576,"tunes":1545},{"content":1520,"stretched":43,"withHeadings":14},[1521,1524,1527,1530,1533,1536,1539,1542],[1522,1523],"Domain","Typical Validators",[1525,1526],"Historical research","Chronology, provenance, geographical plausibility, transmission channels, primary sources, historiography",[1528,1529],"Software debugging","Logs, reproduction, runtime state, configuration, controlled changes, error correlation",[1531,1532],"Software architecture","Requirements, constraints, scalability, maintainability, security, operability, cost",[1534,1535],"Security analysis","Telemetry, attack path, permissions, observable indicators, reproducibility, threat model",[1537,1538],"Product strategy","Customer evidence, pricing behaviour, conversion, alternatives, market constraints, failure criteria",[1540,1541],"Project management","Scope, dependencies, resources, risks, acceptance criteria, milestones, stakeholder constraints",[1543,1544],"Scientific analysis","Experimental design, measurement quality, controls, reproducibility, statistical evidence, alternative explanations",{},{"id":579,"data":1547,"type":218,"tunes":1549},{"text":1548},"The framework controls how claims are processed. Domain validators determine whether those claims survive contact with reality.",{},{"id":584,"data":1551,"type":42,"tunes":1553},{"text":1552,"level":313},"9. Recalibrate Confidence",{},{"id":589,"data":1555,"type":218,"tunes":1557},{"text":1556},"The final conclusion should not preserve the confidence level of the initial answer automatically.",{},{"id":594,"data":1559,"type":218,"tunes":1561},{"text":1560},"Confidence should be recalculated after alternatives, counter-evidence, prompt variation and domain validation have been considered.",{},{"id":599,"data":1563,"type":218,"tunes":1565},{"text":1564},"A useful output can therefore remain uncertain.",{},{"id":604,"data":1567,"type":375,"tunes":1576},{"meta":1568,"items":1569,"style":374},{},[1570,1571,1572,1573,1574,1575],"strongly supported;","provisionally supported;","weakly supported;","underdetermined;","substantially contradicted;","not testable with current evidence.",{},{"id":616,"data":1578,"type":236,"tunes":1580},{"text":1579,"caption":234,"alignment":235},"Uncertainty is not a failure of reasoning when the evidence itself is uncertain.",{},{"id":621,"data":1582,"type":42,"tunes":1584},{"text":1583,"level":252},"The Same Framework Across Different Domains",{},{"id":626,"data":1586,"type":218,"tunes":1588},{"text":1587},"The easiest way to understand the general framework is to observe how the same reasoning architecture behaves when the domain changes.",{},{"id":631,"data":1590,"type":42,"tunes":1592},{"text":1591,"level":313},"Historical Research",{},{"id":635,"data":1594,"type":218,"tunes":1596},{"text":1595},"Problem: determine whether similarities between two intellectual traditions indicate historical transmission.",{},{"id":640,"data":1598,"type":375,"tunes":1609},{"meta":1599,"items":1600,"style":374},{},[1601,1602,1603,1604,1605,1606,1607,1608],"Separate documented similarities from interpretive parallels.","Compare direct transmission, indirect inheritance, convergence and retrospective interpretation.","Test chronology and geographical contact.","Search for documentary or terminological traces.","Identify evidence expected under direct transmission.","Search for earlier independent examples that weaken that hypothesis.","Reframe the question without the preferred genealogy.","Conclude only at the confidence level supported by the surviving evidence.",{},{"id":654,"data":1611,"type":42,"tunes":1613},{"text":1612,"level":313},"Software Debugging",{},{"id":658,"data":1615,"type":218,"tunes":1617},{"text":1616},"Problem: determine why an API connection fails intermittently.",{},{"id":663,"data":1619,"type":375,"tunes":1629},{"meta":1620,"items":1621,"style":374},{},[1622,1623,1624,1625,1626,1627,1628],"Separate observed failures from the developer's suspected cause.","Generate proxy, application, database, network and client hypotheses.","Determine what logs and runtime behaviour each hypothesis predicts.","Perform tests capable of distinguishing among them.","Attempt to reproduce the failure while eliminating layers.","Reject explanations contradicted by controlled observations.","Identify the first confirmed failure mechanism rather than the most persuasive narrative.",{},{"id":676,"data":1631,"type":42,"tunes":1633},{"text":1632,"level":313},"Software Architecture",{},{"id":680,"data":1635,"type":218,"tunes":1637},{"text":1636},"Problem: choose an architecture for a new platform.",{},{"id":685,"data":1639,"type":375,"tunes":1648},{"meta":1640,"items":1641,"style":374},{},[1642,1643,1644,1645,1646,1647],"Separate requirements from implementation preferences.","Compare modular monolith, services and hybrid alternatives.","Test each option against scalability, team structure, deployment, operational complexity and cost.","Identify which requirement genuinely requires architectural complexity.","Search for simpler designs capable of satisfying the same constraints.","Treat technology preference as an assumption rather than a requirement.",{},{"id":697,"data":1650,"type":42,"tunes":1652},{"text":1651,"level":313},"Product Strategy",{},{"id":701,"data":1654,"type":218,"tunes":1656},{"text":1655},"Problem: determine whether a product capability should be commercialized.",{},{"id":706,"data":1658,"type":375,"tunes":1667},{"meta":1659,"items":1660,"style":374},{},[1661,1662,1663,1664,1665,1666],"Separate technical possibility from demonstrated demand.","Define alternative customer problems and alternative solutions.","Identify observable market behaviour predicted by the business thesis.","Define failure criteria before interpreting results.","Search for evidence that customers solve the problem differently.","Distinguish interest, willingness to test and willingness to pay.",{},{"id":718,"data":1669,"type":42,"tunes":1671},{"text":1670,"level":313},"Project and Delivery Analysis",{},{"id":723,"data":1673,"type":218,"tunes":1675},{"text":1674},"Problem: determine why a project is missing planned outcomes.",{},{"id":728,"data":1677,"type":375,"tunes":1685},{"meta":1678,"items":1679,"style":374},{},[1680,1681,1682,1683,1684],"Separate symptoms such as delay from their assumed causes.","Compare scope, dependencies, capacity, requirements, governance and technical risk.","Trace evidence through plans, decisions, changes and acceptance criteria.","Test whether corrective action addresses the actual mechanism rather than the visible symptom.","Reassess the project hypothesis as new delivery evidence appears.",{},{"id":739,"data":1687,"type":42,"tunes":1689},{"text":1688,"level":252},"Why This Is More Than Prompt Engineering",{},{"id":744,"data":1691,"type":218,"tunes":1693},{"text":1692},"Prompt engineering changes the instruction presented to a model.",{},{"id":749,"data":1695,"type":218,"tunes":1697},{"text":1696},"A reasoning framework changes the process through which an answer is allowed to become a conclusion.",{},{"id":754,"data":1699,"type":236,"tunes":1701},{"text":1700,"caption":234,"alignment":235},"Prompt engineering optimizes an invocation. A reasoning framework governs an inference process.",{},{"id":759,"data":1703,"type":218,"tunes":1705},{"text":1704},"This difference becomes important as AI systems move from chat interfaces toward agents, RAG pipelines, automated research, development tools and decision-support systems.",{},{"id":764,"data":1707,"type":218,"tunes":1709},{"text":1708},"A single carefully engineered prompt may improve one model call. A reasoning framework can define how multiple calls, retrieved evidence, tool results and validation stages interact before the system commits to an answer.",{},{"id":769,"data":1711,"type":42,"tunes":1713},{"text":1712,"level":252},"The Framework Is Related to — but Different from — Existing LLM Reasoning Methods",{},{"id":774,"data":1715,"type":218,"tunes":1717},{"text":1716},"The broader research landscape already contains several important approaches showing that LLM performance can improve when inference is structured as a process rather than a single generation.",{},{"id":779,"data":1719,"type":218,"tunes":1721},{"text":1720},"\u003Cb>ReAct\u003C\u002Fb> combines reasoning with actions and observations from an external environment. Instead of relying exclusively on internal model knowledge, the model can act, observe new information and update its next step.",{},{"id":784,"data":1723,"type":218,"tunes":1725},{"text":1724},"\u003Cb>Self-Refine\u003C\u002Fb> uses iterative generation, feedback and refinement, demonstrating that an initial LLM output can often be improved through explicit self-feedback without additional model training.",{},{"id":789,"data":1727,"type":218,"tunes":1729},{"text":1728},"\u003Cb>Reflexion\u003C\u002Fb> uses verbal feedback and episodic memory to allow language agents to learn from previous attempts and modify later behaviour.",{},{"id":794,"data":1731,"type":218,"tunes":1733},{"text":1732},"\u003Cb>Tree of Thoughts\u003C\u002Fb> explores multiple reasoning paths rather than committing immediately to one left-to-right chain, allowing search, evaluation and backtracking over candidate approaches.",{},{"id":799,"data":1735,"type":218,"tunes":1737},{"text":1736},"These methods differ substantially in purpose and implementation, but they establish an important general point: inference-time structure can materially change model performance.",{},{"id":804,"data":1739,"type":218,"tunes":1741},{"text":1740},"The framework proposed in this series addresses a different primary objective.",{},{"id":809,"data":1743,"type":236,"tunes":1745},{"text":1744,"caption":234,"alignment":235},"The goal is not simply to make the model search longer, reflect more or generate a better answer. The goal is to make the path from evidence to conclusion more resistant to framing, confirmation and unsupported assumptions.",{},{"id":814,"data":1747,"type":218,"tunes":1749},{"text":1748},"Prompt Invariance, falsification-oriented testing and domain-specific validation therefore serve as epistemic controls rather than generic inference-expansion techniques.",{},{"id":819,"data":1751,"type":42,"tunes":1753},{"text":1752,"level":252},"A Layered View of AI Reasoning Quality",{},{"id":824,"data":1755,"type":218,"tunes":1757},{"text":1756},"The resulting system can be understood as several layers.",{},{"id":829,"data":1759,"type":576,"tunes":1788},{"content":1760,"stretched":43,"withHeadings":14},[1761,1764,1767,1770,1773,1776,1779,1782,1785],[1762,1763],"Layer","Question",[1765,1766],"Task understanding","What problem are we actually solving?",[1768,1769],"Evidence","What do we actually know?",[1771,1772],"Assumptions","What are we currently taking for granted?",[1774,1775],"Hypotheses","What plausible mechanisms could explain the evidence?",[1777,1778],"Falsification","What evidence could weaken each explanation?",[1780,1781],"Prompt Invariance","Does the conclusion depend excessively on framing?",[1783,1784],"Domain validation","Does the explanation satisfy the rules of the relevant discipline?",[1786,1787],"Confidence calibration","How strongly should the surviving conclusion be trusted?",{},{"id":861,"data":1790,"type":218,"tunes":1792},{"text":1791},"An answer can fail at any layer.",{},{"id":866,"data":1794,"type":218,"tunes":1796},{"text":1795},"It can answer the wrong problem correctly. It can reason correctly from false evidence. It can retrieve correct facts but choose the wrong causal explanation. It can produce a strong explanation that disappears when the prompt is reframed. Or it can survive all general reasoning checks and still violate a domain-specific constraint.",{},{"id":871,"data":1798,"type":236,"tunes":1800},{"text":1799,"caption":234,"alignment":235},"Correctness is not one property. It is the result of several dependencies surviving simultaneously.",{},{"id":876,"data":1802,"type":42,"tunes":1804},{"text":1803,"level":252},"Reasoning Quality Versus Model Capability",{},{"id":881,"data":1806,"type":218,"tunes":1808},{"text":1807},"This leads back to one of the central ideas of the series.",{},{"id":887,"data":1810,"type":218,"tunes":1812},{"text":1811},"A stronger model does not automatically imply that every invocation will use its capabilities in the most rigorous possible way.",{},{"id":893,"data":1814,"type":218,"tunes":1816},{"text":1815},"A model may possess the capability to generate alternatives, inspect logs, search sources, challenge assumptions and revise a conclusion. Whether all of those operations actually occur depends on the task, prompting, available tools, context and surrounding system architecture.",{},{"id":899,"data":1818,"type":218,"tunes":1820},{"text":1819},"The framework therefore separates two questions:",{},{"id":905,"data":1822,"type":375,"tunes":1827},{"meta":1823,"items":1824,"style":374},{},[1825,1826],"\u003Cb>Capability:\u003C\u002Fb> what can the model do?","\u003Cb>Methodological discipline:\u003C\u002Fb> which of those capabilities must be exercised before the system accepts a conclusion?",{},{"id":913,"data":1829,"type":218,"tunes":1831},{"text":1830},"The distinction was the starting point of \u003Ca href=\"https:\u002F\u002Fstajic.de\u002Fblog\u002Fbeyond-prompt-engineering-a-methodology-for-more-reliable-ai-reasoning\">Beyond Prompt Engineering\u003C\u002Fa> and becomes even more important once the methodology is generalized beyond research.",{},{"id":919,"data":1833,"type":42,"tunes":1835},{"text":1834,"level":252},"Not Every Task Needs the Full Framework",{},{"id":924,"data":1837,"type":218,"tunes":1839},{"text":1838},"The framework should not become compulsory overhead for every AI interaction.",{},{"id":930,"data":1841,"type":218,"tunes":1843},{"text":1842},"Many tasks have low epistemic complexity:",{},{"id":936,"data":1845,"type":375,"tunes":1854},{"meta":1846,"items":1847,"style":374},{},[1848,1849,1850,1851,1852,1853],"format this JSON;","translate this sentence;","convert these units;","rewrite this message;","extract these fields;","summarize this supplied text.",{},{"id":948,"data":1856,"type":218,"tunes":1858},{"text":1857},"Running four prompt variants, three competing hypotheses and a falsification stage for such tasks would add cost without proportional value.",{},{"id":954,"data":1860,"type":218,"tunes":1862},{"text":1861},"The full method becomes more valuable when:",{},{"id":960,"data":1864,"type":375,"tunes":1873},{"meta":1865,"items":1866,"style":374},{},[1867,1868,1869,1870,1871,1872],"the answer depends on interpretation rather than direct retrieval;","the user already has a preferred explanation;","multiple causal mechanisms are plausible;","the evidence is incomplete or contradictory;","the decision has significant technical, financial or research consequences;","the AI system is expected to act autonomously on the conclusion.",{},{"id":972,"data":1875,"type":218,"tunes":1877},{"text":1876},"Methodological depth should therefore scale with epistemic risk.",{},{"id":978,"data":1879,"type":42,"tunes":1881},{"text":1880,"level":252},"From Static Prompt to Adaptive Reasoning Policy",{},{"id":983,"data":1883,"type":218,"tunes":1885},{"text":1884},"Once generalized, the framework no longer needs to exist as one enormous prompt.",{},{"id":989,"data":1887,"type":218,"tunes":1889},{"text":1888},"A practical system can decide dynamically which controls a task requires.",{},{"id":994,"data":1891,"type":236,"tunes":1893},{"text":1892,"caption":234,"alignment":235},"Simple task → direct answer\nAnalytical task → structured evidence check\nHigh-uncertainty task → competing hypotheses\nFraming-sensitive task → Prompt Invariance\nHigh-consequence hypothesis → falsification and domain validation",{},{"id":999,"data":1895,"type":218,"tunes":1897},{"text":1896},"This transforms the methodology from a fixed prompt template into an adaptive reasoning policy.",{},{"id":1004,"data":1899,"type":218,"tunes":1901},{"text":1900},"The system does not always reason maximally. It reasons as rigorously as the task requires.",{},{"id":1009,"data":1903,"type":42,"tunes":1905},{"text":1904,"level":252},"A Practical General Framework",{},{"id":1014,"data":1907,"type":218,"tunes":1909},{"text":1908},"The complete process can be summarized as follows:",{},{"id":1019,"data":1911,"type":375,"tunes":1926},{"meta":1912,"items":1913,"style":1036},{"counterType":1022},[1914,1915,1916,1917,1918,1919,1920,1921,1922,1923,1924,1925],"\u003Cb>Normalize the problem.\u003C\u002Fb> Remove embedded conclusions from the task definition where appropriate.","\u003Cb>Identify evidence.\u003C\u002Fb> Separate observations from interpretations.","\u003Cb>Expose assumptions.\u003C\u002Fb> Record propositions that have not yet been established.","\u003Cb>Generate serious alternatives.\u003C\u002Fb> Do not allow the first hypothesis to compete only against weak strawmen.","\u003Cb>Preserve provenance.\u003C\u002Fb> Keep claims traceable to their sources.","\u003Cb>Derive expectations.\u003C\u002Fb> Define what each hypothesis predicts.","\u003Cb>Search for counter-evidence.\u003C\u002Fb> Actively look for observations the preferred hypothesis handles poorly.","\u003Cb>Run discriminating tests.\u003C\u002Fb> Prefer evidence that differentiates among explanations.","\u003Cb>Test framing dependence.\u003C\u002Fb> Apply Prompt Invariance when the user's framing may influence the result.","\u003Cb>Apply domain validators.\u003C\u002Fb> Use the discipline-specific criteria relevant to the problem.","\u003Cb>Recalibrate confidence.\u003C\u002Fb> Allow the conclusion to weaken, remain unresolved or change.","\u003Cb>Preserve traceability.\u003C\u002Fb> Make the path from conclusion back to evidence inspectable.",{},{"id":1039,"data":1928,"type":42,"tunes":1930},{"text":1929,"level":252},"What the Framework Does Not Claim",{},{"id":1044,"data":1932,"type":375,"tunes":1944},{"meta":1933,"items":1934,"style":374},{},[1935,1936,1937,1938,1939,1940,1941,1942,1943],"It does not guarantee truthful output.","It does not eliminate hallucinations.","It does not make an LLM a domain expert.","It does not prove that stable conclusions are correct.","It does not eliminate biases shared across all reasoning passes.","It does not replace experiments, measurements or primary sources.","It does not guarantee that all relevant hypotheses have been generated.","It does not imply that more reasoning is always better.","It does not claim that the complete framework has already been validated as a standardized academic methodology.",{},{"id":1059,"data":1946,"type":218,"tunes":1948},{"text":1947},"Its claim is narrower and more defensible: analytical AI systems can be made methodologically stronger when assumptions, framing, competing explanations, counter-evidence and domain validation are handled explicitly rather than left entirely to one unconstrained generation.",{},{"id":1064,"data":1950,"type":42,"tunes":1952},{"text":1951,"level":252},"The Four Articles Become One Reasoning System",{},{"id":1069,"data":1954,"type":218,"tunes":1956},{"text":1955},"The previous articles can now be understood not as separate prompting techniques but as components of one reasoning architecture.",{},{"id":1074,"data":1958,"type":576,"tunes":1978},{"content":1959,"stretched":43,"withHeadings":14},[1960,1963,1966,1969,1972,1975],[1961,1962],"Article","Function in the Framework",[1964,1965],"\u003Ca href=\"https:\u002F\u002Fstajic.de\u002Fblog\u002Fbeyond-prompt-engineering-a-methodology-for-more-reliable-ai-reasoning\">Beyond Prompt Engineering\u003C\u002Fa>","Defines the shift from answer generation to methodological reasoning.",[1967,1968],"\u003Ca href=\"https:\u002F\u002Fstajic.de\u002Fblog\u002Fthe-prompt-is-part-of-the-bias-how-ai-framing-shapes-reasoning\">The Prompt Is Part of the Bias\u003C\u002Fa>","Treats the user's formulation as a possible source of reasoning bias.",[1970,1971],"\u003Ca href=\"https:\u002F\u002Fstajic.de\u002Fblog\u002Fprompt-invariance-does-the-conclusion-survive-the-prompt\">Prompt Invariance\u003C\u002Fa>","Tests whether the conclusion survives meaningful changes in framing.",[1973,1974],"\u003Ca href=\"https:\u002F\u002Fstajic.de\u002Fblog\u002Ffalsification-for-ai-reasoning-from-answers-to-tested-hypotheses\">Falsification for AI Reasoning\u003C\u002Fa>","Tests whether hypotheses survive disconfirming evidence and serious alternatives.",[1976,1977],"\u003Cb>From Research Protocol to a General AI Reasoning Framework\u003C\u002Fb>","Combines the controls into a domain-independent core with domain-specific validation.",{},{"id":1097,"data":1980,"type":218,"tunes":1982},{"text":1981},"Together they describe a transition:",{},{"id":1102,"data":1984,"type":236,"tunes":1986},{"text":1985,"caption":234,"alignment":235},"Prompt → answer\nbecomes\nProblem → evidence → hypotheses → challenge → validation → calibrated conclusion",{},{"id":1107,"data":1988,"type":42,"tunes":1990},{"text":1989,"level":252},"The Central Principle",{},{"id":1112,"data":1992,"type":218,"tunes":1994},{"text":1993},"The methodology began with a simple observation: asking a powerful reasoning model a question does not automatically mean that every useful reasoning capability available to the model will be used.",{},{"id":1117,"data":1996,"type":218,"tunes":1998},{"text":1997},"The solution is not to force maximal reasoning into every interaction.",{},{"id":1122,"data":2000,"type":218,"tunes":2002},{"text":2001},"The solution is to identify which reasoning controls matter for the task and make them explicit.",{},{"id":1127,"data":2004,"type":236,"tunes":2006},{"text":2005,"caption":234,"alignment":235},"A good AI methodology does not tell the model what conclusion to reach. It defines what a conclusion must survive before it is accepted.",{},{"id":1132,"data":2008,"type":218,"tunes":2010},{"text":2009},"That principle is transferable.",{},{"id":1137,"data":2012,"type":218,"tunes":2014},{"text":2013},"In historical research, the conclusion must survive chronology and source criticism. In debugging, it must survive reproduction and discriminating tests. In architecture, it must survive requirements and operational constraints. In strategy, it must survive market evidence and failure criteria.",{},{"id":1142,"data":2016,"type":218,"tunes":2018},{"text":2017},"The validators change.",{},{"id":1147,"data":2020,"type":218,"tunes":2022},{"text":2021},"The methodological discipline remains.",{},{"id":1152,"data":2024,"type":236,"tunes":2026},{"text":2025,"caption":234,"alignment":235},"The objective is not a model that always thinks longer. It is a system that knows when an answer is not yet justified.",{},{"id":1157,"data":2028,"type":1159,"tunes":2029},{},{},{"id":1162,"data":2031,"type":42,"tunes":2033},{"text":2032,"level":252},"Research Context",{},{"id":1167,"data":2035,"type":218,"tunes":2037},{"text":2036},"The complete framework described in this series is a methodological synthesis rather than an established standardized LLM benchmark. Several adjacent lines of research nevertheless support its central architectural premise: inference quality can change substantially when language-model problem solving is organized as an iterative or multi-stage process rather than a single generation.",{},{"id":1172,"data":2039,"type":218,"tunes":2041},{"text":2040},"ReAct combines reasoning with actions and external observations and demonstrated benefits across question answering, fact verification and interactive decision tasks. Tree of Thoughts explores multiple candidate reasoning paths with evaluation and backtracking rather than committing immediately to one chain. Self-Refine showed improvements across diverse tasks by iterating between generation, feedback and refinement. Reflexion demonstrated that language agents can use verbal feedback from previous attempts to improve later decisions without updating model weights.",{},{"id":1177,"data":2043,"type":218,"tunes":2045},{"text":2044},"These approaches do not implement Prompt Invariance or the falsification-oriented framework proposed here. They provide independent evidence for the broader idea that inference-time procedure matters: model capability and the process through which that capability is exercised are not identical.",{},{"id":1182,"data":2047,"type":218,"tunes":2049},{"text":2048},"The contribution of this series is to organize that insight around epistemic controls: evidence separation, assumption tracking, prompt-framing analysis, competing hypotheses, falsification, provenance, domain validation and calibrated uncertainty.",{},{"id":1187,"data":2051,"type":42,"tunes":2053},{"text":2052,"level":313},"Selected References",{},{"id":1192,"data":2055,"type":375,"tunes":2058},{"meta":2056,"items":2057,"style":374},{},[1196,1197,1198,1199,1200,1201],{},{"id":1204,"data":2060,"type":1159,"tunes":2061},{},{},{"id":1208,"data":2063,"type":42,"tunes":2065},{"text":2064,"level":252},"The AI Reasoning Methodology Series",{},{"id":1213,"data":2067,"type":375,"tunes":2076},{"meta":2068,"items":2069,"style":1036},{"counterType":1022},[2070,2071,2072,2073,2074,2075],"\u003Ca href=\"https:\u002F\u002Fstajic.de\u002Fblog\u002Fbeyond-prompt-engineering-a-methodology-for-more-reliable-ai-reasoning\">\u003Cb>Beyond Prompt Engineering: A Methodology for More Reliable AI Reasoning\u003C\u002Fb>\u003C\u002Fa> — why model capability alone is not enough.","\u003Ca href=\"https:\u002F\u002Fstajic.de\u002Fblog\u002Fthe-prompt-is-part-of-the-bias-how-ai-framing-shapes-reasoning\">\u003Cb>The Prompt Is Part of the Bias\u003C\u002Fb>\u003C\u002Fa> — how task formulation can influence the reasoning environment.","\u003Ca href=\"https:\u002F\u002Fstajic.de\u002Fblog\u002Fprompt-invariance-does-the-conclusion-survive-the-prompt\">\u003Cb>Prompt Invariance: Does the Conclusion Survive the Prompt?\u003C\u002Fb>\u003C\u002Fa> — testing conclusion stability across alternative framings.","\u003Ca href=\"https:\u002F\u002Fstajic.de\u002Fblog\u002Ffalsification-for-ai-reasoning-from-answers-to-tested-hypotheses\">\u003Cb>Falsification for AI Reasoning: From Answers to Tested Hypotheses\u003C\u002Fb>\u003C\u002Fa> — competing hypotheses, counter-evidence and discriminating tests.","\u003Cb>From Research Protocol to a General AI Reasoning Framework\u003C\u002Fb> — integrating the methodology into a reusable domain-independent reasoning core.","A practical application of the same reasoning framework in gaming AI — including game assistants, autonomous agents, patch-sensitive knowledge and live game-state validation — is explored in \u003Ca href=\"https:\u002F\u002Ffigure.rocks\u002Fde\u002Fblog\u002Fwhen-gaming-ai-sounds-right-but-isn-t-the-reasoning-problem-behind-game-assistants-and-agents\">When Gaming AI Sounds Right but Isn't: The Reasoning Problem Behind Game Assistants and Agents on figure.rocks.\u003C\u002Fa>",{},"2.31.6","A methodology developed for rigorous AI-assisted research can be generalized far beyond research itself. By separating evidence from assumptions, testing competing hypotheses, controlling prompt framing, searching for counter-evidence and applying domain-specific validators, the same reasoning architecture can improve debugging, software design, strategy, technical analysis and AI-assisted decision 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U praktičnom testiranju, firmver je uspešno radio i održao ruter stabilnim nekoliko dana, ali stara osnova pokreće važna pitanja o bezbednosti, kontroli modema, putevima nadogradnje i dugoročnoj mogućnosti održavanja.","\u002Fuploads\u002F2026\u002F06\u002Fopenwrt-router-review-dual-sim-04-1781620594653-uvf5oy.webp","2026-06-16T10:39:00.000Z",{"id":2662,"slug":2663,"title":2664,"excerpt":2665,"featuredImage":2666,"publishedAt":2667},"468","ai-agent-memory-is-not-rag-how-to-separate-memory-retrieval-state-and-context","Memorija AI agenta nije RAG: Kako razdvojiti memoriju, pronalaženje, stanje i kontekst","Memorija agenta, RAG, stanje i kontekst često se koriste kao da su međusobno zamenjivi. Oni to nisu. Ovaj praktični arhitektonski model razdvaja ova četiri sloja, pokazuje gde svaki pripada i objašnjava šta se kvari kada ih sistemi stope u jedno.","\u002Fuploads\u002F2026\u002F09\u002Fai-agent-memory-is-not-rag-how-to-separate-memory-retrieval-state-and-context-1790350560308-np0xy6.webp","2026-09-25T11:34:00.000Z",{"id":2669,"slug":2670,"title":2671,"excerpt":2672,"featuredImage":2673,"publishedAt":2674},"436","triggers","Sveobuhvatni vodič za okidače povlačenja u poslovnim AI runbooks-ovima","Ovaj vodič istražuje Rollback Triggers, ključne mehanizme u enterprise AI runbooks koji automatski otkrivaju anomalije i pokreću rollback-ove radi održavanja stabilnosti sistema. Naučite kako da konfigurišete, nadgledate i optimizujete ove triggere za robusne AI deploymente.","\u002Fuploads\u002F2026\u002F06\u002Ftriggers-1781855500994-cibuou.webp","2026-03-01T16:51:00.000Z",{"id":2676,"slug":2677,"title":2678,"excerpt":2679,"featuredImage":2680,"publishedAt":2681},"473","openai-agents-api-vs-agents-sdk-vs-responses-api-what-should-you-build-on-in-2026","OpenAI Agents API vs Agents SDK vs Responses API: Na čemu bi trebalo da gradite u 2026?","OpenAI-jev stek agenata promenio se u septembru 2026. Ovaj arhitekturni vodič razdvaja Agents API, Agents SDK, Responses API i Codex SDK prema vlasništvu nad izvršnim okruženjem—kako bi timovi mogli da izaberu pravu granicu kontrole umesto da porede nazive proizvoda.","\u002Fuploads\u002F2026\u002F09\u002Fopenai-agents-api-vs-agents-sdk-vs-responses-api-what-should-you-build-on-in-2026-1790351846714-zi7lus.webp","2026-09-25T11:56:00.000Z",{"id":2683,"slug":2684,"title":2685,"excerpt":2686,"featuredImage":2687,"publishedAt":2688},"381","enterprise-grade-multi-tenant-architecture-for-an-international-platform","Višezakupna arhitektura korporativnog nivoa za međunarodnu platformu","Loving Rocks je platforma za venčanja poslovne klase, dizajnirana sa istinskom više-zakupnom arhitekturom, izolovanim bazama podataka po zakupcu i ugrađenom internacionalizacijom za globalnu skalabilnost, bezbednost i dugoročnu operativnu stabilnost.","\u002Fuploads\u002F2026\u002F01\u002Fenterprise-grade-multi-tenant-architecture-for-an-international-platform-1769789121298-b6v7ak.webp","2026-01-30T12:04:00.000Z",{"id":2690,"slug":2691,"title":2692,"excerpt":2693,"featuredImage":2694,"publishedAt":2695},"474","migrating-from-openai-agents-sdk-to-the-agents-api-what-actually-changes-architecturally","Migracija sa OpenAI Agents SDK na Agents API: Šta se zapravo menja arhitektonski?","Migracija sa OpenAI Agents SDK na novi Agents API nije samo preimenovanje importa. Granica izvršnog okruženja se menja: petlja agenta, trajna sesija, orkestracija, sažimanje konteksta i oporavak premeštaju se ka upravljanom okviru. Ovaj vodič pokazuje šta treba premestiti, šta treba da ostane u vašoj aplikaciji i kako da dokažete migraciju pre prelaska.","\u002Fuploads\u002F2026\u002F09\u002Fmigrating-from-openai-agents-sdk-to-the-agents-api-what-actually-changes-architecturally-1790352171968-ienxr9.webp","2026-09-25T12:01:00.000Z",{"id":2697,"slug":2698,"title":2699,"excerpt":2700,"featuredImage":2701,"publishedAt":2702},"9","ubuntu-graphics-stack-transition-hybrid-gpu-boot-crashes-wayland-risks-and-stable-deployment-practices","Tranzicija Ubuntu grafičkog steka: Padovi pri pokretanju sa hibridnim GPU-om, Rizici Waylanda i stabilne prakse primene","Nadogradnje Ubuntu desktopa mogu izazvati zaglavljivanje pri pokretanju, nedostajuće sesije prijave i nestabilno renderovanje—posebno na hibridnim Intel + NVIDIA sistemima. Ovaj članak objašnjava osnovnu tranziciju grafičkog steka, zašto dolazi do regresija i kako bezbedno implementirati Ubuntu koristeći LTS bazne linije i validirane strategije drajvera.","\u002Fuploads\u002F2026\u002F01\u002Fchatgpt-image-jan-17-2026-05-23-25-pm-1768673754531-r4c498.webp","2026-01-18T19:14:00.000Z",{"id":2704,"slug":2705,"title":2706,"excerpt":2707,"featuredImage":2708,"publishedAt":2709},"451","test-dev-enterprise","Sveobuhvatni vodič za Test DEv Enterprise Stajic.de: Arhitektura i najbolje prakse","Istražite arhitektonske principe, prednosti i tehničke detalje upravljanja okruženjem za razvoj i testiranje nivoa preduzeća pomoću Test DEv Enterprise Stajic.de.","\u002Fuploads\u002F2026\u002F05\u002Ftest-dev-enterprise-1779534260081-r4dvxn.webp","2026-05-22T23:01:00.000Z",{"id":2711,"slug":2712,"title":2713,"excerpt":2714,"featuredImage":2715,"publishedAt":2716},"435","ultimate-guide-to-acceptance-criteria-for-llm-adoption-in-enterprise-playbooks","Konačni vodič za kriterijume prihvatanja za usvajanje LLM u poslovnim priručnicima","Savladajte veštinu definisanja preciznih kriterijuma prihvatanja kako biste obezbedili uspešnu integraciju LLM-ova u vašem poslovnom okruženju. Ovaj sveobuhvatni vodič pruža praktične okvire, primere i najbolje prakse prilagođene usvajanju vođenom plejbucom.","\u002Fuploads\u002F2026\u002F09\u002Fultimate-guide-to-acceptance-criteria-for-llm-adoption-in-enterprise-playbooks-1788540267775-zgr6mm.webp","2026-09-06T11:50:00.000Z",{"id":2718,"slug":2719,"title":2720,"excerpt":2721,"featuredImage":2722,"publishedAt":2723},"462","the-prompt-is-part-of-the-bias-how-ai-framing-shapes-reasoning","Prompt je deo pristrasnosti: Kako AI uokviravanje oblikuje rezonovanje","Formulacija prompta nije neutralna. Istražite kako uokvirivanje, pretpostavke, praćenje uputstava i podilaženje mogu oblikovati rasuđivanje veštačke inteligencije—i zašto pouzdani zaključci zahtevaju testiranje izvan originalnog prompta.","\u002Fuploads\u002F2026\u002F09\u002Fthe-prompt-is-part-of-the-bias-how-ai-framing-shapes-reasoning-1789804884054-u278vc.webp","2026-09-19T01:04:00.000Z","fallback",[],[]]