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дигитализацију","\u002Fportfolio\u002Fdigitalisierungsportal-archiv-museum-bibliothek-ead-lido-mets-mods",[],{"statusCode":4,"data":206,"message":2628},{"id":207,"title":208,"slug":209,"content":210,"contentJson":211,"excerpt":1219,"featuredImage":1220,"featuredImageAlt":1221,"featuredImageCaption":10,"featuredImageTitle":10,"featuredImageCopyright":10,"featuredImageAuthor":10,"featuredImageSourceUrl":10,"featuredImageLicense":10,"featuredImageIsAiGenerated":43,"status":1222,"publishedAt":1223,"createdAt":1224,"updatedAt":1225,"seoLocalePaths":1226,"categories":1235,"author":1236,"translations":1240},"464","Фалсификовање за резоновање вештачке интелигенције: од одговора до тестираних хипотеза","falsification-for-ai-reasoning-from-answers-to-tested-hypotheses","\u003Cp>Jezički model može da proizvede potkrepljujuće dokaze za iznenađujuće veliki broj verodostojnih objašnjenja.\u003C\u002Fp>\n\u003Cp>Ta sposobnost je korisna za istraživanje, ali opasna kao metod validacije. Ako model počne od hipoteze i zadatak je samo da objasni zašto bi ona mogla biti tačna, koherentan odgovor može se pojaviti mnogo pre nego što je hipoteza prošla ozbiljan test.\u003C\u002Fp>\n\u003Cp>Pouzdano AI rezonovanje stoga zahteva jače pitanje:\u003C\u002Fp>\n\u003Cblockquote class=\"border-l-4 border-gray-300 pl-4 italic\">Koji bi dokazi naterali ovu hipotezu da padne?\u003C\u002Fblockquote>\n\u003Cp>Ovaj članak razvija rezonovanje usmereno na falsifikaciju kao sledeći sloj metodologije predstavljene u \u003Ca href=\"{{STAJIC_ARTICLE_1_URL}}\">Beyond Prompt Engineering: A Methodology for More Reliable AI Reasoning\u003C\u002Fa>. Prethodni članak, \u003Ca href=\"{{STAJIC_ARTICLE_3_URL}}\">Prompt Invariance: Does the Conclusion Survive the Prompt?\u003C\u002Fa>, testira da li zaključak preživljava promene u okviru. Falsifikacija se bavi drugim problemom: da li hipoteza preživljava dokaze koji bi mogli da idu protiv nje.\u003C\u002Fp>\n\u003Cblockquote class=\"border-l-4 border-gray-300 pl-4 italic\">Nepromenljivost upita testira zavisnost od okvira. Falsifikacija testira ranjivost na dokaze.\u003C\u002Fblockquote>\n\u003Ch2>Zašto je potvrđivanje previše lako\u003C\u002Fh2>\n\u003Cp>Pretpostavimo da je AI sistemu data hipoteza i da se od njega traži da utvrdi da li je verodostojna.\u003C\u002Fp>\n\u003Cp>Model može da pretražuje svoje znanje, priložene dokumente ili preuzete izvore radi zapažanja kompatibilnih sa tom hipotezom. Ako se pronađe dovoljno kompatibilnih zapažanja, rezultujuće objašnjenje može postati sve ubedljivije.\u003C\u002Fp>\n\u003Cp>Ali kompatibilnost je slab dokaz kada nekoliko konkurentskih hipoteza predviđa isto zapažanje.\u003C\u002Fp>\n\u003Cp>Razmotrimo apstraktan slučaj:\u003C\u002Fp>\n\u003Cblockquote class=\"border-l-4 border-gray-300 pl-4 italic\">Zapažanje E je kompatibilno sa hipotezom H1.\u003C\u002Fblockquote>\n\u003Cp>Sama ta izjava ne uspostavlja H1. Ako i H2, H3 i H4 takođe predviđaju E, onda E malo doprinosi njihovom razlikovanju.\u003C\u002Fp>\n\u003Cblockquote class=\"border-l-4 border-gray-300 pl-4 italic\">Dokazi postaju informativniji kada konkurentska objašnjenja daju različita predviđanja o tome šta bismo trebali da opazimo.\u003C\u002Fblockquote>\n\u003Cp>Ovo pomera proces rezonovanja sa prikupljanja potkrepljujućih činjenica ka osmišljavanju diskriminišućih testova.\u003C\u002Fp>\n\u003Ch2>Klasična ideja iza falsifikacije\u003C\u002Fh2>\n\u003Cp>Falsifikacionizam Karla Popera naglašavao je asimetriju između verifikacije i opovrgavanja. Ponovljena zapažanja kompatibilna sa univerzalnom tvrdnjom ne mogu logički dokazati da je ta tvrdnja istinita, dok istinsko nekompatibilno zapažanje može biti u direktnom sukobu sa njom.\u003C\u002Fp>\n\u003Cp>U pojednostavljenom logičkom obliku:\u003C\u002Fp>\n\u003Cblockquote class=\"border-l-4 border-gray-300 pl-4 italic\">Ako je H tačno, trebalo bi da se pojavi opažanje O. O se ne pojavljuje. Stoga je H, kako je navedeno pod uslovima testa, dovedeno u pitanje.\u003C\u002Fblockquote>\n\u003Cp>Snaga ovog pristupa ne leži u dokazivanju da su teorije lažne u svakoj prilici, već u zahtevu da se tvrdnje izlože mogućem neuspehu.\u003C\u002Fp>\n\u003Cp>Hipotezu koja može da prihvati svaki zamislivi rezultat bez promene teško je testirati jer nijedno opažanje joj značajno ne protivreči.\u003C\u002Fp>\n\u003Cblockquote class=\"border-l-4 border-gray-300 pl-4 italic\">Korisna hipoteza treba da preuzme rizik: neki mogući dokazi moraju biti manje kompatibilni sa njom nego sa njenim alternativama.\u003C\u002Fblockquote>\n\u003Ch2>Falsifikacija je u praksi složenija\u003C\u002Fh2>\n\u003Cp>Popularna verzija falsifikacije je često previše jednostavna: pojavi se jedno protivrečno opažanje, pa je hipoteza odmah lažna.\u003C\u002Fp>\n\u003Cp>Stvarna istraživanja retko funkcionišu tako čisto.\u003C\u002Fp>\n\u003Cp>Predviđanje obično zavisi ne samo od centralne hipoteze već i od pomoćnih pretpostavki: tačnosti merenja, pouzdanosti izvora, uslova okoline, detalja implementacije, pozadinskih teorija ili potpunosti dostupnih dokaza.\u003C\u002Fp>\n\u003Cp>Realističnija logička struktura je:\u003C\u002Fp>\n\u003Cblockquote class=\"border-l-4 border-gray-300 pl-4 italic\">H + A1 + A2 + A3 → očekivano opažanje O\u003C\u002Fblockquote>\n\u003Cp>Ako se O ne opazi, neuspešno predviđanje nam govori da nešto u kompletnoj eksplanatornoj strukturi nije u redu. Ono nam automatski ne govori koji je deo zakazao.\u003C\u002Fp>\n\u003Cp>Hipoteza može biti pogrešna. Merenje može biti nepouzdano. Pretpostavljeni uslov možda nije bio zadovoljen. Izvor može biti nepotpun. Model je možda pogrešno razumeo dokaze.\u003C\u002Fp>\n\u003Cp>Ova razlika je ključna za AI rezonovanje. Cilj nije naivno odbacivanje. Cilj je sistematsko izlaganje hipoteza dokazima koji su u stanju da ih oslabe.\u003C\u002Fp>\n\u003Ch2>Rezonovanje usmereno na falsifikaciju za LLM-ove\u003C\u002Fh2>\n\u003Cp>Stroga Popperova falsifikacija najprirodnije se primenjuje na tvrdnje koje generišu jasno testirljive posledice. Mnogi zadaci kojima se bave AI asistenti su manje čisti: istorijska interpretacija, otklanjanje grešaka, arhitektura, strategija i kauzalna analiza često uključuju nepotpune dokaze i probabilistička objašnjenja.\u003C\u002Fp>\n\u003Cp>Za te domene, korisniji je opštiji termin \u003Cb>rezonovanje usmereno na falsifikaciju\u003C\u002Fb>.\u003C\u002Fp>\n\u003Cp>Cilj nije nužno dobijanje jednog logički odlučujućeg falsifikatora. Cilj je organizovati analizu tako da dokazi koji mogu protivrečiti, oslabiti ili diskriminisati trenutnu hipotezu dobiju eksplicitnu pažnju.\u003C\u002Fp>\n\u003Cblockquote class=\"border-l-4 border-gray-300 pl-4 italic\">Ne pitajte samo šta podržava H. Pitajte šta bi trebalo da postoji ako je H tačno, šta bi trebalo da bude teško objasniti ako je H tačno i koja alternativa bolje objašnjava iste dokaze.\u003C\u002Fblockquote>\n\u003Ch2>Korak 1 — Precizno navedite hipotezu\u003C\u002Fh2>\n\u003Cp>Hipoteza se ne može smisleno testirati ako je dovoljno neodređena da obuhvati svaki ishod.\u003C\u002Fp>\n\u003Cp>Uporedite:\u003C\u002Fp>\n\u003Cblockquote class=\"border-l-4 border-gray-300 pl-4 italic\">Sistem je nestabilan jer nešto u mrežnom sloju nije u redu.\u003Ccite class=\"block mt-2 text-sm\">— Slaba hipoteza\u003C\u002Fcite>\u003C\u002Fblockquote>\n\u003Cp>sa:\u003C\u002Fp>\n\u003Cblockquote class=\"border-l-4 border-gray-300 pl-4 italic\">Povremene greške API-ja uzrokovane su zatvaranjem nadzemnih veza od strane obrnutog proksija kada se prekorači konfigurisano vremensko ograničenje.\u003Ccite class=\"block mt-2 text-sm\">— Hipoteza koja se može testirati\u003C\u002Fcite>\u003C\u002Fblockquote>\n\u003Cp>Druga tvrdnja izlaže se konkretnim testovima. Možemo pregledati vrednosti vremenskog ograničenja, trajanje veze, zapise proksija, ponašanje nadzemnog sistema i greške koje se javljaju ispod konfigurisanog praga.\u003C\u002Fp>\n\u003Cp>Što preciznije hipoteza definiše tvrdnju o odnosu, to je lakše utvrditi koji bi dokazi išli protiv nje.\u003C\u002Fp>\n\u003Ch2>Korak 2 — Generišite istinske konkurentske hipoteze\u003C\u002Fh2>\n\u003Cp>Testiranje jedne hipoteze izolovano je slabo jer se skoro svako zapažanje tumači u odnosu na nešto drugo.\u003C\u002Fp>\n\u003Cp>Sistem bi stoga trebalo da konstruiše verodostojne alternative pre nego što proceni dokaze.\u003C\u002Fp>\n\u003Cp>Za isti kvar API-ja, moguća objašnjenja mogu uključivati:\u003C\u002Fp>\n\u003Cul>\u003Cli>\u003Cb>H1:\u003C\u002Fb> vremensko ograničenje obrnutog proksija;\u003C\u002Fli>\u003Cli>\u003Cb>H2:\u003C\u002Fb> pad ili restart nadzemne aplikacije;\u003C\u002Fli>\u003Cli>\u003Cb>H3:\u003C\u002Fb> iscrpljenost veza sa bazom podataka;\u003C\u002Fli>\u003Cli>\u003Cb>H4:\u003C\u002Fb> prekid mreže ili gubitak paketa;\u003C\u002Fli>\u003Cli>\u003Cb>H5:\u003C\u002Fb> vremensko ograničenje na strani klijenta;\u003C\u002Fli>\u003Cli>\u003Cb>H6:\u003C\u002Fb> interakcija između nekoliko slojeva, a ne jedan izolovani uzrok.\u003C\u002Fli>\u003C\u002Ful>\n\u003Cp>Alternative moraju biti dovoljno verodostojne da se takmiče. Generisanje očigledno inferiornih alternativa samo stvara privid kritičkog razmišljanja.\u003C\u002Fp>\n\u003Cblockquote class=\"border-l-4 border-gray-300 pl-4 italic\">Hipoteza nije preživela konkurenciju ako su alternative osmišljene da izgube.\u003C\u002Fblockquote>\n\u003Ch2>Korak 3 — Izvedite očekivana zapažanja\u003C\u002Fh2>\n\u003Cp>Za svaku ozbiljnu hipotezu, model bi trebalo da izvede zapažanja koja se očekuju pod tim objašnjenjem.\u003C\u002Fp>\n\u003Cp>Ako je H1 hipoteza o vremenskom ograničenju obrnutog proksija, očekivana zapažanja mogu uključivati greške grupisane oko određenog trajanja, odgovarajuće poruke o vremenskom ograničenju u zapisima proksija, zdrave nadzemne procese u trenutku kvara i nestanak kvara nakon kontrolisane promene vremenskog ograničenja.\u003C\u002Fp>\n\u003Cp>Za H2, hipotezu o restartovanju aplikacije, očekivali bismo drugačiji obrazac: restartovanja procesa, izuzetke, nedostupnost aplikacije, iscrpljivanje resursa ili povezane događaje kontejnera.\u003C\u002Fp>\n\u003Cp>Važan korak je izvesti ova očekivanja \u003Cb>pre\u003C\u002Fb> tumačenja svakog dostupnog zapažanja kao podrške.\u003C\u002Fp>\n\u003Ch2>Korak 4 — Definišite potencijalne opovrgavajuće dokaze\u003C\u002Fh2>\n\u003Cp>Za svaku hipotezu, pitajte koji bi dokazi materijalno oslabili tu hipotezu.\u003C\u002Fp>\n\u003Cblockquote class=\"border-l-4 border-gray-300 pl-4 italic\">Šta bismo očekivali da nećemo primetiti ako je ova hipoteza glavno objašnjenje?\u003C\u002Fblockquote>\n\u003Cp>Za hipotezu o vremenskom ograničenju proksija, primeri uključuju kvarove koji se javljaju daleko ispod konfigurisanog praga, identične kvarove pri zaobilaženju proksija, nepostojanje relevantnog događaja na strani proksija ili dokaze da aplikacija uzvodno prva prekida vezu.\u003C\u002Fp>\n\u003Cp>Ovo menja cilj pretrage modela.\u003C\u002Fp>\n\u003Cblockquote class=\"border-l-4 border-gray-300 pl-4 italic\">Pretraga potvrde: Pronađite dokaze kompatibilne sa H1. Pretraga opovrgavanja: Pronađite zapažanja koja H1 slabo predviđa ili koja H2 predviđa znatno bolje.\u003C\u002Fblockquote>\n\u003Ch2>Korak 5 — Preferirajte diskriminišuće testove\u003C\u002Fh2>\n\u003Cp>Nije svaki test jednako informativan.\u003C\u002Fp>\n\u003Cp>Pretpostavimo da i H1 i H2 predviđaju povišene stope grešaka. Zapažanje još jedne greške pruža malu diskriminaciju.\u003C\u002Fp>\n\u003Cp>Bolji test traži zapažanje na kojem se njihova predviđanja razilaze.\u003C\u002Fp>\n\u003Cblockquote class=\"border-l-4 border-gray-300 pl-4 italic\">Dobar test: zapažanje verovatno pod H1 ali malo verovatno pod H2, ili obrnuto.\u003C\u002Fblockquote>\n\u003Cp>U otklanjanju grešaka, zaobilaženje sumnjivog proksija može biti diskriminišuće. U istorijskom istraživanju, dokazivanje hronologije koja onemogućava direktan prenos može biti snažno diskriminišuće. U analizi proizvoda, zapažanje istog pada potražnje na kontrolnom tržištu na koje predloženi uzrok ne utiče može oslabiti uzročno objašnjenje.\u003C\u002Fp>\n\u003Cp>Metodologija stoga vrednuje dokaze ne samo po pouzdanosti već i po njihovoj sposobnosti da razlikuju konkurentska objašnjenja.\u003C\u002Fp>\n\u003Ch2>Korak 6 — Aktivno tražite kontradokaze\u003C\u002Fh2>\n\u003Cp>Kada su potencijalni falsifikatori i diskriminišuća zapažanja definisani, sistem bi trebalo aktivno da ih traži.\u003C\u002Fp>\n\u003Cp>Ovaj zahtev je važan jer sami jezički modeli mogu pokazivati testiranje hipoteza sa pristrasnošću potvrde.\u003C\u002Fp>\n\u003Cp>Godine 2026. Jhaveri, GX-Chen, Sucholutsky i Choi prilagodili su klasičan zadatak otkrivanja pravila za jedanaest jezičkih modela iz više porodica modela i razmera. Modeli su često predlagali primere koji bi potvrdili njihovo trenutno pravilo, umesto primera osmišljenih da ga opovrgnu.\u003C\u002Fp>\n\u003Cp>Posledica je bila praktična: istraživanje usmereno na potvrđivanje proizvelo je sporije i manje uspešno otkrivanje skrivenog pravila.\u003C\u002Fp>\n\u003Cp>Kada su istraživači izričito podstakli razmatranje kontraprimera, prosečan uspeh otkrivanja pravila porastao je sa 42% na 56% u prijavljenim eksperimentima.\u003C\u002Fp>\n\u003Cblockquote class=\"border-l-4 border-gray-300 pl-4 italic\">Modelu nije bila potrebna nova baza znanja. Bila mu je potrebna bolja strategija testiranja hipoteza.\u003C\u002Fblockquote>\n\u003Cp>Ovo je direktno relevantno za širu metodologiju: kvalitet zaključivanja može se poboljšati kada se proces zaključivanja promeni od traženja potvrde ka istraživanju usmerenom na opovrgavanje.\u003C\u002Fp>\n\u003Ch2>Korak 7 — Razdvojiti protivrečnost od odbacivanja\u003C\u002Fh2>\n\u003Cp>Pronalaženje dokaza protiv hipoteze ne opravdava uvek trenutno odbacivanje.\u003C\u002Fp>\n\u003Cp>Sistem bi prvo trebalo da proceni kvalitet protivdokaza:\u003C\u002Fp>\n\u003Cul>\u003Cli>Da li je zapažanje pouzdano?\u003C\u002Fli>\u003Cli>Da li je izvor primaran ili posredan?\u003C\u002Fli>\u003Cli>Mogu li greška merenja ili pretrage da objasne sukob?\u003C\u002Fli>\u003Cli>Da li hipoteza zaista predviđa sporno zapažanje?\u003C\u002Fli>\u003Cli>Da li protivrečnost zavisi od pomoćne pretpostavke?\u003C\u002Fli>\u003Cli>Da li su protivdokazi nezavisno potkrepljeni?\u003C\u002Fli>\u003Cli>Da li konkurentska hipoteza uspešnije objašnjava dokaze?\u003C\u002Fli>\u003C\u002Ful>\n\u003Cp>Tek nakon ove procene model bi trebalo da utvrdi da li je hipoteza oslabljena, značajno revidirana ili odbačena.\u003C\u002Fp>\n\u003Ch2>Korak 8 — Sprečiti ad hoc spasavanje\u003C\u002Fh2>\n\u003Cp>Hipoteza može postati praktično neopovrgljiva ako svako protivrečno zapažanje proizvede novi izuzetak.\u003C\u002Fp>\n\u003Cp>Obrazac izgleda ovako:\u003C\u002Fp>\n\u003Cblockquote class=\"border-l-4 border-gray-300 pl-4 italic\">Predviđanje ne uspe → dodaj izuzetak → predviđanje ponovo ne uspe → dodaj još jedan izuzetak → zadrži prvobitni zaključak neograničeno\u003C\u002Fblockquote>\n\u003Cp>Nisu sve izmene nelegitimne. Naučni i tehnički napredak često nastaje jer neočekivani dokazi otkrivaju nedostajuću promenljivu ili netačnu pomoćnu pretpostavku.\u003C\u002Fp>\n\u003Cp>Metodološka razlika je u tome da li revizija stvara nove proverljive posledice ili samo štiti preferirani zaključak od neuspeha.\u003C\u002Fp>\n\u003Cblockquote class=\"border-l-4 border-gray-300 pl-4 italic\">Produktivna revizija povećava eksplanatornu i prediktivnu preciznost. Ad hoc spasavanje samo smanjuje šansu da hipoteza ikada može da izgubi.\u003C\u002Fblockquote>\n\u003Ch2>Korak 9 — Ažurirati pouzdanje umesto branjenja početnog odgovora\u003C\u002Fh2>\n\u003Cp>Rezultat rezonovanja usmerenog na falsifikaciju ne mora biti binaran.\u003C\u002Fp>\n\u003Cp>Moguća stanja uključuju:\u003C\u002Fp>\n\u003Cul>\u003Cli>\u003Cb>Čvrsto podržano:\u003C\u002Fb> preživljava ozbiljne diskriminišuće testove, a konkurentska objašnjenja postižu znatno lošije rezultate.\u003C\u002Fli>\u003Cli>\u003Cb>Provizorno podržano:\u003C\u002Fb> najbolje dostupno objašnjenje, ali značajne neizvesnosti ostaju.\u003C\u002Fli>\u003Cli>\u003Cb>Oslabljeno:\u003C\u002Fb> postoje materijalni protivdokazi, ali nisu odlučujući.\u003C\u002Fli>\u003Cli>\u003Cb>Nedovoljno određeno:\u003C\u002Fb> nekoliko hipoteza podjednako dobro objašnjava trenutne dokaze.\u003C\u002Fli>\u003Cli>\u003Cb>Odbijeno:\u003C\u002Fb> pouzdani dokazi su u sukobu sa centralnim predviđanjem, a alternativna objašnjenja postižu bolje rezultate.\u003C\u002Fli>\u003Cli>\u003Cb>Nije testabilno dostupnim dokazima:\u003C\u002Fb> trenutni korpus ne može smisleno razlikovati tvrdnje.\u003C\u002Fli>\u003C\u002Ful>\n\u003Cp>Centralno pravilo je jednostavno: pouzdanje treba da prati rezultat testova, a ne retoričku investiranost modela u njegov prvi odgovor.\u003C\u002Fp>\n\u003Ch2>Matrica falsifikacije\u003C\u002Fh2>\n\u003Cp>Za složenu analizu, hipoteze se mogu normalizovati u matricu poređenja.\u003C\u002Fp>\n\u003Ctable class=\"w-full border-collapse\">\u003Ctr>\u003Ctd class=\"border border-gray-300 px-4 py-2\">Dimenzija\u003C\u002Ftd>\u003Ctd class=\"border border-gray-300 px-4 py-2\">H1\u003C\u002Ftd>\u003Ctd class=\"border border-gray-300 px-4 py-2\">H2\u003C\u002Ftd>\u003Ctd class=\"border border-gray-300 px-4 py-2\">H3\u003C\u002Ftd>\u003C\u002Ftr>\u003Ctr>\u003Ctd class=\"border border-gray-300 px-4 py-2\">Osnovna tvrdnja\u003C\u002Ftd>\u003Ctd class=\"border border-gray-300 px-4 py-2\">Definisati precizno\u003C\u002Ftd>\u003Ctd class=\"border border-gray-300 px-4 py-2\">Definisati precizno\u003C\u002Ftd>\u003Ctd class=\"border border-gray-300 px-4 py-2\">Definisati precizno\u003C\u002Ftd>\u003C\u002Ftr>\u003Ctr>\u003Ctd class=\"border border-gray-300 px-4 py-2\">Očekivani dokazi\u003C\u002Ftd>\u003Ctd class=\"border border-gray-300 px-4 py-2\">Navesti predviđanja\u003C\u002Ftd>\u003Ctd class=\"border border-gray-300 px-4 py-2\">Navesti predviđanja\u003C\u002Ftd>\u003Ctd class=\"border border-gray-300 px-4 py-2\">Navesti predviđanja\u003C\u002Ftd>\u003C\u002Ftr>\u003Ctr>\u003Ctd class=\"border border-gray-300 px-4 py-2\">Potencijalni protivdokazi\u003C\u002Ftd>\u003Ctd class=\"border border-gray-300 px-4 py-2\">Definisati\u003C\u002Ftd>\u003Ctd class=\"border border-gray-300 px-4 py-2\">Definisati\u003C\u002Ftd>\u003Ctd class=\"border border-gray-300 px-4 py-2\">Definisati\u003C\u002Ftd>\u003C\u002Ftr>\u003Ctr>\u003Ctd class=\"border border-gray-300 px-4 py-2\">Diskriminišući test\u003C\u002Ftd>\u003Ctd class=\"border border-gray-300 px-4 py-2\">Specificirati\u003C\u002Ftd>\u003Ctd class=\"border border-gray-300 px-4 py-2\">Specificirati\u003C\u002Ftd>\u003Ctd class=\"border border-gray-300 px-4 py-2\">Specificirati\u003C\u002Ftd>\u003C\u002Ftr>\u003Ctr>\u003Ctd class=\"border border-gray-300 px-4 py-2\">Podržavajuća zapažanja\u003C\u002Ftd>\u003Ctd class=\"border border-gray-300 px-4 py-2\">Zabeležiti\u003C\u002Ftd>\u003Ctd class=\"border border-gray-300 px-4 py-2\">Zabeležiti\u003C\u002Ftd>\u003Ctd class=\"border border-gray-300 px-4 py-2\">Zabeležiti\u003C\u002Ftd>\u003C\u002Ftr>\u003Ctr>\u003Ctd class=\"border border-gray-300 px-4 py-2\">Protivrečna zapažanja\u003C\u002Ftd>\u003Ctd class=\"border border-gray-300 px-4 py-2\">Zabeležiti\u003C\u002Ftd>\u003Ctd class=\"border border-gray-300 px-4 py-2\">Zabeležiti\u003C\u002Ftd>\u003Ctd class=\"border border-gray-300 px-4 py-2\">Zabeležiti\u003C\u002Ftd>\u003C\u002Ftr>\u003Ctr>\u003Ctd class=\"border border-gray-300 px-4 py-2\">Pomoćne pretpostavke\u003C\u002Ftd>\u003Ctd class=\"border border-gray-300 px-4 py-2\">Izložiti\u003C\u002Ftd>\u003Ctd class=\"border border-gray-300 px-4 py-2\">Izložiti\u003C\u002Ftd>\u003Ctd class=\"border border-gray-300 px-4 py-2\">Izložiti\u003C\u002Ftd>\u003C\u002Ftr>\u003Ctr>\u003Ctd class=\"border border-gray-300 px-4 py-2\">Trenutni status\u003C\u002Ftd>\u003Ctd class=\"border border-gray-300 px-4 py-2\">Ponovo proceniti\u003C\u002Ftd>\u003Ctd class=\"border border-gray-300 px-4 py-2\">Ponovo proceniti\u003C\u002Ftd>\u003Ctd class=\"border border-gray-300 px-4 py-2\">Ponovo proceniti\u003C\u002Ftd>\u003C\u002Ftr>\u003C\u002Ftable>\n\u003Cp>Matrica sprečava čest način neuspeha: primenu rigoroznog ispitivanja na alternative, dok se preferiranoj hipotezi dozvoljava da ostane nejasna.\u003C\u002Fp>\n\u003Ch2>Negativni dokazi zahtevaju posebnu pažnju\u003C\u002Fh2>\n\u003Cp>Odsustvo očekivanih dokaza može oslabiti hipotezu, ali samo pod određenim uslovima.\u003C\u002Fp>\n\u003Cp>Tvrdnja 'nismo pronašli dokaze za X' nije ekvivalentna tvrdnji 'X se nije dogodio'.\u003C\u002Fp>\n\u003Cp>Negativni dokazi postaju informativni kada postoji opravdano očekivanje da bi dokazi verovatno bili uočljivi, sačuvani, zabeleženi, dokumentovani ili merljivi ako je hipoteza tačna.\u003C\u002Fp>\n\u003Cblockquote class=\"border-l-4 border-gray-300 pl-4 italic\">Odsustvo dokaza je najvažnije kada dokazi ne bi trebalo da budu odsutni.\u003C\u002Fblockquote>\n\u003Cp>U otklanjanju grešaka, odsustvo potrebnog događaja u dnevniku može biti značajno ako se zna da je evidentiranje potpuno. U istorijskom istraživanju, odsustvo iz fragmentarnog arhiva obično je mnogo slabije. U bezbednosnoj analizi, odsustvo upozorenja ima malu vrednost ako relevantna telemetrija nikada nije prikupljena.\u003C\u002Fp>\n\u003Cp>Model stoga mora proceniti i dokaze koji nedostaju i verovatnoću da bi takvi dokazi preživeli ili bili uočljivi.\u003C\u002Fp>\n\u003Ch2>Istorijsko istraživanje: prenošenje nasuprot sličnosti\u003C\u002Fh2>\n\u003Cp>Istorijsko istraživanje ilustruje zašto rezonovanje usmereno na falsifikaciju mora biti specifično za domen.\u003C\u002Fp>\n\u003Cp>Pretpostavimo da dve tradicije sadrže konceptualno slične ideje, a početna hipoteza predlaže direktno prenošenje.\u003C\u002Fp>\n\u003Cp>Podržavajuća sličnost nije dovoljna. Hipoteza treba da generiše dodatna očekivanja: hronološku kompatibilnost, verovatnu geografsku kontakt, posrednike, tekstualne ili terminološke tragove, dokumentarne dokaze ili obrazac transformacije koji je konzistentan sa prenošenjem.\u003C\u002Fp>\n\u003Cp>Potencijalno štetna zapažanja mogu uključivati hronologiju koja obrće predloženi smer, geografsku izolaciju nekompatibilnu sa tvrđenim mehanizmom, ranije nezavisne primere u obe tradicije, ili dokaze da se navodna zajednička karakteristika pojavila samo u mnogo kasnijim reinterpretacijama.\u003C\u002Fp>\n\u003Cp>Nijedno pojedinačno odsustvo ne mora nužno da opovrgne istorijsko prenošenje. Ali nekoliko nezavisnih neuspeha može da smanji njegovu eksplanatornu prednost u odnosu na konvergenciju ili indirektno nasleđivanje.\u003C\u002Fp>\n\u003Ch2>Otklanjanje grešaka u softveru: Od sumnjivca do korenskog uzroka\u003C\u002Fh2>\n\u003Cp>Otklanjanje grešaka prirodno ima koristi od opovrgavanja jer cilj nije da se stvori verodostojna priča oko poruke o grešci. Cilj je izolovati mehanizam koji proizvodi neuspeh.\u003C\u002Fp>\n\u003Cp>Korisna petlja za otklanjanje grešaka je:\u003C\u002Fp>\n\u003Cblockquote class=\"border-l-4 border-gray-300 pl-4 italic\">Simptom → kandidati za uzrok → predviđena zapažanja → diskriminišući test → eliminisanje uzroka → reprodukcija → korenski uzrok\u003C\u002Fblockquote>\n\u003Cp>Hipoteza postaje jača ne zato što se više teksta može napisati u njenu korist, već zato što realistične alternative padaju na testovima koje ona prolazi.\u003C\u002Fp>\n\u003Ch2>Softverska arhitektura: Opovrgavanje dizajnerske odluke\u003C\u002Fh2>\n\u003Cp>Arhitektonske odluke se obično ne mogu opovrgnuti u strogom naučnom smislu, ali se mogu podvrgnuti analizi usmerenoj na opovrgavanje.\u003C\u002Fp>\n\u003Cp>Pretpostavimo da je hipoteza:\u003C\u002Fp>\n\u003Cblockquote class=\"border-l-4 border-gray-300 pl-4 italic\">Mikroservisna arhitektura je neophodna da bi se zadovoljili zahtevi sistema za skalabilnošću i organizacijom.\u003C\u002Fblockquote>\n\u003Cp>Umesto nabrajanja prednosti mikroservisa, analiza treba da pita šta bi učinilo tvrdnju nepotrebnom.\u003C\u002Fp>\n\u003Cp>Ako realistično testiranje opterećenja pokaže da modularni monolit zadovoljava očekivani obim, ako nezavisnost implementacije nije potrebna, i ako operativna složenost postane dominantni trošak, originalna tvrdnja je materijalno oslabljena.\u003C\u002Fp>\n\u003Cp>Cilj nije opovrgnuti mikroservise kao tehnologiju. Cilj je testirati specifičnu arhitektonsku tvrdnju pod ograničenjima projekta.\u003C\u002Fp>\n\u003Ch2>Strategija: Šta bi učinilo poslovnu tezu pogrešnom?\u003C\u002Fh2>\n\u003Cp>Poslovna strategija često pati od potvrde jer se dokazi mogu tumačiti naknadno.\u003C\u002Fp>\n\u003Cp>Jači proces definiše kriterijume neuspeha pre izvršenja.\u003C\u002Fp>\n\u003Cp>Ako produktna teza predviđa da će ciljni segment platiti za određenu sposobnost, metodologija treba da definiše koje bi uočljivo ponašanje oslabilo tu tvrdnju: niska konverzija nakon kvalifikovane izloženosti, ponovljeno odbijanje iz istog razloga, nemogućnost održavanja ciljne cene ili dokaz da korisnici rešavaju problem kroz alternativni tok rada.\u003C\u002Fp>\n\u003Cblockquote class=\"border-l-4 border-gray-300 pl-4 italic\">Strategija postaje proverljivija kada su njeni kriterijumi uspeha praćeni eksplicitnim kriterijumima neuspeha.\u003C\u002Fblockquote>\n\u003Ch2>Falsifikacija nije isto što i đavolji advokat\u003C\u002Fh2>\n\u003Cp>Model koji dobije instrukciju da 'argumentuje protiv ovog zaključka' uvek može da generiše prigovore.\u003C\u002Fp>\n\u003Cp>To još uvek nije falsifikacija.\u003C\u002Fp>\n\u003Cp>Đavolji advokat optimizuje za suprotstavljanje. Rezonovanje usmereno na falsifikaciju optimizuje za informativne testove.\u003C\u002Fp>\n\u003Cblockquote class=\"border-l-4 border-gray-300 pl-4 italic\">Dobar protivargument zvuči uverljivo. Dobar test falsifikacije ima rezultat koji menja ono u šta treba da verujemo.\u003C\u002Fblockquote>\n\u003Cp>Ova razlika sprečava da proces verifikacije degradira u veštačku debatu u kojoj jedan model argumentuje za stav, a drugi automatski argumentuje protiv njega.\u003C\u002Fp>\n\u003Ch2>Falsifikacija i invarijantnost upita rade zajedno\u003C\u002Fh2>\n\u003Cp>Invarijantnost upita i falsifikacija testiraju različite zavisnosti.\u003C\u002Fp>\n\u003Ctable class=\"w-full border-collapse\">\u003Ctr>\u003Ctd class=\"border border-gray-300 px-4 py-2\">Metod\u003C\u002Ftd>\u003Ctd class=\"border border-gray-300 px-4 py-2\">Primarno pitanje\u003C\u002Ftd>\u003Ctd class=\"border border-gray-300 px-4 py-2\">Otkriva\u003C\u002Ftd>\u003C\u002Ftr>\u003Ctr>\u003Ctd class=\"border border-gray-300 px-4 py-2\">Invarijantnost upita\u003C\u002Ftd>\u003Ctd class=\"border border-gray-300 px-4 py-2\">Da li zaključak preživljava alternativne legitimne okvire?\u003C\u002Ftd>\u003Ctd class=\"border border-gray-300 px-4 py-2\">Zavisnost od okvira upita\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\">Da li hipoteza preživljava dokaze osmišljene da je ospore?\u003C\u002Ftd>\u003Ctd class=\"border border-gray-300 px-4 py-2\">Zavisnost od potvrđivanja i slabog testiranja\u003C\u002Ftd>\u003C\u002Ftr>\u003C\u002Ftable>\n\u003Cp>Hipoteza može da prođe jedan test, a padne drugi.\u003C\u002Fp>\n\u003Cp>Model može da reprodukuje isti netačan zaključak pod nekoliko formulacija upita, proizvodeći visoku stabilnost okvira ali slabu dokaznu validnost. Obrnuto, jaka hipoteza može da izgleda nestabilno jer različiti upiti otkrivaju različite podskupove nepotpunih dokaza.\u003C\u002Fp>\n\u003Cp>Kombinovanje obe metode stvara jači sled:\u003C\u002Fp>\n\u003Cblockquote class=\"border-l-4 border-gray-300 pl-4 italic\">Varijacija okvira → konkurentske hipoteze → očekivana zapažanja → kontradokazi → diskriminišući testovi → ažuriranje pouzdanja\u003C\u002Fblockquote>\n\u003Ch2>Ne dozvolite da isti agent nekritički sudi o sopstvenom testu\u003C\u002Fh2>\n\u003Cp>Postoji još jedan arhitektonski problem.\u003C\u002Fp>\n\u003Cp>Ako isti model generiše hipotezu, osmišljava test, tumači dokaze i odlučuje da li je hipoteza preživela, njegove greške mogu da se propagiraju kroz svaku fazu.\u003C\u002Fp>\n\u003Cp>Ovo ne čini proces beskorisnim, ali motiviše razdvajanje uloga.\u003C\u002Fp>\n\u003Cblockquote class=\"border-l-4 border-gray-300 pl-4 italic\">Generator hipoteza → dizajner testova → pronalazač dokaza → kritičar → verifikator → finalni sintetizator\u003C\u002Fblockquote>\n\u003Cp>Ove uloge ne zahtevaju nužno šest različitih modela. Mogu se implementirati kao izolovani inferencijski prolazi sa odvojenim kontekstom, kontrolisanim dokazima i strukturiranim izlazima.\u003C\u002Fp>\n\u003Cp>Važno svojstvo je proceduralna nezavisnost: kasnije faze ne bi trebalo jednostavno da naslede retoričku obavezu originalnog odgovora.\u003C\u002Fp>\n\u003Cp>Ovo postaje tehnička tema \u003Ca href=\"{{STAJIC_ARTICLE_6_URL}}\">Dizajniranja sloja epistemičke verifikacije za LLM-ove\u003C\u002Fa>.\u003C\u002Fp>\n\u003Ch2>Opšti tok rada AI orijentisan ka falsifikaciji\u003C\u002Fh2>\n\u003Cblockquote class=\"border-l-4 border-gray-300 pl-4 italic\">Problem → normalizacija dokaza → konkurentske hipoteze → predviđanja → potencijalni falsifikatori → diskriminišući testovi → pretraga protivdokaza → provera pomoćnih pretpostavki → poređenje hipoteza → rekalibracija pouzdanja → zaključak\u003C\u002Fblockquote>\n\u003Cp>Ovaj tok rada ne zahteva da se svaki zadatak ponaša kao laboratorijska nauka.\u003C\u002Fp>\n\u003Cp>Umesto toga, on izvlači opšti epistemički princip iz falsifikacije: objašnjenja treba izložiti uslovima pod kojima mogu da izgube.\u003C\u002Fp>\n\u003Cp>Tačan validator se zatim menja u zavisnosti od domena.\u003C\u002Fp>\n\u003Cp>Istorijska analiza testira hronologiju, poreklo i prenošenje. Otklanjanje grešaka testira uočljivo ponašanje sistema. Arhitektura testira zahteve i ograničenja. Strategija testira tržišne pretpostavke i unapred definisane kriterijume neuspeha.\u003C\u002Fp>\n\u003Cp>Ovo jezgro nezavisno od domena i njegovi validatori specifični za domen dalje su razvijeni u \u003Ca href=\"{{STAJIC_ARTICLE_5_URL}}\">Od istraživačkog protokola do opšteg okvira za AI rezonovanje\u003C\u002Fa>.\u003C\u002Fp>\n\u003Ch2>Šta falsifikacija ne može da uradi\u003C\u002Fh2>\n\u003Cul>\u003Cli>Ne može da učini nepotpune dokaze potpunim.\u003C\u002Fli>\u003Cli>Ne može da garantuje da je generisana ispravna alternativna hipoteza.\u003C\u002Fli>\u003Cli>Ne može da eliminiše greške koje dele model, izvori i proces evaluacije.\u003C\u002Fli>\u003Cli>Ne može da pretvori suštinski interpretativne tvrdnje u laboratorijske eksperimente.\u003C\u002Fli>\u003Cli>Ne može da tretira svako nedostajuće opažanje kao dokaz protiv hipoteze.\u003C\u002Fli>\u003Cli>Ne može automatski da identifikuje koja pomoćna pretpostavka nije uspela kada je predviđanje opovrgnuto.\u003C\u002Fli>\u003Cli>Ne može da dokaže da je hipoteza koja je opstala istinita.\u003C\u002Fli>\u003Cli>Ne može da zameni eksperimente, primarne izvore, ekspertizu u domenu ili empirijsko merenje tamo gde su oni potrebni.\u003C\u002Fli>\u003C\u002Ful>\n\u003Cp>Hipoteza koja preživi ponovljene pokušaje falsifikacije bolje se opisuje kao \u003Cb>potkrepljena izvršenim testovima\u003C\u002Fb> nego kao dokazana.\u003C\u002Fp>\n\u003Ch2>Centralni princip\u003C\u002Fh2>\n\u003Cp>Generativna AI čini potvrđivanje jeftinim.\u003C\u002Fp>\n\u003Cp>Za dovoljno verodostojnu tvrdnju, moćan jezički model obično može da proizvede argumente, analogije, potkrepljujuće činjenice i koherentne narative oko nje.\u003C\u002Fp>\n\u003Cp>Upravo zato potvrda ne bi trebalo da bude konačni test.\u003C\u002Fp>\n\u003Cblockquote class=\"border-l-4 border-gray-300 pl-4 italic\">Pouzdan proces zaključivanja ne bi trebalo da pita samo zašto bi hipoteza mogla biti tačna. On mora takođe da definiše kako bi hipoteza mogla biti pogrešna.\u003C\u002Fblockquote>\n\u003Cp>Kvalitet zaključka AI stoga ne zavisi samo od toga koliko dokaza koji ga podržavaju sistem može da pronađe, već i od toga da li je konkurentskim objašnjenjima dozvoljeno da pobede.\u003C\u002Fp>\n\u003Cp>To menja ulogu modela.\u003C\u002Fp>\n\u003Cp>On više nije samo generator odgovora.\u003C\u002Fp>\n\u003Cp>On postaje učesnik u kontrolisanom procesu u kojem je njegovo sopstveno prvo objašnjenje privremeno, proverljivo i zamenljivo.\u003C\u002Fp>\n\u003Cblockquote class=\"border-l-4 border-gray-300 pl-4 italic\">Najjači AI odgovor nije onaj sa najviše argumenata koji ga podržavaju. To je onaj čijim je najjačim alternativama data poštena prilika da ga pobede.\u003C\u002Fblockquote>\n\u003Cdiv class=\"ce-delimiter cdx-block my-8\">\u003C\u002Fdiv>\n\u003Ch2>Kontekst istraživanja\u003C\u002Fh2>\n\u003Cp>Metodologija u ovom članku prilagođava ideje iz filozofije nauke i savremenih empirijskih istraživanja zaključivanja jezičkih modela. Karl Popper je svojim falsifikacionizmom naglasio da naučne tvrdnje treba da se izlože mogućim zapažanjima koja su sa njima u sukobu, dok je kasnija filozofija nauke jasno pokazala da je praktična falsifikacija složenija od jednostavnog odbacivanja hipoteze nakon jednog anomalnog zapažanja.\u003C\u002Fp>\n\u003Cp>Razlika je važna za AI sisteme jer stvarni testovi obično zavise od pomoćnih pretpostavki, kvaliteta dokaza i interpretacije. Zaključivanje AI usmereno na falsifikaciju stoga koristi logiku opovrgavanja, a da pritom ne pretpostavlja da se svaka složena analitička tvrdnja može svesti na jedan odlučujući eksperiment.\u003C\u002Fp>\n\u003Cp>Studija iz 2026. koju su sproveli Jhaveri, GX-Chen, Sucholutsky i Choi pruža direktnu empirijsku motivaciju za ovaj dizajn. Na jedanaest LLM-ova, autori su otkrili istraživanje hipoteza pristrasno ka potvrđivanju u interaktivnom zadatku otkrivanja pravila. Podsticanje modela da razmotre kontraprimere dosledno je smanjilo ovu pristrasnost i povećalo prosečnu stopu otkrivanja pravila sa 42% na 56%.\u003C\u002Fp>\n\u003Cp>Ovi nalazi ne pokazuju da je kompletna metodologija predložena ovde eksperimentalno validirana kao jedinstveni okvir. Oni podržavaju užu i važnu tvrdnju: eksplicitne intervencije ka opovrgavajućim dokazima mogu poboljšati istraživanje hipoteza kod LLM-ova.\u003C\u002Fp>\n\u003Ch3>Odabrane reference\u003C\u002Fh3>\n\u003Cul>\u003Cli>Popper, K. R. — \u003Ci>The Logic of Scientific Discovery\u003C\u002Fi>. Englesko izdanje, 1959.\u003C\u002Fli>\u003Cli>Popper, K. R. — \u003Ci>Conjectures and Refutations: The Growth of Scientific Knowledge\u003C\u002Fi>. 1963.\u003C\u002Fli>\u003Cli>Stanford Encyclopedia of Philosophy — \u003Ci>Scientific Method\u003C\u002Fi>, odeljci o hipotetičko-deduktivnom testiranju i falsifikacionizmu.\u003C\u002Fli>\u003Cli>Stanford Encyclopedia of Philosophy — \u003Ci>Karl Popper\u003C\u002Fi>, rasprava o osnovnim iskazima, falsifikabilnosti i praktičnim komplikacijama falsifikacije.\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>. arXiv:2604.02485, 2026.\u003C\u002Fli>\u003C\u002Ful>\n\u003Cdiv class=\"ce-delimiter cdx-block my-8\">\u003C\u002Fdiv>\n\u003Ch2>Nastavite seriju\u003C\u002Fh2>\n\u003Cul>\u003Cli>\u003Cb>\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>\u003C\u002Fb> — kompletan metodološki okvir.\u003C\u002Fli>\u003Cli>\u003Cb>\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\u002Fb> — zašto okvir i pretpostavke korisnika mogu uticati na AI zaključivanje.\u003C\u002Fli>\u003Cli>\u003Cb>\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\u002Fb> — testiranje da li zaključci preživljavaju slepe, obrnute i suprotstavljene okvire.\u003C\u002Fli>\u003Cli>\u003Cb>\u003Ca href=\"https:\u002F\u002Fstajic.de\u002Fsr\u002Fblog\u002Ffrom-research-protocol-to-a-general-ai-reasoning-framework\">From Research Protocol to General AI Reasoning Framework\u003C\u002Fa>\u003C\u002Fb> — pretvaranje metodologije u proces zaključivanja nezavisan od domena.\u003C\u002Fli>\u003C\u002Ful>",{"time":212,"blocks":213,"version":1218},1789811393126,[214,220,225,230,238,243,248,254,259,264,269,274,279,284,289,294,299,304,309,314,320,326,331,336,342,348,354,360,365,371,377,383,388,394,400,406,411,416,421,426,432,437,443,448,453,458,463,468,473,487,492,497,502,507,512,517,522,527,532,537,542,547,552,557,562,567,572,577,582,587,592,597,602,607,612,617,622,627,632,637,642,655,660,665,670,675,680,685,690,695,700,705,710,722,727,732,737,771,776,781,786,791,796,801,806,811,816,821,826,831,836,841,846,851,856,861,866,871,876,881,886,891,896,901,906,911,916,921,926,931,936,941,946,951,956,961,966,983,988,993,998,1003,1008,1013,1018,1023,1028,1033,1038,1043,1048,1053,1058,1063,1068,1073,1078,1083,1097,1102,1107,1112,1117,1122,1127,1132,1137,1142,1147,1152,1157,1162,1167,1172,1177,1182,1188,1199,1203,1208],{"id":215,"data":216,"type":218,"tunes":219},"i02QyfgW6t",{"text":217},"Jezički model može da proizvede potkrepljujuće dokaze za iznenađujuće veliki broj verodostojnih objašnjenja.","paragraph",{},{"id":221,"data":222,"type":218,"tunes":224},"67IgneVFBd",{"text":223},"Ta sposobnost je korisna za istraživanje, ali opasna kao metod validacije. Ako model počne od hipoteze i zadatak je samo da objasni zašto bi ona mogla biti tačna, koherentan odgovor može se pojaviti mnogo pre nego što je hipoteza prošla ozbiljan test.",{},{"id":226,"data":227,"type":218,"tunes":229},"167Yo_p807",{"text":228},"Pouzdano AI rezonovanje stoga zahteva jače pitanje:",{},{"id":231,"data":232,"type":236,"tunes":237},"Jv6rg5BSPG",{"text":233,"caption":234,"alignment":235},"Koji bi dokazi naterali ovu hipotezu da padne?","","left","quote",{},{"id":239,"data":240,"type":218,"tunes":242},"NiwGy1wCDc",{"text":241},"Ovaj članak razvija rezonovanje usmereno na falsifikaciju kao sledeći sloj metodologije predstavljene u \u003Ca href=\"{{STAJIC_ARTICLE_1_URL}}\">Beyond Prompt Engineering: A Methodology for More Reliable AI Reasoning\u003C\u002Fa>. Prethodni članak, \u003Ca href=\"{{STAJIC_ARTICLE_3_URL}}\">Prompt Invariance: Does the Conclusion Survive the Prompt?\u003C\u002Fa>, testira da li zaključak preživljava promene u okviru. Falsifikacija se bavi drugim problemom: da li hipoteza preživljava dokaze koji bi mogli da idu protiv nje.",{},{"id":244,"data":245,"type":236,"tunes":247},"VQxKWrgVxK",{"text":246,"caption":234,"alignment":235},"Nepromenljivost upita testira zavisnost od okvira. Falsifikacija testira ranjivost na dokaze.",{},{"id":249,"data":250,"type":42,"tunes":253},"JqaeAPhxbk",{"text":251,"level":252},"Zašto je potvrđivanje previše lako",2,{},{"id":255,"data":256,"type":218,"tunes":258},"lKD9JfgkoH",{"text":257},"Pretpostavimo da je AI sistemu data hipoteza i da se od njega traži da utvrdi da li je verodostojna.",{},{"id":260,"data":261,"type":218,"tunes":263},"5sJsr6TXCH",{"text":262},"Model može da pretražuje svoje znanje, priložene dokumente ili preuzete izvore radi zapažanja kompatibilnih sa tom hipotezom. Ako se pronađe dovoljno kompatibilnih zapažanja, rezultujuće objašnjenje može postati sve ubedljivije.",{},{"id":265,"data":266,"type":218,"tunes":268},"XDmsbhLaOL",{"text":267},"Ali kompatibilnost je slab dokaz kada nekoliko konkurentskih hipoteza predviđa isto zapažanje.",{},{"id":270,"data":271,"type":218,"tunes":273},"W-PqvKBX2Y",{"text":272},"Razmotrimo apstraktan slučaj:",{},{"id":275,"data":276,"type":236,"tunes":278},"r4irpPsK3Z",{"text":277,"caption":234,"alignment":235},"Zapažanje E je kompatibilno sa hipotezom H1.",{},{"id":280,"data":281,"type":218,"tunes":283},"c5OGlvsYEo",{"text":282},"Sama ta izjava ne uspostavlja H1. Ako i H2, H3 i H4 takođe predviđaju E, onda E malo doprinosi njihovom razlikovanju.",{},{"id":285,"data":286,"type":236,"tunes":288},"3k2YJOSEw2",{"text":287,"caption":234,"alignment":235},"Dokazi postaju informativniji kada konkurentska objašnjenja daju različita predviđanja o tome šta bismo trebali da opazimo.",{},{"id":290,"data":291,"type":218,"tunes":293},"CCu-NbuXmW",{"text":292},"Ovo pomera proces rezonovanja sa prikupljanja potkrepljujućih činjenica ka osmišljavanju diskriminišućih testova.",{},{"id":295,"data":296,"type":42,"tunes":298},"SoStpl-vwv",{"text":297,"level":252},"Klasična ideja iza falsifikacije",{},{"id":300,"data":301,"type":218,"tunes":303},"jId-oX1tg3",{"text":302},"Falsifikacionizam Karla Popera naglašavao je asimetriju između verifikacije i opovrgavanja. Ponovljena zapažanja kompatibilna sa univerzalnom tvrdnjom ne mogu logički dokazati da je ta tvrdnja istinita, dok istinsko nekompatibilno zapažanje može biti u direktnom sukobu sa njom.",{},{"id":305,"data":306,"type":218,"tunes":308},"cifut7fdtF",{"text":307},"U pojednostavljenom logičkom obliku:",{},{"id":310,"data":311,"type":236,"tunes":313},"yJb9ANIBq_",{"text":312,"caption":234,"alignment":235},"Ako je H tačno, trebalo bi da se pojavi opažanje O. O se ne pojavljuje. Stoga je H, kako je navedeno pod uslovima testa, dovedeno u pitanje.",{},{"id":315,"data":316,"type":218,"tunes":319},"2d7LhSjBst",{"text":317,"type":218,"tunes":318},"Snaga ovog pristupa ne leži u dokazivanju da su teorije lažne u svakoj prilici, već u zahtevu da se tvrdnje izlože mogućem neuspehu.",{},{},{"id":321,"data":322,"type":218,"tunes":325},"kSTwb_cUlk",{"text":323,"type":218,"tunes":324},"Hipotezu koja može da prihvati svaki zamislivi rezultat bez promene teško je testirati jer nijedno opažanje joj značajno ne protivreči.",{},{},{"id":327,"data":328,"type":236,"tunes":330},"xgSvZ2PHwL",{"text":329,"caption":234,"alignment":235},"Korisna hipoteza treba da preuzme rizik: neki mogući dokazi moraju biti manje kompatibilni sa njom nego sa njenim alternativama.",{},{"id":332,"data":333,"type":42,"tunes":335},"gvC5oqbfST",{"text":334,"level":252},"Falsifikacija je u praksi složenija",{},{"id":337,"data":338,"type":218,"tunes":341},"s69877zNlb",{"text":339,"type":218,"tunes":340},"Popularna verzija falsifikacije je često previše jednostavna: pojavi se jedno protivrečno opažanje, pa je hipoteza odmah lažna.",{},{},{"id":343,"data":344,"type":218,"tunes":347},"iCd3z7e_yJ",{"text":345,"type":218,"tunes":346},"Stvarna istraživanja retko funkcionišu tako čisto.",{},{},{"id":349,"data":350,"type":218,"tunes":353},"4aa_Y4Miwj",{"text":351,"type":218,"tunes":352},"Predviđanje obično zavisi ne samo od centralne hipoteze već i od pomoćnih pretpostavki: tačnosti merenja, pouzdanosti izvora, uslova okoline, detalja implementacije, pozadinskih teorija ili potpunosti dostupnih dokaza.",{},{},{"id":355,"data":356,"type":218,"tunes":359},"wNC4zca5ig",{"text":357,"type":218,"tunes":358},"Realističnija logička struktura je:",{},{},{"id":361,"data":362,"type":236,"tunes":364},"w9qV9-x03c",{"text":363,"caption":234,"alignment":235},"H + A1 + A2 + A3 → očekivano opažanje O",{},{"id":366,"data":367,"type":218,"tunes":370},"IfZbepy0sG",{"text":368,"type":218,"tunes":369},"Ako se O ne opazi, neuspešno predviđanje nam govori da nešto u kompletnoj eksplanatornoj strukturi nije u redu. Ono nam automatski ne govori koji je deo zakazao.",{},{},{"id":372,"data":373,"type":218,"tunes":376},"2oDpL0yId4",{"text":374,"type":218,"tunes":375},"Hipoteza može biti pogrešna. Merenje može biti nepouzdano. Pretpostavljeni uslov možda nije bio zadovoljen. Izvor može biti nepotpun. Model je možda pogrešno razumeo dokaze.",{},{},{"id":378,"data":379,"type":218,"tunes":382},"2I16bMuYeb",{"text":380,"type":218,"tunes":381},"Ova razlika je ključna za AI rezonovanje. Cilj nije naivno odbacivanje. Cilj je sistematsko izlaganje hipoteza dokazima koji su u stanju da ih oslabe.",{},{},{"id":384,"data":385,"type":42,"tunes":387},"kGTeE6jRa1",{"text":386,"level":252},"Rezonovanje usmereno na falsifikaciju za LLM-ove",{},{"id":389,"data":390,"type":218,"tunes":393},"aWVwveQXLj",{"text":391,"type":218,"tunes":392},"Stroga Popperova falsifikacija najprirodnije se primenjuje na tvrdnje koje generišu jasno testirljive posledice. Mnogi zadaci kojima se bave AI asistenti su manje čisti: istorijska interpretacija, otklanjanje grešaka, arhitektura, strategija i kauzalna analiza često uključuju nepotpune dokaze i probabilistička objašnjenja.",{},{},{"id":395,"data":396,"type":218,"tunes":399},"OzhnuXxG3m",{"text":397,"type":218,"tunes":398},"Za te domene, korisniji je opštiji termin \u003Cb>rezonovanje usmereno na falsifikaciju\u003C\u002Fb>.",{},{},{"id":401,"data":402,"type":218,"tunes":405},"tmWDhTYpw1",{"text":403,"type":218,"tunes":404},"Cilj nije nužno dobijanje jednog logički odlučujućeg falsifikatora. Cilj je organizovati analizu tako da dokazi koji mogu protivrečiti, oslabiti ili diskriminisati trenutnu hipotezu dobiju eksplicitnu pažnju.",{},{},{"id":407,"data":408,"type":236,"tunes":410},"Qo3NPH1q3j",{"text":409,"caption":234,"alignment":235},"Ne pitajte samo šta podržava H. Pitajte šta bi trebalo da postoji ako je H tačno, šta bi trebalo da bude teško objasniti ako je H tačno i koja alternativa bolje objašnjava iste dokaze.",{},{"id":412,"data":413,"type":42,"tunes":415},"fckuCMmX6d",{"text":414,"level":252},"Korak 1 — Precizno navedite hipotezu",{},{"id":417,"data":418,"type":218,"tunes":420},"JluAFx81nA",{"text":419},"Hipoteza se ne može smisleno testirati ako je dovoljno neodređena da obuhvati svaki ishod.",{},{"id":422,"data":423,"type":218,"tunes":425},"e2OMHHe4Nf",{"text":424},"Uporedite:",{},{"id":427,"data":428,"type":236,"tunes":431},"0PtMJwVhsb",{"text":429,"caption":430,"alignment":235},"Sistem je nestabilan jer nešto u mrežnom sloju nije u redu.","Slaba hipoteza",{},{"id":433,"data":434,"type":218,"tunes":436},"We3dkWldqK",{"text":435},"sa:",{},{"id":438,"data":439,"type":236,"tunes":442},"TYnOciItVj",{"text":440,"caption":441,"alignment":235},"Povremene greške API-ja uzrokovane su zatvaranjem nadzemnih veza od strane obrnutog proksija kada se prekorači konfigurisano vremensko ograničenje.","Hipoteza koja se može testirati",{},{"id":444,"data":445,"type":218,"tunes":447},"JQ7E5dA4Ha",{"text":446},"Druga tvrdnja izlaže se konkretnim testovima. Možemo pregledati vrednosti vremenskog ograničenja, trajanje veze, zapise proksija, ponašanje nadzemnog sistema i greške koje se javljaju ispod konfigurisanog praga.",{},{"id":449,"data":450,"type":218,"tunes":452},"FXtEDznyAh",{"text":451},"Što preciznije hipoteza definiše tvrdnju o odnosu, to je lakše utvrditi koji bi dokazi išli protiv nje.",{},{"id":454,"data":455,"type":42,"tunes":457},"9rpdQoi2vw",{"text":456,"level":252},"Korak 2 — Generišite istinske konkurentske hipoteze",{},{"id":459,"data":460,"type":218,"tunes":462},"rFmosy87ye",{"text":461},"Testiranje jedne hipoteze izolovano je slabo jer se skoro svako zapažanje tumači u odnosu na nešto drugo.",{},{"id":464,"data":465,"type":218,"tunes":467},"O-91zwQt8x",{"text":466},"Sistem bi stoga trebalo da konstruiše verodostojne alternative pre nego što proceni dokaze.",{},{"id":469,"data":470,"type":218,"tunes":472},"_epkMsKypP",{"text":471},"Za isti kvar API-ja, moguća objašnjenja mogu uključivati:",{},{"id":474,"data":475,"type":485,"tunes":486},"w5kp-by4bv",{"meta":476,"items":477,"style":484},{},[478,479,480,481,482,483],"\u003Cb>H1:\u003C\u002Fb> vremensko ograničenje obrnutog proksija;","\u003Cb>H2:\u003C\u002Fb> pad ili restart nadzemne aplikacije;","\u003Cb>H3:\u003C\u002Fb> iscrpljenost veza sa bazom podataka;","\u003Cb>H4:\u003C\u002Fb> prekid mreže ili gubitak paketa;","\u003Cb>H5:\u003C\u002Fb> vremensko ograničenje na strani klijenta;","\u003Cb>H6:\u003C\u002Fb> interakcija između nekoliko slojeva, a ne jedan izolovani uzrok.","unordered","list",{},{"id":488,"data":489,"type":218,"tunes":491},"-b5SlkifJB",{"text":490},"Alternative moraju biti dovoljno verodostojne da se takmiče. Generisanje očigledno inferiornih alternativa samo stvara privid kritičkog razmišljanja.",{},{"id":493,"data":494,"type":236,"tunes":496},"uq-fd--TLQ",{"text":495,"caption":234,"alignment":235},"Hipoteza nije preživela konkurenciju ako su alternative osmišljene da izgube.",{},{"id":498,"data":499,"type":42,"tunes":501},"HYAdyOKBw6",{"text":500,"level":252},"Korak 3 — Izvedite očekivana zapažanja",{},{"id":503,"data":504,"type":218,"tunes":506},"zPNz32Lu3K",{"text":505},"Za svaku ozbiljnu hipotezu, model bi trebalo da izvede zapažanja koja se očekuju pod tim objašnjenjem.",{},{"id":508,"data":509,"type":218,"tunes":511},"liNj49Sano",{"text":510},"Ako je H1 hipoteza o vremenskom ograničenju obrnutog proksija, očekivana zapažanja mogu uključivati greške grupisane oko određenog trajanja, odgovarajuće poruke o vremenskom ograničenju u zapisima proksija, zdrave nadzemne procese u trenutku kvara i nestanak kvara nakon kontrolisane promene vremenskog ograničenja.",{},{"id":513,"data":514,"type":218,"tunes":516},"Cme39nLtEl",{"text":515},"Za H2, hipotezu o restartovanju aplikacije, očekivali bismo drugačiji obrazac: restartovanja procesa, izuzetke, nedostupnost aplikacije, iscrpljivanje resursa ili povezane događaje kontejnera.",{},{"id":518,"data":519,"type":218,"tunes":521},"gs6yDl-O9i",{"text":520},"Važan korak je izvesti ova očekivanja \u003Cb>pre\u003C\u002Fb> tumačenja svakog dostupnog zapažanja kao podrške.",{},{"id":523,"data":524,"type":42,"tunes":526},"5JooacBK3t",{"text":525,"level":252},"Korak 4 — Definišite potencijalne opovrgavajuće dokaze",{},{"id":528,"data":529,"type":218,"tunes":531},"4TVRQ0Fe7o",{"text":530},"Za svaku hipotezu, pitajte koji bi dokazi materijalno oslabili tu hipotezu.",{},{"id":533,"data":534,"type":236,"tunes":536},"mRYP6ANWRE",{"text":535,"caption":234,"alignment":235},"Šta bismo očekivali da nećemo primetiti ako je ova hipoteza glavno objašnjenje?",{},{"id":538,"data":539,"type":218,"tunes":541},"YZG2Ij_nxm",{"text":540},"Za hipotezu o vremenskom ograničenju proksija, primeri uključuju kvarove koji se javljaju daleko ispod konfigurisanog praga, identične kvarove pri zaobilaženju proksija, nepostojanje relevantnog događaja na strani proksija ili dokaze da aplikacija uzvodno prva prekida vezu.",{},{"id":543,"data":544,"type":218,"tunes":546},"_MoPXmNQWQ",{"text":545},"Ovo menja cilj pretrage modela.",{},{"id":548,"data":549,"type":236,"tunes":551},"LxF512uQwp",{"text":550,"caption":234,"alignment":235},"Pretraga potvrde: Pronađite dokaze kompatibilne sa H1. Pretraga opovrgavanja: Pronađite zapažanja koja H1 slabo predviđa ili koja H2 predviđa znatno bolje.",{},{"id":553,"data":554,"type":42,"tunes":556},"gAalT3qoTw",{"text":555,"level":252},"Korak 5 — Preferirajte diskriminišuće testove",{},{"id":558,"data":559,"type":218,"tunes":561},"dfY5nrMkxJ",{"text":560},"Nije svaki test jednako informativan.",{},{"id":563,"data":564,"type":218,"tunes":566},"D5Hc-vzXbt",{"text":565},"Pretpostavimo da i H1 i H2 predviđaju povišene stope grešaka. Zapažanje još jedne greške pruža malu diskriminaciju.",{},{"id":568,"data":569,"type":218,"tunes":571},"87X08XjEQZ",{"text":570},"Bolji test traži zapažanje na kojem se njihova predviđanja razilaze.",{},{"id":573,"data":574,"type":236,"tunes":576},"-8M3bAYrvX",{"text":575,"caption":234,"alignment":235},"Dobar test: zapažanje verovatno pod H1 ali malo verovatno pod H2, ili obrnuto.",{},{"id":578,"data":579,"type":218,"tunes":581},"ElUma_dnFK",{"text":580},"U otklanjanju grešaka, zaobilaženje sumnjivog proksija može biti diskriminišuće. U istorijskom istraživanju, dokazivanje hronologije koja onemogućava direktan prenos može biti snažno diskriminišuće. U analizi proizvoda, zapažanje istog pada potražnje na kontrolnom tržištu na koje predloženi uzrok ne utiče može oslabiti uzročno objašnjenje.",{},{"id":583,"data":584,"type":218,"tunes":586},"aMsCv48OJa",{"text":585},"Metodologija stoga vrednuje dokaze ne samo po pouzdanosti već i po njihovoj sposobnosti da razlikuju konkurentska objašnjenja.",{},{"id":588,"data":589,"type":42,"tunes":591},"QQ4Th-nwqA",{"text":590,"level":252},"Korak 6 — Aktivno tražite kontradokaze",{},{"id":593,"data":594,"type":218,"tunes":596},"ahSEkuIa-7",{"text":595},"Kada su potencijalni falsifikatori i diskriminišuća zapažanja definisani, sistem bi trebalo aktivno da ih traži.",{},{"id":598,"data":599,"type":218,"tunes":601},"U8HkPTs6X-",{"text":600},"Ovaj zahtev je važan jer sami jezički modeli mogu pokazivati testiranje hipoteza sa pristrasnošću potvrde.",{},{"id":603,"data":604,"type":218,"tunes":606},"WEm5yhr_7P",{"text":605},"Godine 2026. Jhaveri, GX-Chen, Sucholutsky i Choi prilagodili su klasičan zadatak otkrivanja pravila za jedanaest jezičkih modela iz više porodica modela i razmera. Modeli su često predlagali primere koji bi potvrdili njihovo trenutno pravilo, umesto primera osmišljenih da ga opovrgnu.",{},{"id":608,"data":609,"type":218,"tunes":611},"zXM8383nSR",{"text":610},"Posledica je bila praktična: istraživanje usmereno na potvrđivanje proizvelo je sporije i manje uspešno otkrivanje skrivenog pravila.",{},{"id":613,"data":614,"type":218,"tunes":616},"eHdYtwyjnH",{"text":615},"Kada su istraživači izričito podstakli razmatranje kontraprimera, prosečan uspeh otkrivanja pravila porastao je sa 42% na 56% u prijavljenim eksperimentima.",{},{"id":618,"data":619,"type":236,"tunes":621},"eifCoJKlFl",{"text":620,"caption":234,"alignment":235},"Modelu nije bila potrebna nova baza znanja. Bila mu je potrebna bolja strategija testiranja hipoteza.",{},{"id":623,"data":624,"type":218,"tunes":626},"ww3-vggbJb",{"text":625},"Ovo je direktno relevantno za širu metodologiju: kvalitet zaključivanja može se poboljšati kada se proces zaključivanja promeni od traženja potvrde ka istraživanju usmerenom na opovrgavanje.",{},{"id":628,"data":629,"type":42,"tunes":631},"T2Bj2B59tj",{"text":630,"level":252},"Korak 7 — Razdvojiti protivrečnost od odbacivanja",{},{"id":633,"data":634,"type":218,"tunes":636},"fAdASfms4W",{"text":635},"Pronalaženje dokaza protiv hipoteze ne opravdava uvek trenutno odbacivanje.",{},{"id":638,"data":639,"type":218,"tunes":641},"6bdAMQedEe",{"text":640},"Sistem bi prvo trebalo da proceni kvalitet protivdokaza:",{},{"id":643,"data":644,"type":485,"tunes":654},"QFsFbZAgsF",{"meta":645,"items":646,"style":484},{},[647,648,649,650,651,652,653],"Da li je zapažanje pouzdano?","Da li je izvor primaran ili posredan?","Mogu li greška merenja ili pretrage da objasne sukob?","Da li hipoteza zaista predviđa sporno zapažanje?","Da li protivrečnost zavisi od pomoćne pretpostavke?","Da li su protivdokazi nezavisno potkrepljeni?","Da li konkurentska hipoteza uspešnije objašnjava dokaze?",{},{"id":656,"data":657,"type":218,"tunes":659},"cQeyIvJfKt",{"text":658},"Tek nakon ove procene model bi trebalo da utvrdi da li je hipoteza oslabljena, značajno revidirana ili odbačena.",{},{"id":661,"data":662,"type":42,"tunes":664},"uniCBTmcCp",{"text":663,"level":252},"Korak 8 — Sprečiti ad hoc spasavanje",{},{"id":666,"data":667,"type":218,"tunes":669},"ktukb4T7sy",{"text":668},"Hipoteza može postati praktično neopovrgljiva ako svako protivrečno zapažanje proizvede novi izuzetak.",{},{"id":671,"data":672,"type":218,"tunes":674},"OX1R7u-I9n",{"text":673},"Obrazac izgleda ovako:",{},{"id":676,"data":677,"type":236,"tunes":679},"fiyn_CdbEe",{"text":678,"caption":234,"alignment":235},"Predviđanje ne uspe → dodaj izuzetak → predviđanje ponovo ne uspe → dodaj još jedan izuzetak → zadrži prvobitni zaključak neograničeno",{},{"id":681,"data":682,"type":218,"tunes":684},"u7tc5RfwKs",{"text":683},"Nisu sve izmene nelegitimne. Naučni i tehnički napredak često nastaje jer neočekivani dokazi otkrivaju nedostajuću promenljivu ili netačnu pomoćnu pretpostavku.",{},{"id":686,"data":687,"type":218,"tunes":689},"QYchvE4e-r",{"text":688},"Metodološka razlika je u tome da li revizija stvara nove proverljive posledice ili samo štiti preferirani zaključak od neuspeha.",{},{"id":691,"data":692,"type":236,"tunes":694},"AZiFmZekHx",{"text":693,"caption":234,"alignment":235},"Produktivna revizija povećava eksplanatornu i prediktivnu preciznost. Ad hoc spasavanje samo smanjuje šansu da hipoteza ikada može da izgubi.",{},{"id":696,"data":697,"type":42,"tunes":699},"vv0xWnfmOB",{"text":698,"level":252},"Korak 9 — Ažurirati pouzdanje umesto branjenja početnog odgovora",{},{"id":701,"data":702,"type":218,"tunes":704},"uAWov1akKH",{"text":703},"Rezultat rezonovanja usmerenog na falsifikaciju ne mora biti binaran.",{},{"id":706,"data":707,"type":218,"tunes":709},"xVJwRBl45g",{"text":708},"Moguća stanja uključuju:",{},{"id":711,"data":712,"type":485,"tunes":721},"x5-wIlzXG1",{"meta":713,"items":714,"style":484},{},[715,716,717,718,719,720],"\u003Cb>Čvrsto podržano:\u003C\u002Fb> preživljava ozbiljne diskriminišuće testove, a konkurentska objašnjenja postižu znatno lošije rezultate.","\u003Cb>Provizorno podržano:\u003C\u002Fb> najbolje dostupno objašnjenje, ali značajne neizvesnosti ostaju.","\u003Cb>Oslabljeno:\u003C\u002Fb> postoje materijalni protivdokazi, ali nisu odlučujući.","\u003Cb>Nedovoljno određeno:\u003C\u002Fb> nekoliko hipoteza podjednako dobro objašnjava trenutne dokaze.","\u003Cb>Odbijeno:\u003C\u002Fb> pouzdani dokazi su u sukobu sa centralnim predviđanjem, a alternativna objašnjenja postižu bolje rezultate.","\u003Cb>Nije testabilno dostupnim dokazima:\u003C\u002Fb> trenutni korpus ne može smisleno razlikovati tvrdnje.",{},{"id":723,"data":724,"type":218,"tunes":726},"7sV9rnMWz4",{"text":725},"Centralno pravilo je jednostavno: pouzdanje treba da prati rezultat testova, a ne retoričku investiranost modela u njegov prvi odgovor.",{},{"id":728,"data":729,"type":42,"tunes":731},"BH5ZZ1JFol",{"text":730,"level":252},"Matrica falsifikacije",{},{"id":733,"data":734,"type":218,"tunes":736},"ryK4KQRrjy",{"text":735},"Za složenu analizu, hipoteze se mogu normalizovati u matricu poređenja.",{},{"id":738,"data":739,"type":769,"tunes":770},"Q3XRo3fg6I",{"content":740,"stretched":43,"withHeadings":14},[741,746,749,752,755,758,761,763,766],[742,743,744,745],"Dimenzija","H1","H2","H3",[747,748,748,748],"Osnovna tvrdnja","Definisati precizno",[750,751,751,751],"Očekivani dokazi","Navesti predviđanja",[753,754,754,754],"Potencijalni protivdokazi","Definisati",[756,757,757,757],"Diskriminišući test","Specificirati",[759,760,760,760],"Podržavajuća zapažanja","Zabeležiti",[762,760,760,760],"Protivrečna zapažanja",[764,765,765,765],"Pomoćne pretpostavke","Izložiti",[767,768,768,768],"Trenutni status","Ponovo proceniti","table",{},{"id":772,"data":773,"type":218,"tunes":775},"YCn6rUs0hW",{"text":774},"Matrica sprečava čest način neuspeha: primenu rigoroznog ispitivanja na alternative, dok se preferiranoj hipotezi dozvoljava da ostane nejasna.",{},{"id":777,"data":778,"type":42,"tunes":780},"BQg7UmiVg2",{"text":779,"level":252},"Negativni dokazi zahtevaju posebnu pažnju",{},{"id":782,"data":783,"type":218,"tunes":785},"2tC0EX6EJi",{"text":784},"Odsustvo očekivanih dokaza može oslabiti hipotezu, ali samo pod određenim uslovima.",{},{"id":787,"data":788,"type":218,"tunes":790},"KtDzLct2Ef",{"text":789},"Tvrdnja 'nismo pronašli dokaze za X' nije ekvivalentna tvrdnji 'X se nije dogodio'.",{},{"id":792,"data":793,"type":218,"tunes":795},"XuucSV3LV4",{"text":794},"Negativni dokazi postaju informativni kada postoji opravdano očekivanje da bi dokazi verovatno bili uočljivi, sačuvani, zabeleženi, dokumentovani ili merljivi ako je hipoteza tačna.",{},{"id":797,"data":798,"type":236,"tunes":800},"tThM65Pyb2",{"text":799,"caption":234,"alignment":235},"Odsustvo dokaza je najvažnije kada dokazi ne bi trebalo da budu odsutni.",{},{"id":802,"data":803,"type":218,"tunes":805},"7XW_PhILzD",{"text":804},"U otklanjanju grešaka, odsustvo potrebnog događaja u dnevniku može biti značajno ako se zna da je evidentiranje potpuno. U istorijskom istraživanju, odsustvo iz fragmentarnog arhiva obično je mnogo slabije. U bezbednosnoj analizi, odsustvo upozorenja ima malu vrednost ako relevantna telemetrija nikada nije prikupljena.",{},{"id":807,"data":808,"type":218,"tunes":810},"cNn_H3lw4E",{"text":809},"Model stoga mora proceniti i dokaze koji nedostaju i verovatnoću da bi takvi dokazi preživeli ili bili uočljivi.",{},{"id":812,"data":813,"type":42,"tunes":815},"nSt2auDic3",{"text":814,"level":252},"Istorijsko istraživanje: prenošenje nasuprot sličnosti",{},{"id":817,"data":818,"type":218,"tunes":820},"7ktuWOnwGt",{"text":819},"Istorijsko istraživanje ilustruje zašto rezonovanje usmereno na falsifikaciju mora biti specifično za domen.",{},{"id":822,"data":823,"type":218,"tunes":825},"xqHnRKeahO",{"text":824},"Pretpostavimo da dve tradicije sadrže konceptualno slične ideje, a početna hipoteza predlaže direktno prenošenje.",{},{"id":827,"data":828,"type":218,"tunes":830},"6CWXj4SV7j",{"text":829},"Podržavajuća sličnost nije dovoljna. Hipoteza treba da generiše dodatna očekivanja: hronološku kompatibilnost, verovatnu geografsku kontakt, posrednike, tekstualne ili terminološke tragove, dokumentarne dokaze ili obrazac transformacije koji je konzistentan sa prenošenjem.",{},{"id":832,"data":833,"type":218,"tunes":835},"bjtNul_qo_",{"text":834},"Potencijalno štetna zapažanja mogu uključivati hronologiju koja obrće predloženi smer, geografsku izolaciju nekompatibilnu sa tvrđenim mehanizmom, ranije nezavisne primere u obe tradicije, ili dokaze da se navodna zajednička karakteristika pojavila samo u mnogo kasnijim reinterpretacijama.",{},{"id":837,"data":838,"type":218,"tunes":840},"zKwGPoGQzo",{"text":839},"Nijedno pojedinačno odsustvo ne mora nužno da opovrgne istorijsko prenošenje. Ali nekoliko nezavisnih neuspeha može da smanji njegovu eksplanatornu prednost u odnosu na konvergenciju ili indirektno nasleđivanje.",{},{"id":842,"data":843,"type":42,"tunes":845},"U0mHfz_a9d",{"text":844,"level":252},"Otklanjanje grešaka u softveru: Od sumnjivca do korenskog uzroka",{},{"id":847,"data":848,"type":218,"tunes":850},"i61Enjpd_n",{"text":849},"Otklanjanje grešaka prirodno ima koristi od opovrgavanja jer cilj nije da se stvori verodostojna priča oko poruke o grešci. Cilj je izolovati mehanizam koji proizvodi neuspeh.",{},{"id":852,"data":853,"type":218,"tunes":855},"olkjV81zDQ",{"text":854},"Korisna petlja za otklanjanje grešaka je:",{},{"id":857,"data":858,"type":236,"tunes":860},"-tk7jk1syZ",{"text":859,"caption":234,"alignment":235},"Simptom → kandidati za uzrok → predviđena zapažanja → diskriminišući test → eliminisanje uzroka → reprodukcija → korenski uzrok",{},{"id":862,"data":863,"type":218,"tunes":865},"U0d0J91Nmi",{"text":864},"Hipoteza postaje jača ne zato što se više teksta može napisati u njenu korist, već zato što realistične alternative padaju na testovima koje ona prolazi.",{},{"id":867,"data":868,"type":42,"tunes":870},"BnzW9s6w7s",{"text":869,"level":252},"Softverska arhitektura: Opovrgavanje dizajnerske odluke",{},{"id":872,"data":873,"type":218,"tunes":875},"-_v_seOsQz",{"text":874},"Arhitektonske odluke se obično ne mogu opovrgnuti u strogom naučnom smislu, ali se mogu podvrgnuti analizi usmerenoj na opovrgavanje.",{},{"id":877,"data":878,"type":218,"tunes":880},"tVnm2PR7NZ",{"text":879},"Pretpostavimo da je hipoteza:",{},{"id":882,"data":883,"type":236,"tunes":885},"C_orszCiPc",{"text":884,"caption":234,"alignment":235},"Mikroservisna arhitektura je neophodna da bi se zadovoljili zahtevi sistema za skalabilnošću i organizacijom.",{},{"id":887,"data":888,"type":218,"tunes":890},"-NTcWjXLop",{"text":889},"Umesto nabrajanja prednosti mikroservisa, analiza treba da pita šta bi učinilo tvrdnju nepotrebnom.",{},{"id":892,"data":893,"type":218,"tunes":895},"XgU5OD5-P8",{"text":894},"Ako realistično testiranje opterećenja pokaže da modularni monolit zadovoljava očekivani obim, ako nezavisnost implementacije nije potrebna, i ako operativna složenost postane dominantni trošak, originalna tvrdnja je materijalno oslabljena.",{},{"id":897,"data":898,"type":218,"tunes":900},"5ftgXYPJqM",{"text":899},"Cilj nije opovrgnuti mikroservise kao tehnologiju. Cilj je testirati specifičnu arhitektonsku tvrdnju pod ograničenjima projekta.",{},{"id":902,"data":903,"type":42,"tunes":905},"gCnhyaLF4z",{"text":904,"level":252},"Strategija: Šta bi učinilo poslovnu tezu pogrešnom?",{},{"id":907,"data":908,"type":218,"tunes":910},"ATKm56ZPgX",{"text":909},"Poslovna strategija često pati od potvrde jer se dokazi mogu tumačiti naknadno.",{},{"id":912,"data":913,"type":218,"tunes":915},"027zKakU-x",{"text":914},"Jači proces definiše kriterijume neuspeha pre izvršenja.",{},{"id":917,"data":918,"type":218,"tunes":920},"GmUrknec1R",{"text":919},"Ako produktna teza predviđa da će ciljni segment platiti za određenu sposobnost, metodologija treba da definiše koje bi uočljivo ponašanje oslabilo tu tvrdnju: niska konverzija nakon kvalifikovane izloženosti, ponovljeno odbijanje iz istog razloga, nemogućnost održavanja ciljne cene ili dokaz da korisnici rešavaju problem kroz alternativni tok rada.",{},{"id":922,"data":923,"type":236,"tunes":925},"3fBRSfaMnz",{"text":924,"caption":234,"alignment":235},"Strategija postaje proverljivija kada su njeni kriterijumi uspeha praćeni eksplicitnim kriterijumima neuspeha.",{},{"id":927,"data":928,"type":42,"tunes":930},"TDMNbb0olF",{"text":929,"level":252},"Falsifikacija nije isto što i đavolji advokat",{},{"id":932,"data":933,"type":218,"tunes":935},"GeS9ueELEm",{"text":934},"Model koji dobije instrukciju da 'argumentuje protiv ovog zaključka' uvek može da generiše prigovore.",{},{"id":937,"data":938,"type":218,"tunes":940},"E9pcdMnCbO",{"text":939},"To još uvek nije falsifikacija.",{},{"id":942,"data":943,"type":218,"tunes":945},"iRxRCMHcB3",{"text":944},"Đavolji advokat optimizuje za suprotstavljanje. Rezonovanje usmereno na falsifikaciju optimizuje za informativne testove.",{},{"id":947,"data":948,"type":236,"tunes":950},"cZNcdygnrR",{"text":949,"caption":234,"alignment":235},"Dobar protivargument zvuči uverljivo. Dobar test falsifikacije ima rezultat koji menja ono u šta treba da verujemo.",{},{"id":952,"data":953,"type":218,"tunes":955},"Fr6fM7CRRr",{"text":954},"Ova razlika sprečava da proces verifikacije degradira u veštačku debatu u kojoj jedan model argumentuje za stav, a drugi automatski argumentuje protiv njega.",{},{"id":957,"data":958,"type":42,"tunes":960},"RUIiffCsNB",{"text":959,"level":252},"Falsifikacija i invarijantnost upita rade zajedno",{},{"id":962,"data":963,"type":218,"tunes":965},"c99tT5oc8Z",{"text":964},"Invarijantnost upita i falsifikacija testiraju različite zavisnosti.",{},{"id":967,"data":968,"type":769,"tunes":982},"ypIJCeQo6Y",{"content":969,"stretched":43,"withHeadings":14},[970,974,978],[971,972,973],"Metod","Primarno pitanje","Otkriva",[975,976,977],"Invarijantnost upita","Da li zaključak preživljava alternativne legitimne okvire?","Zavisnost od okvira upita",[979,980,981],"Falsifikacija","Da li hipoteza preživljava dokaze osmišljene da je ospore?","Zavisnost od potvrđivanja i slabog testiranja",{},{"id":984,"data":985,"type":218,"tunes":987},"xShjY_11BX",{"text":986},"Hipoteza može da prođe jedan test, a padne drugi.",{},{"id":989,"data":990,"type":218,"tunes":992},"MRp3QSvyjN",{"text":991},"Model može da reprodukuje isti netačan zaključak pod nekoliko formulacija upita, proizvodeći visoku stabilnost okvira ali slabu dokaznu validnost. Obrnuto, jaka hipoteza može da izgleda nestabilno jer različiti upiti otkrivaju različite podskupove nepotpunih dokaza.",{},{"id":994,"data":995,"type":218,"tunes":997},"cP-8sIVjug",{"text":996},"Kombinovanje obe metode stvara jači sled:",{},{"id":999,"data":1000,"type":236,"tunes":1002},"99J5rojU2O",{"text":1001,"caption":234,"alignment":235},"Varijacija okvira → konkurentske hipoteze → očekivana zapažanja → kontradokazi → diskriminišući testovi → ažuriranje pouzdanja",{},{"id":1004,"data":1005,"type":42,"tunes":1007},"iLQPA55iCQ",{"text":1006,"level":252},"Ne dozvolite da isti agent nekritički sudi o sopstvenom testu",{},{"id":1009,"data":1010,"type":218,"tunes":1012},"OUKeUN2F5w",{"text":1011},"Postoji još jedan arhitektonski problem.",{},{"id":1014,"data":1015,"type":218,"tunes":1017},"Ua0N7Lba8U",{"text":1016},"Ako isti model generiše hipotezu, osmišljava test, tumači dokaze i odlučuje da li je hipoteza preživela, njegove greške mogu da se propagiraju kroz svaku fazu.",{},{"id":1019,"data":1020,"type":218,"tunes":1022},"g7uGYjgWy-",{"text":1021},"Ovo ne čini proces beskorisnim, ali motiviše razdvajanje uloga.",{},{"id":1024,"data":1025,"type":236,"tunes":1027},"np9u2NUdXz",{"text":1026,"caption":234,"alignment":235},"Generator hipoteza → dizajner testova → pronalazač dokaza → kritičar → verifikator → finalni sintetizator",{},{"id":1029,"data":1030,"type":218,"tunes":1032},"eYJ1Z8Dw5_",{"text":1031},"Ove uloge ne zahtevaju nužno šest različitih modela. Mogu se implementirati kao izolovani inferencijski prolazi sa odvojenim kontekstom, kontrolisanim dokazima i strukturiranim izlazima.",{},{"id":1034,"data":1035,"type":218,"tunes":1037},"2n3qG7vB2D",{"text":1036},"Važno svojstvo je proceduralna nezavisnost: kasnije faze ne bi trebalo jednostavno da naslede retoričku obavezu originalnog odgovora.",{},{"id":1039,"data":1040,"type":218,"tunes":1042},"vqlRGTUywH",{"text":1041},"Ovo postaje tehnička tema \u003Ca href=\"{{STAJIC_ARTICLE_6_URL}}\">Dizajniranja sloja epistemičke verifikacije za LLM-ove\u003C\u002Fa>.",{},{"id":1044,"data":1045,"type":42,"tunes":1047},"ruojX3kDwu",{"text":1046,"level":252},"Opšti tok rada AI orijentisan ka falsifikaciji",{},{"id":1049,"data":1050,"type":236,"tunes":1052},"S8taBOUfb0",{"text":1051,"caption":234,"alignment":235},"Problem → normalizacija dokaza → konkurentske hipoteze → predviđanja → potencijalni falsifikatori → diskriminišući testovi → pretraga protivdokaza → provera pomoćnih pretpostavki → poređenje hipoteza → rekalibracija pouzdanja → zaključak",{},{"id":1054,"data":1055,"type":218,"tunes":1057},"XT6-WYF016",{"text":1056},"Ovaj tok rada ne zahteva da se svaki zadatak ponaša kao laboratorijska nauka.",{},{"id":1059,"data":1060,"type":218,"tunes":1062},"__sqUEl7ab",{"text":1061},"Umesto toga, on izvlači opšti epistemički princip iz falsifikacije: objašnjenja treba izložiti uslovima pod kojima mogu da izgube.",{},{"id":1064,"data":1065,"type":218,"tunes":1067},"09ZLp4dPeR",{"text":1066},"Tačan validator se zatim menja u zavisnosti od domena.",{},{"id":1069,"data":1070,"type":218,"tunes":1072},"1_PAOT4qnJ",{"text":1071},"Istorijska analiza testira hronologiju, poreklo i prenošenje. Otklanjanje grešaka testira uočljivo ponašanje sistema. Arhitektura testira zahteve i ograničenja. Strategija testira tržišne pretpostavke i unapred definisane kriterijume neuspeha.",{},{"id":1074,"data":1075,"type":218,"tunes":1077},"zF9aypLOBr",{"text":1076},"Ovo jezgro nezavisno od domena i njegovi validatori specifični za domen dalje su razvijeni u \u003Ca href=\"{{STAJIC_ARTICLE_5_URL}}\">Od istraživačkog protokola do opšteg okvira za AI rezonovanje\u003C\u002Fa>.",{},{"id":1079,"data":1080,"type":42,"tunes":1082},"6kpgYe1r-V",{"text":1081,"level":252},"Šta falsifikacija ne može da uradi",{},{"id":1084,"data":1085,"type":485,"tunes":1096},"m-43Zwil8y",{"meta":1086,"items":1087,"style":484},{},[1088,1089,1090,1091,1092,1093,1094,1095],"Ne može da učini nepotpune dokaze potpunim.","Ne može da garantuje da je generisana ispravna alternativna hipoteza.","Ne može da eliminiše greške koje dele model, izvori i proces evaluacije.","Ne može da pretvori suštinski interpretativne tvrdnje u laboratorijske eksperimente.","Ne može da tretira svako nedostajuće opažanje kao dokaz protiv hipoteze.","Ne može automatski da identifikuje koja pomoćna pretpostavka nije uspela kada je predviđanje opovrgnuto.","Ne može da dokaže da je hipoteza koja je opstala istinita.","Ne može da zameni eksperimente, primarne izvore, ekspertizu u domenu ili empirijsko merenje tamo gde su oni potrebni.",{},{"id":1098,"data":1099,"type":218,"tunes":1101},"UjMN48p74y",{"text":1100},"Hipoteza koja preživi ponovljene pokušaje falsifikacije bolje se opisuje kao \u003Cb>potkrepljena izvršenim testovima\u003C\u002Fb> nego kao dokazana.",{},{"id":1103,"data":1104,"type":42,"tunes":1106},"lnnOeezITl",{"text":1105,"level":252},"Centralni princip",{},{"id":1108,"data":1109,"type":218,"tunes":1111},"3bc-laue9s",{"text":1110},"Generativna AI čini potvrđivanje jeftinim.",{},{"id":1113,"data":1114,"type":218,"tunes":1116},"CkrzUk8My9",{"text":1115},"Za dovoljno verodostojnu tvrdnju, moćan jezički model obično može da proizvede argumente, analogije, potkrepljujuće činjenice i koherentne narative oko nje.",{},{"id":1118,"data":1119,"type":218,"tunes":1121},"uCI626CQ5O",{"text":1120},"Upravo zato potvrda ne bi trebalo da bude konačni test.",{},{"id":1123,"data":1124,"type":236,"tunes":1126},"HX5lhiRtbZ",{"text":1125,"caption":234,"alignment":235},"Pouzdan proces zaključivanja ne bi trebalo da pita samo zašto bi hipoteza mogla biti tačna. On mora takođe da definiše kako bi hipoteza mogla biti pogrešna.",{},{"id":1128,"data":1129,"type":218,"tunes":1131},"m3fGXOx6r4",{"text":1130},"Kvalitet zaključka AI stoga ne zavisi samo od toga koliko dokaza koji ga podržavaju sistem može da pronađe, već i od toga da li je konkurentskim objašnjenjima dozvoljeno da pobede.",{},{"id":1133,"data":1134,"type":218,"tunes":1136},"lCJmcFOveh",{"text":1135},"To menja ulogu modela.",{},{"id":1138,"data":1139,"type":218,"tunes":1141},"b7gsUJe7yN",{"text":1140},"On više nije samo generator odgovora.",{},{"id":1143,"data":1144,"type":218,"tunes":1146},"yuOWgcdN7m",{"text":1145},"On postaje učesnik u kontrolisanom procesu u kojem je njegovo sopstveno prvo objašnjenje privremeno, proverljivo i zamenljivo.",{},{"id":1148,"data":1149,"type":236,"tunes":1151},"MawChI1QNz",{"text":1150,"caption":234,"alignment":235},"Najjači AI odgovor nije onaj sa najviše argumenata koji ga podržavaju. To je onaj čijim je najjačim alternativama data poštena prilika da ga pobede.",{},{"id":1153,"data":1154,"type":1155,"tunes":1156},"4rMuhiZkDJ",{},"delimiter",{},{"id":1158,"data":1159,"type":42,"tunes":1161},"1jcICUuEQb",{"text":1160,"level":252},"Kontekst istraživanja",{},{"id":1163,"data":1164,"type":218,"tunes":1166},"y_d8vf02x9",{"text":1165},"Metodologija u ovom članku prilagođava ideje iz filozofije nauke i savremenih empirijskih istraživanja zaključivanja jezičkih modela. Karl Popper je svojim falsifikacionizmom naglasio da naučne tvrdnje treba da se izlože mogućim zapažanjima koja su sa njima u sukobu, dok je kasnija filozofija nauke jasno pokazala da je praktična falsifikacija složenija od jednostavnog odbacivanja hipoteze nakon jednog anomalnog zapažanja.",{},{"id":1168,"data":1169,"type":218,"tunes":1171},"GaSQemV7d0",{"text":1170},"Razlika je važna za AI sisteme jer stvarni testovi obično zavise od pomoćnih pretpostavki, kvaliteta dokaza i interpretacije. Zaključivanje AI usmereno na falsifikaciju stoga koristi logiku opovrgavanja, a da pritom ne pretpostavlja da se svaka složena analitička tvrdnja može svesti na jedan odlučujući eksperiment.",{},{"id":1173,"data":1174,"type":218,"tunes":1176},"NJd9wCH4vo",{"text":1175},"Studija iz 2026. koju su sproveli Jhaveri, GX-Chen, Sucholutsky i Choi pruža direktnu empirijsku motivaciju za ovaj dizajn. Na jedanaest LLM-ova, autori su otkrili istraživanje hipoteza pristrasno ka potvrđivanju u interaktivnom zadatku otkrivanja pravila. Podsticanje modela da razmotre kontraprimere dosledno je smanjilo ovu pristrasnost i povećalo prosečnu stopu otkrivanja pravila sa 42% na 56%.",{},{"id":1178,"data":1179,"type":218,"tunes":1181},"Rf7LMywSAW",{"text":1180},"Ovi nalazi ne pokazuju da je kompletna metodologija predložena ovde eksperimentalno validirana kao jedinstveni okvir. Oni podržavaju užu i važnu tvrdnju: eksplicitne intervencije ka opovrgavajućim dokazima mogu poboljšati istraživanje hipoteza kod LLM-ova.",{},{"id":1183,"data":1184,"type":42,"tunes":1187},"rhXl_6qblP",{"text":1185,"level":1186},"Odabrane reference",3,{},{"id":1189,"data":1190,"type":485,"tunes":1198},"B1isuRcUNx",{"meta":1191,"items":1192,"style":484},{},[1193,1194,1195,1196,1197],"Popper, K. R. — \u003Ci>The Logic of Scientific Discovery\u003C\u002Fi>. Englesko izdanje, 1959.","Popper, K. R. — \u003Ci>Conjectures and Refutations: The Growth of Scientific Knowledge\u003C\u002Fi>. 1963.","Stanford Encyclopedia of Philosophy — \u003Ci>Scientific Method\u003C\u002Fi>, odeljci o hipotetičko-deduktivnom testiranju i falsifikacionizmu.","Stanford Encyclopedia of Philosophy — \u003Ci>Karl Popper\u003C\u002Fi>, rasprava o osnovnim iskazima, falsifikabilnosti i praktičnim komplikacijama falsifikacije.","Jhaveri, A. R., GX-Chen, A., Sucholutsky, I. & Choi, E. — \u003Ci>Failing to Falsify: Evaluating and Mitigating Confirmation Bias in Language Models\u003C\u002Fi>. arXiv:2604.02485, 2026.",{},{"id":1200,"data":1201,"type":1155,"tunes":1202},"eg9056YrEd",{},{},{"id":1204,"data":1205,"type":42,"tunes":1207},"AKTL4fFXB0",{"text":1206,"level":252},"Nastavite seriju",{},{"id":1209,"data":1210,"type":485,"tunes":1217},"PZVMS25Pe9",{"meta":1211,"items":1212,"style":484},{},[1213,1214,1215,1216],"\u003Cb>\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>\u003C\u002Fb> — kompletan metodološki okvir.","\u003Cb>\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\u002Fb> — zašto okvir i pretpostavke korisnika mogu uticati na AI zaključivanje.","\u003Cb>\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\u002Fb> — testiranje da li zaključci preživljavaju slepe, obrnute i suprotstavljene okvire.","\u003Cb>\u003Ca href=\"https:\u002F\u002Fstajic.de\u002Fsr\u002Fblog\u002Ffrom-research-protocol-to-a-general-ai-reasoning-framework\">From Research Protocol to General AI Reasoning Framework\u003C\u002Fa>\u003C\u002Fb> — pretvaranje metodologije u proces zaključivanja nezavisan od domena.",{},"2.31","AI modeli mogu da generišu ubedljive dokaze za skoro svaku plausibilnu hipotezu. Pouzdanija metodologija postavlja suprotno pitanje: koji bi dokazi oslabili, opovrgli ili nas naterali da napustimo zaključak? Ovaj članak razvija rasuđivanje usmereno na falsifikaciju za LLM-ove koristeći konkurentske hipoteze, diskriminišuće testove, protivdokaze i eksplicitne kriterijume odbacivanja.","\u002Fuploads\u002F2026\u002F09\u002Ffalsification-for-ai-reasoning-from-answers-to-tested-hypotheses-1789811137616-3hce1b.webp","falsification-for-ai-reasoning-from-answers-to-tested-hypotheses-1789811137616-3hce1b","PUBLISHED","2026-09-19T01:11:00.000Z","2026-09-19T07:11:50.436Z","2026-09-19T09:54:17.148Z",{"en":1227,"de":1228,"sr":1229,"es":1230,"fr":1231,"it":1232,"ru":1233,"zh":1234},"\u002Fblog\u002Ffalsification-for-ai-reasoning-from-answers-to-tested-hypotheses","\u002Fde\u002Fblog\u002Ffalsification-for-ai-reasoning-from-answers-to-tested-hypotheses","\u002Fsr\u002Fblog\u002Ffalsification-for-ai-reasoning-from-answers-to-tested-hypotheses","\u002Fes\u002Fblog\u002Ffalsification-for-ai-reasoning-from-answers-to-tested-hypotheses","\u002Ffr\u002Fblog\u002Ffalsification-for-ai-reasoning-from-answers-to-tested-hypotheses","\u002Fit\u002Fblog\u002Ffalsification-for-ai-reasoning-from-answers-to-tested-hypotheses","\u002Fru\u002Fblog\u002Ffalsification-for-ai-reasoning-from-answers-to-tested-hypotheses","\u002Fzh\u002Fblog\u002Ffalsification-for-ai-reasoning-from-answers-to-tested-hypotheses",[],{"id":1237,"login":1238,"email":234,"displayName":1239},"20","rooth8233","Aleksandar Stajić",[1241,2047],{"lang":1242,"title":1243,"content":1244,"contentJson":1245,"excerpt":2046},"en","Falsification for AI Reasoning: From Answers to Tested Hypotheses","{\"time\":1789811247069,\"blocks\":[{\"id\":\"i02QyfgW6t\",\"type\":\"paragraph\",\"data\":{\"text\":\"A language model can produce supporting evidence for a surprisingly large number of plausible explanations.\"},\"tunes\":{}},{\"id\":\"67IgneVFBd\",\"type\":\"paragraph\",\"data\":{\"text\":\"That ability is useful for exploration, but dangerous as a validation method. If the model begins with a hypothesis and the task is simply to explain why it might be correct, a coherent answer can emerge long before the hypothesis has survived a serious test.\"},\"tunes\":{}},{\"id\":\"167Yo_p807\",\"type\":\"paragraph\",\"data\":{\"text\":\"Reliable AI reasoning therefore requires a stronger question:\"},\"tunes\":{}},{\"id\":\"Jv6rg5BSPG\",\"type\":\"quote\",\"data\":{\"text\":\"What evidence would make this hypothesis fail?\",\"caption\":\"\",\"alignment\":\"left\"},\"tunes\":{}},{\"id\":\"NiwGy1wCDc\",\"type\":\"paragraph\",\"data\":{\"text\":\"This article develops falsification-oriented reasoning as the next layer of the methodology introduced in \u003Ca href=\\\"{{STAJIC_ARTICLE_1_URL}}\\\">Beyond Prompt Engineering: A Methodology for More Reliable AI Reasoning\u003C\u002Fa>. The previous article, \u003Ca href=\\\"{{STAJIC_ARTICLE_3_URL}}\\\">Prompt Invariance: Does the Conclusion Survive the Prompt?\u003C\u002Fa>, tests whether a conclusion survives changes in framing. Falsification addresses a different problem: whether the hypothesis survives evidence that could count against it.\"},\"tunes\":{}},{\"id\":\"VQxKWrgVxK\",\"type\":\"quote\",\"data\":{\"text\":\"Prompt Invariance tests dependence on framing. Falsification tests vulnerability to evidence.\",\"caption\":\"\",\"alignment\":\"left\"},\"tunes\":{}},{\"id\":\"JqaeAPhxbk\",\"type\":\"header\",\"data\":{\"text\":\"Why Confirmation Is Too Easy\",\"level\":2},\"tunes\":{}},{\"id\":\"lKD9JfgkoH\",\"type\":\"paragraph\",\"data\":{\"text\":\"Suppose an AI system is given a hypothesis and asked to determine whether it is plausible.\"},\"tunes\":{}},{\"id\":\"5sJsr6TXCH\",\"type\":\"paragraph\",\"data\":{\"text\":\"The model can search its knowledge, supplied documents or retrieved sources for observations compatible with that hypothesis. If enough compatible observations are found, the resulting explanation may become increasingly persuasive.\"},\"tunes\":{}},{\"id\":\"XDmsbhLaOL\",\"type\":\"paragraph\",\"data\":{\"text\":\"But compatibility is weak evidence when several competing hypotheses predict the same observation.\"},\"tunes\":{}},{\"id\":\"W-PqvKBX2Y\",\"type\":\"paragraph\",\"data\":{\"text\":\"Consider an abstract case:\"},\"tunes\":{}},{\"id\":\"r4irpPsK3Z\",\"type\":\"quote\",\"data\":{\"text\":\"Observation E is compatible with hypothesis H1.\",\"caption\":\"\",\"alignment\":\"left\"},\"tunes\":{}},{\"id\":\"c5OGlvsYEo\",\"type\":\"paragraph\",\"data\":{\"text\":\"That statement alone does not establish H1. If H2, H3 and H4 also predict E, then E does little to distinguish among them.\"},\"tunes\":{}},{\"id\":\"3k2YJOSEw2\",\"type\":\"quote\",\"data\":{\"text\":\"Evidence becomes more informative when competing explanations make different predictions about what we should observe.\",\"caption\":\"\",\"alignment\":\"left\"},\"tunes\":{}},{\"id\":\"CCu-NbuXmW\",\"type\":\"paragraph\",\"data\":{\"text\":\"This shifts the reasoning process from collecting supportive facts toward designing discriminating tests.\"},\"tunes\":{}},{\"id\":\"SoStpl-vwv\",\"type\":\"header\",\"data\":{\"text\":\"The Classical Idea Behind Falsification\",\"level\":2},\"tunes\":{}},{\"id\":\"jId-oX1tg3\",\"type\":\"paragraph\",\"data\":{\"text\":\"Karl Popper's falsificationism emphasized an asymmetry between verification and refutation. Repeated observations compatible with a universal claim cannot logically prove that claim true, while a genuine incompatible observation can conflict directly with it.\"},\"tunes\":{}},{\"id\":\"cifut7fdtF\",\"type\":\"paragraph\",\"data\":{\"text\":\"In simplified logical form:\"},\"tunes\":{}},{\"id\":\"yJb9ANIBq_\",\"type\":\"quote\",\"data\":{\"text\":\"If H is true, observation O should occur. O does not occur. Therefore H, as stated under the test conditions, is challenged.\",\"caption\":\"\",\"alignment\":\"left\"},\"tunes\":{}},{\"id\":\"2d7LhSjBst\",\"type\":\"paragraph\",\"data\":{\"text\":\"The strength of this approach lies not in proving theories false at every opportunity, but in requiring claims to expose themselves to possible failure.\"},\"tunes\":{}},{\"id\":\"kSTwb_cUlk\",\"type\":\"paragraph\",\"data\":{\"text\":\"A hypothesis that can accommodate every conceivable result without changing is difficult to test because no observation meaningfully counts against it.\"},\"tunes\":{}},{\"id\":\"xgSvZ2PHwL\",\"type\":\"quote\",\"data\":{\"text\":\"A useful hypothesis should take a risk: some possible evidence must be less compatible with it than with its alternatives.\",\"caption\":\"\",\"alignment\":\"left\"},\"tunes\":{}},{\"id\":\"gvC5oqbfST\",\"type\":\"header\",\"data\":{\"text\":\"Falsification Is More Complicated in Practice\",\"level\":2},\"tunes\":{}},{\"id\":\"s69877zNlb\",\"type\":\"paragraph\",\"data\":{\"text\":\"The popular version of falsification is often too simple: one contradictory observation appears, therefore the hypothesis is immediately false.\"},\"tunes\":{}},{\"id\":\"iCd3z7e_yJ\",\"type\":\"paragraph\",\"data\":{\"text\":\"Real investigations rarely work that cleanly.\"},\"tunes\":{}},{\"id\":\"4aa_Y4Miwj\",\"type\":\"paragraph\",\"data\":{\"text\":\"A prediction usually depends not only on the central hypothesis but also on auxiliary assumptions: measurement accuracy, source reliability, environmental conditions, implementation details, background theories or the completeness of the available evidence.\"},\"tunes\":{}},{\"id\":\"wNC4zca5ig\",\"type\":\"paragraph\",\"data\":{\"text\":\"A more realistic logical structure is:\"},\"tunes\":{}},{\"id\":\"w9qV9-x03c\",\"type\":\"quote\",\"data\":{\"text\":\"H + A1 + A2 + A3 → expected observation O\",\"caption\":\"\",\"alignment\":\"left\"},\"tunes\":{}},{\"id\":\"IfZbepy0sG\",\"type\":\"paragraph\",\"data\":{\"text\":\"If O is not observed, the failed prediction tells us that something in the complete explanatory structure is wrong. It does not automatically tell us which component failed.\"},\"tunes\":{}},{\"id\":\"2oDpL0yId4\",\"type\":\"paragraph\",\"data\":{\"text\":\"The hypothesis may be wrong. The measurement may be unreliable. An assumed condition may not have been satisfied. A source may be incomplete. The model may have misunderstood the evidence.\"},\"tunes\":{}},{\"id\":\"2I16bMuYeb\",\"type\":\"paragraph\",\"data\":{\"text\":\"This distinction is crucial for AI reasoning. The goal is not naïve rejection. The goal is systematic exposure of hypotheses to evidence capable of weakening them.\"},\"tunes\":{}},{\"id\":\"kGTeE6jRa1\",\"type\":\"header\",\"data\":{\"text\":\"Falsification-Oriented Reasoning for LLMs\",\"level\":2},\"tunes\":{}},{\"id\":\"aWVwveQXLj\",\"type\":\"paragraph\",\"data\":{\"text\":\"Strict Popperian falsification applies most naturally to claims that generate clearly testable consequences. Many tasks handled by AI assistants are less clean: historical interpretation, debugging, architecture, strategy and causal analysis often involve incomplete evidence and probabilistic explanations.\"},\"tunes\":{}},{\"id\":\"OzhnuXxG3m\",\"type\":\"paragraph\",\"data\":{\"text\":\"For those domains, the more general term \u003Cb>falsification-oriented reasoning\u003C\u002Fb> is useful.\"},\"tunes\":{}},{\"id\":\"tmWDhTYpw1\",\"type\":\"paragraph\",\"data\":{\"text\":\"The objective is not necessarily to obtain a single logically decisive falsifier. It is to organize the analysis so that evidence capable of contradicting, weakening or discriminating against the current hypothesis receives explicit attention.\"},\"tunes\":{}},{\"id\":\"Qo3NPH1q3j\",\"type\":\"quote\",\"data\":{\"text\":\"Do not merely ask what supports H. Ask what should exist if H is true, what should be difficult to explain if H is true, and what alternative explains the same evidence better.\",\"caption\":\"\",\"alignment\":\"left\"},\"tunes\":{}},{\"id\":\"fckuCMmX6d\",\"type\":\"header\",\"data\":{\"text\":\"Step 1 — State the Hypothesis Precisely\",\"level\":2},\"tunes\":{}},{\"id\":\"JluAFx81nA\",\"type\":\"paragraph\",\"data\":{\"text\":\"A hypothesis cannot be meaningfully tested if it is vague enough to absorb every outcome.\"},\"tunes\":{}},{\"id\":\"e2OMHHe4Nf\",\"type\":\"paragraph\",\"data\":{\"text\":\"Compare:\"},\"tunes\":{}},{\"id\":\"0PtMJwVhsb\",\"type\":\"quote\",\"data\":{\"text\":\"The system is unstable because something in the network layer is wrong.\",\"caption\":\"Weak hypothesis\",\"alignment\":\"left\"},\"tunes\":{}},{\"id\":\"We3dkWldqK\",\"type\":\"paragraph\",\"data\":{\"text\":\"with:\"},\"tunes\":{}},{\"id\":\"TYnOciItVj\",\"type\":\"quote\",\"data\":{\"text\":\"The intermittent API failures are caused by the reverse proxy closing upstream connections when the configured timeout is exceeded.\",\"caption\":\"Testable hypothesis\",\"alignment\":\"left\"},\"tunes\":{}},{\"id\":\"JQ7E5dA4Ha\",\"type\":\"paragraph\",\"data\":{\"text\":\"The second claim exposes itself to concrete tests. We can inspect timeout values, connection duration, proxy logs, upstream behaviour and failures occurring below the configured threshold.\"},\"tunes\":{}},{\"id\":\"FXtEDznyAh\",\"type\":\"paragraph\",\"data\":{\"text\":\"The more precisely a hypothesis defines the claimed relationship, the easier it becomes to determine what evidence would count against it.\"},\"tunes\":{}},{\"id\":\"9rpdQoi2vw\",\"type\":\"header\",\"data\":{\"text\":\"Step 2 — Generate Genuine Competing Hypotheses\",\"level\":2},\"tunes\":{}},{\"id\":\"rFmosy87ye\",\"type\":\"paragraph\",\"data\":{\"text\":\"Testing one hypothesis in isolation is weak because almost every observation is interpreted relative to something else.\"},\"tunes\":{}},{\"id\":\"O-91zwQt8x\",\"type\":\"paragraph\",\"data\":{\"text\":\"The system should therefore construct credible alternatives before evaluating the evidence.\"},\"tunes\":{}},{\"id\":\"_epkMsKypP\",\"type\":\"paragraph\",\"data\":{\"text\":\"For the same API failure, candidate explanations might include:\"},\"tunes\":{}},{\"id\":\"w5kp-by4bv\",\"type\":\"list\",\"data\":{\"style\":\"unordered\",\"meta\":{},\"items\":[\"\u003Cb>H1:\u003C\u002Fb> reverse-proxy timeout;\",\"\u003Cb>H2:\u003C\u002Fb> upstream application crash or restart;\",\"\u003Cb>H3:\u003C\u002Fb> database connection exhaustion;\",\"\u003Cb>H4:\u003C\u002Fb> network interruption or packet loss;\",\"\u003Cb>H5:\u003C\u002Fb> client-side timeout;\",\"\u003Cb>H6:\u003C\u002Fb> interaction between several layers rather than one isolated cause.\"]},\"tunes\":{}},{\"id\":\"-b5SlkifJB\",\"type\":\"paragraph\",\"data\":{\"text\":\"The alternatives must be plausible enough to compete. Generating obviously inferior alternatives merely creates the appearance of critical reasoning.\"},\"tunes\":{}},{\"id\":\"uq-fd--TLQ\",\"type\":\"quote\",\"data\":{\"text\":\"A hypothesis has not survived competition if the alternatives were designed to lose.\",\"caption\":\"\",\"alignment\":\"left\"},\"tunes\":{}},{\"id\":\"HYAdyOKBw6\",\"type\":\"header\",\"data\":{\"text\":\"Step 3 — Derive Expected Observations\",\"level\":2},\"tunes\":{}},{\"id\":\"zPNz32Lu3K\",\"type\":\"paragraph\",\"data\":{\"text\":\"For each serious hypothesis, the model should derive observations expected under that explanation.\"},\"tunes\":{}},{\"id\":\"liNj49Sano\",\"type\":\"paragraph\",\"data\":{\"text\":\"If H1 is the reverse-proxy timeout hypothesis, expected observations could include failures clustered around a specific duration, corresponding timeout messages in proxy logs, healthy upstream processes at the time of failure and disappearance of the failure after a controlled timeout change.\"},\"tunes\":{}},{\"id\":\"Cme39nLtEl\",\"type\":\"paragraph\",\"data\":{\"text\":\"For H2, an application restart hypothesis, we would expect a different pattern: process restarts, exceptions, missing application availability, resource exhaustion or correlated container events.\"},\"tunes\":{}},{\"id\":\"gs6yDl-O9i\",\"type\":\"paragraph\",\"data\":{\"text\":\"The important step is to derive these expectations \u003Cb>before\u003C\u002Fb> interpreting every available observation as support.\"},\"tunes\":{}},{\"id\":\"5JooacBK3t\",\"type\":\"header\",\"data\":{\"text\":\"Step 4 — Define Potential Disconfirming Evidence\",\"level\":2},\"tunes\":{}},{\"id\":\"4TVRQ0Fe7o\",\"type\":\"paragraph\",\"data\":{\"text\":\"For each hypothesis, ask what evidence would materially weaken it.\"},\"tunes\":{}},{\"id\":\"mRYP6ANWRE\",\"type\":\"quote\",\"data\":{\"text\":\"What would we expect not to observe if this hypothesis were the main explanation?\",\"caption\":\"\",\"alignment\":\"left\"},\"tunes\":{}},{\"id\":\"YZG2Ij_nxm\",\"type\":\"paragraph\",\"data\":{\"text\":\"For the proxy-timeout hypothesis, examples include failures occurring far below the configured threshold, identical failures when bypassing the proxy, no relevant proxy-side event, or evidence that the upstream application terminates the connection first.\"},\"tunes\":{}},{\"id\":\"_MoPXmNQWQ\",\"type\":\"paragraph\",\"data\":{\"text\":\"This changes the model's search objective.\"},\"tunes\":{}},{\"id\":\"LxF512uQwp\",\"type\":\"quote\",\"data\":{\"text\":\"Confirmation search: Find evidence compatible with H1. Disconfirmation search: Find observations that H1 predicts poorly or that H2 predicts substantially better.\",\"caption\":\"\",\"alignment\":\"left\"},\"tunes\":{}},{\"id\":\"gAalT3qoTw\",\"type\":\"header\",\"data\":{\"text\":\"Step 5 — Prefer Discriminating Tests\",\"level\":2},\"tunes\":{}},{\"id\":\"dfY5nrMkxJ\",\"type\":\"paragraph\",\"data\":{\"text\":\"Not every test is equally informative.\"},\"tunes\":{}},{\"id\":\"D5Hc-vzXbt\",\"type\":\"paragraph\",\"data\":{\"text\":\"Suppose H1 and H2 both predict elevated error rates. Observing another error provides little discrimination.\"},\"tunes\":{}},{\"id\":\"87X08XjEQZ\",\"type\":\"paragraph\",\"data\":{\"text\":\"A better test searches for an observation on which their predictions diverge.\"},\"tunes\":{}},{\"id\":\"-8M3bAYrvX\",\"type\":\"quote\",\"data\":{\"text\":\"Good test: an observation likely under H1 but unlikely under H2, or vice versa.\",\"caption\":\"\",\"alignment\":\"left\"},\"tunes\":{}},{\"id\":\"ElUma_dnFK\",\"type\":\"paragraph\",\"data\":{\"text\":\"In debugging, bypassing the suspected proxy may be discriminating. In historical research, demonstrating chronology that makes direct transmission impossible can be strongly discriminating. In product analysis, observing the same demand decline in a control market unaffected by the proposed cause can weaken a causal explanation.\"},\"tunes\":{}},{\"id\":\"aMsCv48OJa\",\"type\":\"paragraph\",\"data\":{\"text\":\"The methodology therefore values evidence not only by reliability but also by its ability to distinguish among competing explanations.\"},\"tunes\":{}},{\"id\":\"QQ4Th-nwqA\",\"type\":\"header\",\"data\":{\"text\":\"Step 6 — Actively Search for Counter-Evidence\",\"level\":2},\"tunes\":{}},{\"id\":\"ahSEkuIa-7\",\"type\":\"paragraph\",\"data\":{\"text\":\"Once potential falsifiers and discriminating observations are defined, the system should actively search for them.\"},\"tunes\":{}},{\"id\":\"U8HkPTs6X-\",\"type\":\"paragraph\",\"data\":{\"text\":\"This requirement matters because language models themselves can exhibit confirmation-biased hypothesis testing.\"},\"tunes\":{}},{\"id\":\"WEm5yhr_7P\",\"type\":\"paragraph\",\"data\":{\"text\":\"In 2026, Jhaveri, GX-Chen, Sucholutsky and Choi adapted a classic rule-discovery task to eleven language models from multiple model families and scales. Models frequently proposed examples that would confirm their current rule rather than examples designed to falsify it.\"},\"tunes\":{}},{\"id\":\"zXM8383nSR\",\"type\":\"paragraph\",\"data\":{\"text\":\"The consequence was practical: confirmation-oriented exploration produced slower and less successful discovery of the hidden rule.\"},\"tunes\":{}},{\"id\":\"eHdYtwyjnH\",\"type\":\"paragraph\",\"data\":{\"text\":\"When the researchers explicitly encouraged consideration of counterexamples, average rule-discovery success increased from 42% to 56% in the reported experiments.\"},\"tunes\":{}},{\"id\":\"eifCoJKlFl\",\"type\":\"quote\",\"data\":{\"text\":\"The model did not need a new knowledge base. It needed a better hypothesis-testing strategy.\",\"caption\":\"\",\"alignment\":\"left\"},\"tunes\":{}},{\"id\":\"ww3-vggbJb\",\"type\":\"paragraph\",\"data\":{\"text\":\"This is directly relevant to the broader methodology: reasoning quality can improve when the inference process changes from confirmation-seeking to falsification-oriented exploration.\"},\"tunes\":{}},{\"id\":\"T2Bj2B59tj\",\"type\":\"header\",\"data\":{\"text\":\"Step 7 — Separate Contradiction from Rejection\",\"level\":2},\"tunes\":{}},{\"id\":\"fAdASfms4W\",\"type\":\"paragraph\",\"data\":{\"text\":\"Finding evidence against a hypothesis does not always justify immediate rejection.\"},\"tunes\":{}},{\"id\":\"6bdAMQedEe\",\"type\":\"paragraph\",\"data\":{\"text\":\"The system should first evaluate the quality of the counter-evidence:\"},\"tunes\":{}},{\"id\":\"QFsFbZAgsF\",\"type\":\"list\",\"data\":{\"style\":\"unordered\",\"meta\":{},\"items\":[\"Is the observation reliable?\",\"Is the source primary or indirect?\",\"Could measurement or retrieval error explain the conflict?\",\"Does the hypothesis actually predict the disputed observation?\",\"Does the contradiction depend on an auxiliary assumption?\",\"Is the counter-evidence independently corroborated?\",\"Does a competing hypothesis explain the evidence more successfully?\"]},\"tunes\":{}},{\"id\":\"cQeyIvJfKt\",\"type\":\"paragraph\",\"data\":{\"text\":\"Only after this evaluation should the model determine whether the hypothesis is weakened, substantially revised or rejected.\"},\"tunes\":{}},{\"id\":\"uniCBTmcCp\",\"type\":\"header\",\"data\":{\"text\":\"Step 8 — Prevent Ad Hoc Rescue\",\"level\":2},\"tunes\":{}},{\"id\":\"ktukb4T7sy\",\"type\":\"paragraph\",\"data\":{\"text\":\"A hypothesis can become effectively unfalsifiable if every contradictory observation produces a new exception.\"},\"tunes\":{}},{\"id\":\"OX1R7u-I9n\",\"type\":\"paragraph\",\"data\":{\"text\":\"The pattern looks like this:\"},\"tunes\":{}},{\"id\":\"fiyn_CdbEe\",\"type\":\"quote\",\"data\":{\"text\":\"Prediction fails → add exception → prediction fails again → add another exception → retain original conclusion indefinitely\",\"caption\":\"\",\"alignment\":\"left\"},\"tunes\":{}},{\"id\":\"u7tc5RfwKs\",\"type\":\"paragraph\",\"data\":{\"text\":\"Not every modification is illegitimate. Scientific and technical progress frequently occurs because unexpected evidence reveals a missing variable or incorrect auxiliary assumption.\"},\"tunes\":{}},{\"id\":\"QYchvE4e-r\",\"type\":\"paragraph\",\"data\":{\"text\":\"The methodological distinction is whether the revision creates new testable consequences or merely protects the preferred conclusion from failure.\"},\"tunes\":{}},{\"id\":\"AZiFmZekHx\",\"type\":\"quote\",\"data\":{\"text\":\"A productive revision increases explanatory and predictive precision. An ad hoc rescue only decreases the chance that the hypothesis can ever lose.\",\"caption\":\"\",\"alignment\":\"left\"},\"tunes\":{}},{\"id\":\"vv0xWnfmOB\",\"type\":\"header\",\"data\":{\"text\":\"Step 9 — Update Confidence Instead of Defending the Initial Answer\",\"level\":2},\"tunes\":{}},{\"id\":\"uAWov1akKH\",\"type\":\"paragraph\",\"data\":{\"text\":\"The output of falsification-oriented reasoning does not have to be binary.\"},\"tunes\":{}},{\"id\":\"xVJwRBl45g\",\"type\":\"paragraph\",\"data\":{\"text\":\"Possible states include:\"},\"tunes\":{}},{\"id\":\"x5-wIlzXG1\",\"type\":\"list\",\"data\":{\"style\":\"unordered\",\"meta\":{},\"items\":[\"\u003Cb>Strongly supported:\u003C\u002Fb> survives serious discriminating tests and competing explanations perform substantially worse.\",\"\u003Cb>Provisionally supported:\u003C\u002Fb> best available explanation, but important uncertainties remain.\",\"\u003Cb>Weakened:\u003C\u002Fb> material counter-evidence exists but is not decisive.\",\"\u003Cb>Underdetermined:\u003C\u002Fb> several hypotheses explain the current evidence similarly well.\",\"\u003Cb>Rejected:\u003C\u002Fb> reliable evidence conflicts with a central prediction and alternative explanations perform better.\",\"\u003Cb>Not testable with available evidence:\u003C\u002Fb> the current corpus cannot meaningfully discriminate among the claims.\"]},\"tunes\":{}},{\"id\":\"7sV9rnMWz4\",\"type\":\"paragraph\",\"data\":{\"text\":\"The central rule is simple: confidence should follow the result of the tests rather than the model's rhetorical investment in its first answer.\"},\"tunes\":{}},{\"id\":\"BH5ZZ1JFol\",\"type\":\"header\",\"data\":{\"text\":\"A Falsification Matrix\",\"level\":2},\"tunes\":{}},{\"id\":\"ryK4KQRrjy\",\"type\":\"paragraph\",\"data\":{\"text\":\"For complex analysis, hypotheses can be normalized into a comparison matrix.\"},\"tunes\":{}},{\"id\":\"Q3XRo3fg6I\",\"type\":\"table\",\"data\":{\"withHeadings\":true,\"stretched\":false,\"content\":[[\"Dimension\",\"H1\",\"H2\",\"H3\"],[\"Core claim\",\"Define precisely\",\"Define precisely\",\"Define precisely\"],[\"Expected evidence\",\"List predictions\",\"List predictions\",\"List predictions\"],[\"Potential counter-evidence\",\"Define\",\"Define\",\"Define\"],[\"Discriminating test\",\"Specify\",\"Specify\",\"Specify\"],[\"Supporting observations\",\"Record\",\"Record\",\"Record\"],[\"Contradictory observations\",\"Record\",\"Record\",\"Record\"],[\"Auxiliary assumptions\",\"Expose\",\"Expose\",\"Expose\"],[\"Current status\",\"Reassess\",\"Reassess\",\"Reassess\"]]},\"tunes\":{}},{\"id\":\"YCn6rUs0hW\",\"type\":\"paragraph\",\"data\":{\"text\":\"The matrix prevents a common failure mode: applying rigorous scrutiny to alternatives while allowing the preferred hypothesis to remain vague.\"},\"tunes\":{}},{\"id\":\"BQg7UmiVg2\",\"type\":\"header\",\"data\":{\"text\":\"Negative Evidence Requires Special Care\",\"level\":2},\"tunes\":{}},{\"id\":\"2tC0EX6EJi\",\"type\":\"paragraph\",\"data\":{\"text\":\"Absence of expected evidence can weaken a hypothesis, but only under specific conditions.\"},\"tunes\":{}},{\"id\":\"KtDzLct2Ef\",\"type\":\"paragraph\",\"data\":{\"text\":\"The statement 'we found no evidence for X' is not equivalent to 'X did not occur'.\"},\"tunes\":{}},{\"id\":\"XuucSV3LV4\",\"type\":\"paragraph\",\"data\":{\"text\":\"Negative evidence becomes informative when there is a justified expectation that the evidence would probably be observable, preserved, logged, documented or measurable if the hypothesis were true.\"},\"tunes\":{}},{\"id\":\"tThM65Pyb2\",\"type\":\"quote\",\"data\":{\"text\":\"Absence of evidence matters most when evidence should not be absent.\",\"caption\":\"\",\"alignment\":\"left\"},\"tunes\":{}},{\"id\":\"7XW_PhILzD\",\"type\":\"paragraph\",\"data\":{\"text\":\"In debugging, the absence of a required log event can be meaningful if logging is known to be complete. In historical research, absence from a fragmentary archive is usually much weaker. In security analysis, absence of an alert has little value if the relevant telemetry was never collected.\"},\"tunes\":{}},{\"id\":\"cNn_H3lw4E\",\"type\":\"paragraph\",\"data\":{\"text\":\"The model must therefore evaluate both the missing evidence and the probability that such evidence would have survived or been observable.\"},\"tunes\":{}},{\"id\":\"nSt2auDic3\",\"type\":\"header\",\"data\":{\"text\":\"Historical Research: Transmission Versus Similarity\",\"level\":2},\"tunes\":{}},{\"id\":\"7ktuWOnwGt\",\"type\":\"paragraph\",\"data\":{\"text\":\"Historical research illustrates why falsification-oriented reasoning must be domain-specific.\"},\"tunes\":{}},{\"id\":\"xqHnRKeahO\",\"type\":\"paragraph\",\"data\":{\"text\":\"Suppose two traditions contain conceptually similar ideas and the initial hypothesis proposes direct transmission.\"},\"tunes\":{}},{\"id\":\"6CWXj4SV7j\",\"type\":\"paragraph\",\"data\":{\"text\":\"Supporting similarity is not enough. The hypothesis should generate additional expectations: chronological compatibility, plausible geographical contact, intermediaries, textual or terminological traces, documentary evidence or a pattern of transformation consistent with transmission.\"},\"tunes\":{}},{\"id\":\"bjtNul_qo_\",\"type\":\"paragraph\",\"data\":{\"text\":\"Potentially damaging observations could include chronology that reverses the proposed direction, geographical isolation incompatible with the claimed mechanism, earlier independent examples in both traditions, or evidence that the alleged shared feature appeared only in much later reinterpretations.\"},\"tunes\":{}},{\"id\":\"zKwGPoGQzo\",\"type\":\"paragraph\",\"data\":{\"text\":\"No single absence necessarily falsifies historical transmission. But several independent failures can reduce its explanatory advantage relative to convergence or indirect inheritance.\"},\"tunes\":{}},{\"id\":\"U0mHfz_a9d\",\"type\":\"header\",\"data\":{\"text\":\"Software Debugging: From Suspect to Root Cause\",\"level\":2},\"tunes\":{}},{\"id\":\"i61Enjpd_n\",\"type\":\"paragraph\",\"data\":{\"text\":\"Debugging naturally benefits from falsification because the objective is not to create a plausible narrative around an error message. It is to isolate the mechanism producing the failure.\"},\"tunes\":{}},{\"id\":\"olkjV81zDQ\",\"type\":\"paragraph\",\"data\":{\"text\":\"A useful debugging loop is:\"},\"tunes\":{}},{\"id\":\"-tk7jk1syZ\",\"type\":\"quote\",\"data\":{\"text\":\"Symptom → candidate causes → predicted observations → discriminating test → eliminate causes → reproduce → root cause\",\"caption\":\"\",\"alignment\":\"left\"},\"tunes\":{}},{\"id\":\"U0d0J91Nmi\",\"type\":\"paragraph\",\"data\":{\"text\":\"A hypothesis becomes stronger not because more prose can be written in its favour, but because realistic alternatives fail tests that it passes.\"},\"tunes\":{}},{\"id\":\"BnzW9s6w7s\",\"type\":\"header\",\"data\":{\"text\":\"Software Architecture: Falsifying a Design Decision\",\"level\":2},\"tunes\":{}},{\"id\":\"-_v_seOsQz\",\"type\":\"paragraph\",\"data\":{\"text\":\"Architecture decisions cannot usually be falsified in the strict scientific sense, but they can be subjected to falsification-oriented analysis.\"},\"tunes\":{}},{\"id\":\"tVnm2PR7NZ\",\"type\":\"paragraph\",\"data\":{\"text\":\"Suppose the hypothesis is:\"},\"tunes\":{}},{\"id\":\"C_orszCiPc\",\"type\":\"quote\",\"data\":{\"text\":\"A microservice architecture is required to satisfy the system's scalability and organizational requirements.\",\"caption\":\"\",\"alignment\":\"left\"},\"tunes\":{}},{\"id\":\"-NTcWjXLop\",\"type\":\"paragraph\",\"data\":{\"text\":\"Instead of listing benefits of microservices, the analysis should ask what would make the claim unnecessary.\"},\"tunes\":{}},{\"id\":\"XgU5OD5-P8\",\"type\":\"paragraph\",\"data\":{\"text\":\"If realistic load testing shows that a modular monolith satisfies the expected scale, if deployment independence is not required, and if operational complexity becomes the dominant cost, the original claim has been materially weakened.\"},\"tunes\":{}},{\"id\":\"5ftgXYPJqM\",\"type\":\"paragraph\",\"data\":{\"text\":\"The objective is not to falsify microservices as a technology. It is to test the specific architectural claim under the project's constraints.\"},\"tunes\":{}},{\"id\":\"gCnhyaLF4z\",\"type\":\"header\",\"data\":{\"text\":\"Strategy: What Would Make the Business Thesis Wrong?\",\"level\":2},\"tunes\":{}},{\"id\":\"ATKm56ZPgX\",\"type\":\"paragraph\",\"data\":{\"text\":\"Business strategy frequently suffers from confirmation because evidence can be interpreted after the fact.\"},\"tunes\":{}},{\"id\":\"027zKakU-x\",\"type\":\"paragraph\",\"data\":{\"text\":\"A stronger process defines failure criteria before execution.\"},\"tunes\":{}},{\"id\":\"GmUrknec1R\",\"type\":\"paragraph\",\"data\":{\"text\":\"If a product thesis predicts that a target segment will pay for a particular capability, the methodology should define what observable behaviour would weaken that claim: low conversion after qualified exposure, repeated rejection for the same reason, inability to sustain a target price or evidence that customers solve the problem through an alternative workflow.\"},\"tunes\":{}},{\"id\":\"3fBRSfaMnz\",\"type\":\"quote\",\"data\":{\"text\":\"A strategy becomes more testable when its success criteria are accompanied by explicit failure criteria.\",\"caption\":\"\",\"alignment\":\"left\"},\"tunes\":{}},{\"id\":\"TDMNbb0olF\",\"type\":\"header\",\"data\":{\"text\":\"Falsification Is Not the Same as Devil's Advocacy\",\"level\":2},\"tunes\":{}},{\"id\":\"GeS9ueELEm\",\"type\":\"paragraph\",\"data\":{\"text\":\"A model instructed to 'argue against this conclusion' can always generate objections.\"},\"tunes\":{}},{\"id\":\"E9pcdMnCbO\",\"type\":\"paragraph\",\"data\":{\"text\":\"That is not yet falsification.\"},\"tunes\":{}},{\"id\":\"iRxRCMHcB3\",\"type\":\"paragraph\",\"data\":{\"text\":\"Devil's advocacy optimizes for opposition. Falsification-oriented reasoning optimizes for informative tests.\"},\"tunes\":{}},{\"id\":\"cZNcdygnrR\",\"type\":\"quote\",\"data\":{\"text\":\"A good counterargument sounds plausible. A good falsification test has a result that changes what we should believe.\",\"caption\":\"\",\"alignment\":\"left\"},\"tunes\":{}},{\"id\":\"Fr6fM7CRRr\",\"type\":\"paragraph\",\"data\":{\"text\":\"This distinction prevents the verification process from degrading into artificial debate in which one model argues for a position and another automatically argues against it.\"},\"tunes\":{}},{\"id\":\"RUIiffCsNB\",\"type\":\"header\",\"data\":{\"text\":\"Falsification and Prompt Invariance Work Together\",\"level\":2},\"tunes\":{}},{\"id\":\"c99tT5oc8Z\",\"type\":\"paragraph\",\"data\":{\"text\":\"Prompt Invariance and falsification test different dependencies.\"},\"tunes\":{}},{\"id\":\"ypIJCeQo6Y\",\"type\":\"table\",\"data\":{\"withHeadings\":true,\"stretched\":false,\"content\":[[\"Method\",\"Primary Question\",\"Detects\"],[\"Prompt Invariance\",\"Does the conclusion survive alternative legitimate framings?\",\"Dependence on prompt framing\"],[\"Falsification\",\"Does the hypothesis survive evidence designed to challenge it?\",\"Dependence on confirmation and weak testing\"]]},\"tunes\":{}},{\"id\":\"xShjY_11BX\",\"type\":\"paragraph\",\"data\":{\"text\":\"A hypothesis can pass one test and fail the other.\"},\"tunes\":{}},{\"id\":\"MRp3QSvyjN\",\"type\":\"paragraph\",\"data\":{\"text\":\"A model may reproduce the same incorrect conclusion under several prompt formulations, producing high framing stability but poor evidential validity. Conversely, a strong hypothesis may appear unstable because different prompts expose different subsets of incomplete evidence.\"},\"tunes\":{}},{\"id\":\"cP-8sIVjug\",\"type\":\"paragraph\",\"data\":{\"text\":\"Combining both methods creates a stronger sequence:\"},\"tunes\":{}},{\"id\":\"99J5rojU2O\",\"type\":\"quote\",\"data\":{\"text\":\"Framing variation → competing hypotheses → expected observations → counter-evidence → discriminating tests → confidence update\",\"caption\":\"\",\"alignment\":\"left\"},\"tunes\":{}},{\"id\":\"iLQPA55iCQ\",\"type\":\"header\",\"data\":{\"text\":\"Do Not Let the Same Agent Judge Its Own Test Uncritically\",\"level\":2},\"tunes\":{}},{\"id\":\"OUKeUN2F5w\",\"type\":\"paragraph\",\"data\":{\"text\":\"There is another architectural problem.\"},\"tunes\":{}},{\"id\":\"Ua0N7Lba8U\",\"type\":\"paragraph\",\"data\":{\"text\":\"If the same model generates a hypothesis, designs the test, interprets the evidence and decides whether the hypothesis survived, its errors can propagate through every stage.\"},\"tunes\":{}},{\"id\":\"g7uGYjgWy-\",\"type\":\"paragraph\",\"data\":{\"text\":\"This does not make the process useless, but it motivates separation of roles.\"},\"tunes\":{}},{\"id\":\"np9u2NUdXz\",\"type\":\"quote\",\"data\":{\"text\":\"Hypothesis generator → test designer → evidence retriever → critic → verifier → final synthesizer\",\"caption\":\"\",\"alignment\":\"left\"},\"tunes\":{}},{\"id\":\"eYJ1Z8Dw5_\",\"type\":\"paragraph\",\"data\":{\"text\":\"These roles do not necessarily require six different models. They can be implemented as isolated inference passes with separate context, controlled evidence and structured outputs.\"},\"tunes\":{}},{\"id\":\"2n3qG7vB2D\",\"type\":\"paragraph\",\"data\":{\"text\":\"The important property is procedural independence: later stages should not simply inherit the rhetorical commitment of the original answer.\"},\"tunes\":{}},{\"id\":\"vqlRGTUywH\",\"type\":\"paragraph\",\"data\":{\"text\":\"This becomes the technical subject of \u003Ca href=\\\"{{STAJIC_ARTICLE_6_URL}}\\\">Designing an Epistemic Verification Layer for LLMs\u003C\u002Fa>.\"},\"tunes\":{}},{\"id\":\"ruojX3kDwu\",\"type\":\"header\",\"data\":{\"text\":\"A General Falsification-Oriented AI Workflow\",\"level\":2},\"tunes\":{}},{\"id\":\"S8taBOUfb0\",\"type\":\"quote\",\"data\":{\"text\":\"Problem → evidence normalization → competing hypotheses → predictions → potential falsifiers → discriminating tests → counter-evidence search → auxiliary-assumption check → hypothesis comparison → confidence recalibration → conclusion\",\"caption\":\"\",\"alignment\":\"left\"},\"tunes\":{}},{\"id\":\"XT6-WYF016\",\"type\":\"paragraph\",\"data\":{\"text\":\"This workflow does not require every task to behave like laboratory science.\"},\"tunes\":{}},{\"id\":\"__sqUEl7ab\",\"type\":\"paragraph\",\"data\":{\"text\":\"Instead, it extracts a general epistemic principle from falsification: explanations should be exposed to conditions under which they can lose.\"},\"tunes\":{}},{\"id\":\"09ZLp4dPeR\",\"type\":\"paragraph\",\"data\":{\"text\":\"The exact validator then changes by domain.\"},\"tunes\":{}},{\"id\":\"1_PAOT4qnJ\",\"type\":\"paragraph\",\"data\":{\"text\":\"Historical analysis tests chronology, provenance and transmission. Debugging tests observable system behaviour. Architecture tests requirements and constraints. Strategy tests market assumptions and predefined failure criteria.\"},\"tunes\":{}},{\"id\":\"zF9aypLOBr\",\"type\":\"paragraph\",\"data\":{\"text\":\"This domain-independent core and its domain-specific validators are developed further in \u003Ca href=\\\"{{STAJIC_ARTICLE_5_URL}}\\\">From Research Protocol to General AI Reasoning Framework\u003C\u002Fa>.\"},\"tunes\":{}},{\"id\":\"6kpgYe1r-V\",\"type\":\"header\",\"data\":{\"text\":\"What Falsification Cannot Do\",\"level\":2},\"tunes\":{}},{\"id\":\"m-43Zwil8y\",\"type\":\"list\",\"data\":{\"style\":\"unordered\",\"meta\":{},\"items\":[\"It cannot make incomplete evidence complete.\",\"It cannot guarantee that the correct alternative hypothesis has been generated.\",\"It cannot eliminate errors shared by the model, sources and evaluation process.\",\"It cannot convert inherently interpretive claims into laboratory experiments.\",\"It cannot treat every missing observation as evidence against a hypothesis.\",\"It cannot automatically identify which auxiliary assumption failed when a prediction is contradicted.\",\"It cannot prove a surviving hypothesis true.\",\"It cannot replace experiments, primary sources, domain expertise or empirical measurement where those are required.\"]},\"tunes\":{}},{\"id\":\"UjMN48p74y\",\"type\":\"paragraph\",\"data\":{\"text\":\"A hypothesis that survives repeated attempts at falsification is better described as \u003Cb>corroborated by the tests performed\u003C\u002Fb> than as proven.\"},\"tunes\":{}},{\"id\":\"lnnOeezITl\",\"type\":\"header\",\"data\":{\"text\":\"The Central Principle\",\"level\":2},\"tunes\":{}},{\"id\":\"3bc-laue9s\",\"type\":\"paragraph\",\"data\":{\"text\":\"Generative AI makes confirmation cheap.\"},\"tunes\":{}},{\"id\":\"CkrzUk8My9\",\"type\":\"paragraph\",\"data\":{\"text\":\"Given a sufficiently plausible proposition, a powerful language model can usually produce arguments, analogies, supporting facts and coherent narratives around it.\"},\"tunes\":{}},{\"id\":\"uCI626CQ5O\",\"type\":\"paragraph\",\"data\":{\"text\":\"That is exactly why confirmation should not be the final test.\"},\"tunes\":{}},{\"id\":\"HX5lhiRtbZ\",\"type\":\"quote\",\"data\":{\"text\":\"A reliable reasoning process should not ask only why a hypothesis might be right. It must also define how the hypothesis could be wrong.\",\"caption\":\"\",\"alignment\":\"left\"},\"tunes\":{}},{\"id\":\"m3fGXOx6r4\",\"type\":\"paragraph\",\"data\":{\"text\":\"The quality of an AI conclusion therefore depends not only on how much supporting evidence the system can retrieve, but on whether competing explanations were allowed to win.\"},\"tunes\":{}},{\"id\":\"lCJmcFOveh\",\"type\":\"paragraph\",\"data\":{\"text\":\"That changes the role of the model.\"},\"tunes\":{}},{\"id\":\"b7gsUJe7yN\",\"type\":\"paragraph\",\"data\":{\"text\":\"It is no longer merely an answer generator.\"},\"tunes\":{}},{\"id\":\"yuOWgcdN7m\",\"type\":\"paragraph\",\"data\":{\"text\":\"It becomes a participant in a controlled process in which its own first explanation is provisional, testable and replaceable.\"},\"tunes\":{}},{\"id\":\"MawChI1QNz\",\"type\":\"quote\",\"data\":{\"text\":\"The strongest AI answer is not the one with the most supporting arguments. It is the one whose strongest alternatives were given a fair opportunity to defeat it.\",\"caption\":\"\",\"alignment\":\"left\"},\"tunes\":{}},{\"id\":\"4rMuhiZkDJ\",\"type\":\"delimiter\",\"data\":{},\"tunes\":{}},{\"id\":\"1jcICUuEQb\",\"type\":\"header\",\"data\":{\"text\":\"Research Context\",\"level\":2},\"tunes\":{}},{\"id\":\"y_d8vf02x9\",\"type\":\"paragraph\",\"data\":{\"text\":\"The methodology in this article adapts ideas from philosophy of science and contemporary empirical research on language-model reasoning. Karl Popper's falsificationism emphasized that scientific claims should expose themselves to possible observations that conflict with them, while later philosophy of science made clear that practical falsification is more complicated than the simple rejection of a hypothesis after one anomalous observation.\"},\"tunes\":{}},{\"id\":\"GaSQemV7d0\",\"type\":\"paragraph\",\"data\":{\"text\":\"The distinction matters for AI systems because real tests usually depend on auxiliary assumptions, evidence quality and interpretation. Falsification-oriented AI reasoning therefore uses the logic of disconfirmation without pretending that every complex analytical claim can be reduced to a single decisive experiment.\"},\"tunes\":{}},{\"id\":\"NJd9wCH4vo\",\"type\":\"paragraph\",\"data\":{\"text\":\"The 2026 study by Jhaveri, GX-Chen, Sucholutsky and Choi provides direct empirical motivation for this design. Across eleven LLMs, the authors found confirmation-biased hypothesis exploration in an interactive rule-discovery task. Prompting the models to consider counterexamples consistently reduced this bias and increased average rule-discovery rates from 42% to 56%.\"},\"tunes\":{}},{\"id\":\"Rf7LMywSAW\",\"type\":\"paragraph\",\"data\":{\"text\":\"These findings do not demonstrate that the complete methodology proposed here has been experimentally validated as one unified framework. They support a narrower and important claim: explicit interventions toward disconfirming evidence can improve LLM hypothesis exploration.\"},\"tunes\":{}},{\"id\":\"rhXl_6qblP\",\"type\":\"header\",\"data\":{\"text\":\"Selected References\",\"level\":3},\"tunes\":{}},{\"id\":\"B1isuRcUNx\",\"type\":\"list\",\"data\":{\"style\":\"unordered\",\"meta\":{},\"items\":[\"Popper, K. R. — \u003Ci>The Logic of Scientific Discovery\u003C\u002Fi>. English edition, 1959.\",\"Popper, K. R. — \u003Ci>Conjectures and Refutations: The Growth of Scientific Knowledge\u003C\u002Fi>. 1963.\",\"Stanford Encyclopedia of Philosophy — \u003Ci>Scientific Method\u003C\u002Fi>, sections on hypothetico-deductive testing and falsificationism.\",\"Stanford Encyclopedia of Philosophy — \u003Ci>Karl Popper\u003C\u002Fi>, discussion of basic statements, falsifiability and practical complications of falsification.\",\"Jhaveri, A. R., GX-Chen, A., Sucholutsky, I. & Choi, E. — \u003Ci>Failing to Falsify: Evaluating and Mitigating Confirmation Bias in Language Models\u003C\u002Fi>. arXiv:2604.02485, 2026.\"]},\"tunes\":{}},{\"id\":\"eg9056YrEd\",\"type\":\"delimiter\",\"data\":{},\"tunes\":{}},{\"id\":\"AKTL4fFXB0\",\"type\":\"header\",\"data\":{\"text\":\"Continue the Series\",\"level\":2},\"tunes\":{}},{\"id\":\"PZVMS25Pe9\",\"type\":\"list\",\"data\":{\"style\":\"unordered\",\"meta\":{},\"items\":[\"\u003Cb>\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>\u003C\u002Fb> — the complete methodological framework.\",\"\u003Cb>\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>\u003C\u002Fb> — why framing and user assumptions can influence AI reasoning.\",\"\u003Cb>\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>\u003C\u002Fb> — testing whether conclusions survive blind, inverted and adversarial framings.\",\"\u003Cb>\u003Ca href=\\\"https:\u002F\u002Fstajic.de\u002Fblog\u002Ffrom-research-protocol-to-a-general-ai-reasoning-framework\\\">From Research Protocol to General AI Reasoning Framework\u003C\u002Fa>\u003C\u002Fb> — converting the methodology into a domain-independent reasoning process.\"]},\"tunes\":{}}],\"version\":\"2.31.6\"}",{"time":1246,"blocks":1247,"version":2045},1789811247069,[1248,1252,1256,1260,1264,1268,1272,1276,1280,1284,1288,1292,1296,1300,1304,1308,1312,1316,1320,1324,1328,1332,1336,1340,1344,1348,1352,1356,1360,1364,1368,1372,1376,1380,1384,1388,1392,1396,1400,1404,1409,1413,1418,1422,1426,1430,1434,1438,1442,1453,1457,1461,1465,1469,1473,1477,1481,1485,1489,1493,1497,1501,1505,1509,1513,1517,1521,1525,1529,1533,1537,1541,1545,1549,1553,1557,1561,1565,1569,1573,1577,1589,1593,1597,1601,1605,1609,1613,1617,1621,1625,1629,1633,1644,1648,1652,1656,1685,1689,1693,1697,1701,1705,1709,1713,1717,1721,1725,1729,1733,1737,1741,1745,1749,1753,1757,1761,1765,1769,1773,1777,1781,1785,1789,1793,1797,1801,1805,1809,1813,1817,1821,1825,1829,1833,1837,1841,1857,1861,1865,1869,1873,1877,1881,1885,1889,1893,1897,1901,1905,1909,1913,1917,1921,1925,1929,1933,1937,1950,1954,1958,1962,1966,1970,1974,1978,1982,1986,1990,1994,1997,2001,2005,2009,2013,2017,2021,2029,2032,2036],{"id":215,"data":1249,"type":218,"tunes":1251},{"text":1250},"A language model can produce supporting evidence for a surprisingly large number of plausible explanations.",{},{"id":221,"data":1253,"type":218,"tunes":1255},{"text":1254},"That ability is useful for exploration, but dangerous as a validation method. If the model begins with a hypothesis and the task is simply to explain why it might be correct, a coherent answer can emerge long before the hypothesis has survived a serious test.",{},{"id":226,"data":1257,"type":218,"tunes":1259},{"text":1258},"Reliable AI reasoning therefore requires a stronger question:",{},{"id":231,"data":1261,"type":236,"tunes":1263},{"text":1262,"caption":234,"alignment":235},"What evidence would make this hypothesis fail?",{},{"id":239,"data":1265,"type":218,"tunes":1267},{"text":1266},"This article develops falsification-oriented reasoning as the next layer of the methodology introduced in \u003Ca href=\"{{STAJIC_ARTICLE_1_URL}}\">Beyond Prompt Engineering: A Methodology for More Reliable AI Reasoning\u003C\u002Fa>. The previous article, \u003Ca href=\"{{STAJIC_ARTICLE_3_URL}}\">Prompt Invariance: Does the Conclusion Survive the Prompt?\u003C\u002Fa>, tests whether a conclusion survives changes in framing. Falsification addresses a different problem: whether the hypothesis survives evidence that could count against it.",{},{"id":244,"data":1269,"type":236,"tunes":1271},{"text":1270,"caption":234,"alignment":235},"Prompt Invariance tests dependence on framing. Falsification tests vulnerability to evidence.",{},{"id":249,"data":1273,"type":42,"tunes":1275},{"text":1274,"level":252},"Why Confirmation Is Too Easy",{},{"id":255,"data":1277,"type":218,"tunes":1279},{"text":1278},"Suppose an AI system is given a hypothesis and asked to determine whether it is plausible.",{},{"id":260,"data":1281,"type":218,"tunes":1283},{"text":1282},"The model can search its knowledge, supplied documents or retrieved sources for observations compatible with that hypothesis. If enough compatible observations are found, the resulting explanation may become increasingly persuasive.",{},{"id":265,"data":1285,"type":218,"tunes":1287},{"text":1286},"But compatibility is weak evidence when several competing hypotheses predict the same observation.",{},{"id":270,"data":1289,"type":218,"tunes":1291},{"text":1290},"Consider an abstract case:",{},{"id":275,"data":1293,"type":236,"tunes":1295},{"text":1294,"caption":234,"alignment":235},"Observation E is compatible with hypothesis H1.",{},{"id":280,"data":1297,"type":218,"tunes":1299},{"text":1298},"That statement alone does not establish H1. If H2, H3 and H4 also predict E, then E does little to distinguish among them.",{},{"id":285,"data":1301,"type":236,"tunes":1303},{"text":1302,"caption":234,"alignment":235},"Evidence becomes more informative when competing explanations make different predictions about what we should observe.",{},{"id":290,"data":1305,"type":218,"tunes":1307},{"text":1306},"This shifts the reasoning process from collecting supportive facts toward designing discriminating tests.",{},{"id":295,"data":1309,"type":42,"tunes":1311},{"text":1310,"level":252},"The Classical Idea Behind Falsification",{},{"id":300,"data":1313,"type":218,"tunes":1315},{"text":1314},"Karl Popper's falsificationism emphasized an asymmetry between verification and refutation. Repeated observations compatible with a universal claim cannot logically prove that claim true, while a genuine incompatible observation can conflict directly with it.",{},{"id":305,"data":1317,"type":218,"tunes":1319},{"text":1318},"In simplified logical form:",{},{"id":310,"data":1321,"type":236,"tunes":1323},{"text":1322,"caption":234,"alignment":235},"If H is true, observation O should occur. O does not occur. Therefore H, as stated under the test conditions, is challenged.",{},{"id":315,"data":1325,"type":218,"tunes":1327},{"text":1326},"The strength of this approach lies not in proving theories false at every opportunity, but in requiring claims to expose themselves to possible failure.",{},{"id":321,"data":1329,"type":218,"tunes":1331},{"text":1330},"A hypothesis that can accommodate every conceivable result without changing is difficult to test because no observation meaningfully counts against it.",{},{"id":327,"data":1333,"type":236,"tunes":1335},{"text":1334,"caption":234,"alignment":235},"A useful hypothesis should take a risk: some possible evidence must be less compatible with it than with its alternatives.",{},{"id":332,"data":1337,"type":42,"tunes":1339},{"text":1338,"level":252},"Falsification Is More Complicated in Practice",{},{"id":337,"data":1341,"type":218,"tunes":1343},{"text":1342},"The popular version of falsification is often too simple: one contradictory observation appears, therefore the hypothesis is immediately false.",{},{"id":343,"data":1345,"type":218,"tunes":1347},{"text":1346},"Real investigations rarely work that cleanly.",{},{"id":349,"data":1349,"type":218,"tunes":1351},{"text":1350},"A prediction usually depends not only on the central hypothesis but also on auxiliary assumptions: measurement accuracy, source reliability, environmental conditions, implementation details, background theories or the completeness of the available evidence.",{},{"id":355,"data":1353,"type":218,"tunes":1355},{"text":1354},"A more realistic logical structure is:",{},{"id":361,"data":1357,"type":236,"tunes":1359},{"text":1358,"caption":234,"alignment":235},"H + A1 + A2 + A3 → expected observation O",{},{"id":366,"data":1361,"type":218,"tunes":1363},{"text":1362},"If O is not observed, the failed prediction tells us that something in the complete explanatory structure is wrong. It does not automatically tell us which component failed.",{},{"id":372,"data":1365,"type":218,"tunes":1367},{"text":1366},"The hypothesis may be wrong. The measurement may be unreliable. An assumed condition may not have been satisfied. A source may be incomplete. The model may have misunderstood the evidence.",{},{"id":378,"data":1369,"type":218,"tunes":1371},{"text":1370},"This distinction is crucial for AI reasoning. The goal is not naïve rejection. The goal is systematic exposure of hypotheses to evidence capable of weakening them.",{},{"id":384,"data":1373,"type":42,"tunes":1375},{"text":1374,"level":252},"Falsification-Oriented Reasoning for LLMs",{},{"id":389,"data":1377,"type":218,"tunes":1379},{"text":1378},"Strict Popperian falsification applies most naturally to claims that generate clearly testable consequences. Many tasks handled by AI assistants are less clean: historical interpretation, debugging, architecture, strategy and causal analysis often involve incomplete evidence and probabilistic explanations.",{},{"id":395,"data":1381,"type":218,"tunes":1383},{"text":1382},"For those domains, the more general term \u003Cb>falsification-oriented reasoning\u003C\u002Fb> is useful.",{},{"id":401,"data":1385,"type":218,"tunes":1387},{"text":1386},"The objective is not necessarily to obtain a single logically decisive falsifier. It is to organize the analysis so that evidence capable of contradicting, weakening or discriminating against the current hypothesis receives explicit attention.",{},{"id":407,"data":1389,"type":236,"tunes":1391},{"text":1390,"caption":234,"alignment":235},"Do not merely ask what supports H. Ask what should exist if H is true, what should be difficult to explain if H is true, and what alternative explains the same evidence better.",{},{"id":412,"data":1393,"type":42,"tunes":1395},{"text":1394,"level":252},"Step 1 — State the Hypothesis Precisely",{},{"id":417,"data":1397,"type":218,"tunes":1399},{"text":1398},"A hypothesis cannot be meaningfully tested if it is vague enough to absorb every outcome.",{},{"id":422,"data":1401,"type":218,"tunes":1403},{"text":1402},"Compare:",{},{"id":427,"data":1405,"type":236,"tunes":1408},{"text":1406,"caption":1407,"alignment":235},"The system is unstable because something in the network layer is wrong.","Weak hypothesis",{},{"id":433,"data":1410,"type":218,"tunes":1412},{"text":1411},"with:",{},{"id":438,"data":1414,"type":236,"tunes":1417},{"text":1415,"caption":1416,"alignment":235},"The intermittent API failures are caused by the reverse proxy closing upstream connections when the configured timeout is exceeded.","Testable hypothesis",{},{"id":444,"data":1419,"type":218,"tunes":1421},{"text":1420},"The second claim exposes itself to concrete tests. We can inspect timeout values, connection duration, proxy logs, upstream behaviour and failures occurring below the configured threshold.",{},{"id":449,"data":1423,"type":218,"tunes":1425},{"text":1424},"The more precisely a hypothesis defines the claimed relationship, the easier it becomes to determine what evidence would count against it.",{},{"id":454,"data":1427,"type":42,"tunes":1429},{"text":1428,"level":252},"Step 2 — Generate Genuine Competing Hypotheses",{},{"id":459,"data":1431,"type":218,"tunes":1433},{"text":1432},"Testing one hypothesis in isolation is weak because almost every observation is interpreted relative to something else.",{},{"id":464,"data":1435,"type":218,"tunes":1437},{"text":1436},"The system should therefore construct credible alternatives before evaluating the evidence.",{},{"id":469,"data":1439,"type":218,"tunes":1441},{"text":1440},"For the same API failure, candidate explanations might include:",{},{"id":474,"data":1443,"type":485,"tunes":1452},{"meta":1444,"items":1445,"style":484},{},[1446,1447,1448,1449,1450,1451],"\u003Cb>H1:\u003C\u002Fb> reverse-proxy timeout;","\u003Cb>H2:\u003C\u002Fb> upstream application crash or restart;","\u003Cb>H3:\u003C\u002Fb> database connection exhaustion;","\u003Cb>H4:\u003C\u002Fb> network interruption or packet loss;","\u003Cb>H5:\u003C\u002Fb> client-side timeout;","\u003Cb>H6:\u003C\u002Fb> interaction between several layers rather than one isolated cause.",{},{"id":488,"data":1454,"type":218,"tunes":1456},{"text":1455},"The alternatives must be plausible enough to compete. Generating obviously inferior alternatives merely creates the appearance of critical reasoning.",{},{"id":493,"data":1458,"type":236,"tunes":1460},{"text":1459,"caption":234,"alignment":235},"A hypothesis has not survived competition if the alternatives were designed to lose.",{},{"id":498,"data":1462,"type":42,"tunes":1464},{"text":1463,"level":252},"Step 3 — Derive Expected Observations",{},{"id":503,"data":1466,"type":218,"tunes":1468},{"text":1467},"For each serious hypothesis, the model should derive observations expected under that explanation.",{},{"id":508,"data":1470,"type":218,"tunes":1472},{"text":1471},"If H1 is the reverse-proxy timeout hypothesis, expected observations could include failures clustered around a specific duration, corresponding timeout messages in proxy logs, healthy upstream processes at the time of failure and disappearance of the failure after a controlled timeout change.",{},{"id":513,"data":1474,"type":218,"tunes":1476},{"text":1475},"For H2, an application restart hypothesis, we would expect a different pattern: process restarts, exceptions, missing application availability, resource exhaustion or correlated container events.",{},{"id":518,"data":1478,"type":218,"tunes":1480},{"text":1479},"The important step is to derive these expectations \u003Cb>before\u003C\u002Fb> interpreting every available observation as support.",{},{"id":523,"data":1482,"type":42,"tunes":1484},{"text":1483,"level":252},"Step 4 — Define Potential Disconfirming Evidence",{},{"id":528,"data":1486,"type":218,"tunes":1488},{"text":1487},"For each hypothesis, ask what evidence would materially weaken it.",{},{"id":533,"data":1490,"type":236,"tunes":1492},{"text":1491,"caption":234,"alignment":235},"What would we expect not to observe if this hypothesis were the main explanation?",{},{"id":538,"data":1494,"type":218,"tunes":1496},{"text":1495},"For the proxy-timeout hypothesis, examples include failures occurring far below the configured threshold, identical failures when bypassing the proxy, no relevant proxy-side event, or evidence that the upstream application terminates the connection first.",{},{"id":543,"data":1498,"type":218,"tunes":1500},{"text":1499},"This changes the model's search objective.",{},{"id":548,"data":1502,"type":236,"tunes":1504},{"text":1503,"caption":234,"alignment":235},"Confirmation search: Find evidence compatible with H1. Disconfirmation search: Find observations that H1 predicts poorly or that H2 predicts substantially better.",{},{"id":553,"data":1506,"type":42,"tunes":1508},{"text":1507,"level":252},"Step 5 — Prefer Discriminating Tests",{},{"id":558,"data":1510,"type":218,"tunes":1512},{"text":1511},"Not every test is equally informative.",{},{"id":563,"data":1514,"type":218,"tunes":1516},{"text":1515},"Suppose H1 and H2 both predict elevated error rates. Observing another error provides little discrimination.",{},{"id":568,"data":1518,"type":218,"tunes":1520},{"text":1519},"A better test searches for an observation on which their predictions diverge.",{},{"id":573,"data":1522,"type":236,"tunes":1524},{"text":1523,"caption":234,"alignment":235},"Good test: an observation likely under H1 but unlikely under H2, or vice versa.",{},{"id":578,"data":1526,"type":218,"tunes":1528},{"text":1527},"In debugging, bypassing the suspected proxy may be discriminating. In historical research, demonstrating chronology that makes direct transmission impossible can be strongly discriminating. In product analysis, observing the same demand decline in a control market unaffected by the proposed cause can weaken a causal explanation.",{},{"id":583,"data":1530,"type":218,"tunes":1532},{"text":1531},"The methodology therefore values evidence not only by reliability but also by its ability to distinguish among competing explanations.",{},{"id":588,"data":1534,"type":42,"tunes":1536},{"text":1535,"level":252},"Step 6 — Actively Search for Counter-Evidence",{},{"id":593,"data":1538,"type":218,"tunes":1540},{"text":1539},"Once potential falsifiers and discriminating observations are defined, the system should actively search for them.",{},{"id":598,"data":1542,"type":218,"tunes":1544},{"text":1543},"This requirement matters because language models themselves can exhibit confirmation-biased hypothesis testing.",{},{"id":603,"data":1546,"type":218,"tunes":1548},{"text":1547},"In 2026, Jhaveri, GX-Chen, Sucholutsky and Choi adapted a classic rule-discovery task to eleven language models from multiple model families and scales. Models frequently proposed examples that would confirm their current rule rather than examples designed to falsify it.",{},{"id":608,"data":1550,"type":218,"tunes":1552},{"text":1551},"The consequence was practical: confirmation-oriented exploration produced slower and less successful discovery of the hidden rule.",{},{"id":613,"data":1554,"type":218,"tunes":1556},{"text":1555},"When the researchers explicitly encouraged consideration of counterexamples, average rule-discovery success increased from 42% to 56% in the reported experiments.",{},{"id":618,"data":1558,"type":236,"tunes":1560},{"text":1559,"caption":234,"alignment":235},"The model did not need a new knowledge base. It needed a better hypothesis-testing strategy.",{},{"id":623,"data":1562,"type":218,"tunes":1564},{"text":1563},"This is directly relevant to the broader methodology: reasoning quality can improve when the inference process changes from confirmation-seeking to falsification-oriented exploration.",{},{"id":628,"data":1566,"type":42,"tunes":1568},{"text":1567,"level":252},"Step 7 — Separate Contradiction from Rejection",{},{"id":633,"data":1570,"type":218,"tunes":1572},{"text":1571},"Finding evidence against a hypothesis does not always justify immediate rejection.",{},{"id":638,"data":1574,"type":218,"tunes":1576},{"text":1575},"The system should first evaluate the quality of the counter-evidence:",{},{"id":643,"data":1578,"type":485,"tunes":1588},{"meta":1579,"items":1580,"style":484},{},[1581,1582,1583,1584,1585,1586,1587],"Is the observation reliable?","Is the source primary or indirect?","Could measurement or retrieval error explain the conflict?","Does the hypothesis actually predict the disputed observation?","Does the contradiction depend on an auxiliary assumption?","Is the counter-evidence independently corroborated?","Does a competing hypothesis explain the evidence more successfully?",{},{"id":656,"data":1590,"type":218,"tunes":1592},{"text":1591},"Only after this evaluation should the model determine whether the hypothesis is weakened, substantially revised or rejected.",{},{"id":661,"data":1594,"type":42,"tunes":1596},{"text":1595,"level":252},"Step 8 — Prevent Ad Hoc Rescue",{},{"id":666,"data":1598,"type":218,"tunes":1600},{"text":1599},"A hypothesis can become effectively unfalsifiable if every contradictory observation produces a new exception.",{},{"id":671,"data":1602,"type":218,"tunes":1604},{"text":1603},"The pattern looks like this:",{},{"id":676,"data":1606,"type":236,"tunes":1608},{"text":1607,"caption":234,"alignment":235},"Prediction fails → add exception → prediction fails again → add another exception → retain original conclusion indefinitely",{},{"id":681,"data":1610,"type":218,"tunes":1612},{"text":1611},"Not every modification is illegitimate. Scientific and technical progress frequently occurs because unexpected evidence reveals a missing variable or incorrect auxiliary assumption.",{},{"id":686,"data":1614,"type":218,"tunes":1616},{"text":1615},"The methodological distinction is whether the revision creates new testable consequences or merely protects the preferred conclusion from failure.",{},{"id":691,"data":1618,"type":236,"tunes":1620},{"text":1619,"caption":234,"alignment":235},"A productive revision increases explanatory and predictive precision. An ad hoc rescue only decreases the chance that the hypothesis can ever lose.",{},{"id":696,"data":1622,"type":42,"tunes":1624},{"text":1623,"level":252},"Step 9 — Update Confidence Instead of Defending the Initial Answer",{},{"id":701,"data":1626,"type":218,"tunes":1628},{"text":1627},"The output of falsification-oriented reasoning does not have to be binary.",{},{"id":706,"data":1630,"type":218,"tunes":1632},{"text":1631},"Possible states include:",{},{"id":711,"data":1634,"type":485,"tunes":1643},{"meta":1635,"items":1636,"style":484},{},[1637,1638,1639,1640,1641,1642],"\u003Cb>Strongly supported:\u003C\u002Fb> survives serious discriminating tests and competing explanations perform substantially worse.","\u003Cb>Provisionally supported:\u003C\u002Fb> best available explanation, but important uncertainties remain.","\u003Cb>Weakened:\u003C\u002Fb> material counter-evidence exists but is not decisive.","\u003Cb>Underdetermined:\u003C\u002Fb> several hypotheses explain the current evidence similarly well.","\u003Cb>Rejected:\u003C\u002Fb> reliable evidence conflicts with a central prediction and alternative explanations perform better.","\u003Cb>Not testable with available evidence:\u003C\u002Fb> the current corpus cannot meaningfully discriminate among the claims.",{},{"id":723,"data":1645,"type":218,"tunes":1647},{"text":1646},"The central rule is simple: confidence should follow the result of the tests rather than the model's rhetorical investment in its first answer.",{},{"id":728,"data":1649,"type":42,"tunes":1651},{"text":1650,"level":252},"A Falsification Matrix",{},{"id":733,"data":1653,"type":218,"tunes":1655},{"text":1654},"For complex analysis, hypotheses can be normalized into a comparison matrix.",{},{"id":738,"data":1657,"type":769,"tunes":1684},{"content":1658,"stretched":43,"withHeadings":14},[1659,1661,1664,1667,1670,1673,1676,1678,1681],[1660,743,744,745],"Dimension",[1662,1663,1663,1663],"Core claim","Define precisely",[1665,1666,1666,1666],"Expected evidence","List predictions",[1668,1669,1669,1669],"Potential counter-evidence","Define",[1671,1672,1672,1672],"Discriminating test","Specify",[1674,1675,1675,1675],"Supporting observations","Record",[1677,1675,1675,1675],"Contradictory observations",[1679,1680,1680,1680],"Auxiliary assumptions","Expose",[1682,1683,1683,1683],"Current status","Reassess",{},{"id":772,"data":1686,"type":218,"tunes":1688},{"text":1687},"The matrix prevents a common failure mode: applying rigorous scrutiny to alternatives while allowing the preferred hypothesis to remain vague.",{},{"id":777,"data":1690,"type":42,"tunes":1692},{"text":1691,"level":252},"Negative Evidence Requires Special Care",{},{"id":782,"data":1694,"type":218,"tunes":1696},{"text":1695},"Absence of expected evidence can weaken a hypothesis, but only under specific conditions.",{},{"id":787,"data":1698,"type":218,"tunes":1700},{"text":1699},"The statement 'we found no evidence for X' is not equivalent to 'X did not occur'.",{},{"id":792,"data":1702,"type":218,"tunes":1704},{"text":1703},"Negative evidence becomes informative when there is a justified expectation that the evidence would probably be observable, preserved, logged, documented or measurable if the hypothesis were true.",{},{"id":797,"data":1706,"type":236,"tunes":1708},{"text":1707,"caption":234,"alignment":235},"Absence of evidence matters most when evidence should not be absent.",{},{"id":802,"data":1710,"type":218,"tunes":1712},{"text":1711},"In debugging, the absence of a required log event can be meaningful if logging is known to be complete. In historical research, absence from a fragmentary archive is usually much weaker. In security analysis, absence of an alert has little value if the relevant telemetry was never collected.",{},{"id":807,"data":1714,"type":218,"tunes":1716},{"text":1715},"The model must therefore evaluate both the missing evidence and the probability that such evidence would have survived or been observable.",{},{"id":812,"data":1718,"type":42,"tunes":1720},{"text":1719,"level":252},"Historical Research: Transmission Versus Similarity",{},{"id":817,"data":1722,"type":218,"tunes":1724},{"text":1723},"Historical research illustrates why falsification-oriented reasoning must be domain-specific.",{},{"id":822,"data":1726,"type":218,"tunes":1728},{"text":1727},"Suppose two traditions contain conceptually similar ideas and the initial hypothesis proposes direct transmission.",{},{"id":827,"data":1730,"type":218,"tunes":1732},{"text":1731},"Supporting similarity is not enough. The hypothesis should generate additional expectations: chronological compatibility, plausible geographical contact, intermediaries, textual or terminological traces, documentary evidence or a pattern of transformation consistent with transmission.",{},{"id":832,"data":1734,"type":218,"tunes":1736},{"text":1735},"Potentially damaging observations could include chronology that reverses the proposed direction, geographical isolation incompatible with the claimed mechanism, earlier independent examples in both traditions, or evidence that the alleged shared feature appeared only in much later reinterpretations.",{},{"id":837,"data":1738,"type":218,"tunes":1740},{"text":1739},"No single absence necessarily falsifies historical transmission. But several independent failures can reduce its explanatory advantage relative to convergence or indirect inheritance.",{},{"id":842,"data":1742,"type":42,"tunes":1744},{"text":1743,"level":252},"Software Debugging: From Suspect to Root Cause",{},{"id":847,"data":1746,"type":218,"tunes":1748},{"text":1747},"Debugging naturally benefits from falsification because the objective is not to create a plausible narrative around an error message. It is to isolate the mechanism producing the failure.",{},{"id":852,"data":1750,"type":218,"tunes":1752},{"text":1751},"A useful debugging loop is:",{},{"id":857,"data":1754,"type":236,"tunes":1756},{"text":1755,"caption":234,"alignment":235},"Symptom → candidate causes → predicted observations → discriminating test → eliminate causes → reproduce → root cause",{},{"id":862,"data":1758,"type":218,"tunes":1760},{"text":1759},"A hypothesis becomes stronger not because more prose can be written in its favour, but because realistic alternatives fail tests that it passes.",{},{"id":867,"data":1762,"type":42,"tunes":1764},{"text":1763,"level":252},"Software Architecture: Falsifying a Design Decision",{},{"id":872,"data":1766,"type":218,"tunes":1768},{"text":1767},"Architecture decisions cannot usually be falsified in the strict scientific sense, but they can be subjected to falsification-oriented analysis.",{},{"id":877,"data":1770,"type":218,"tunes":1772},{"text":1771},"Suppose the hypothesis is:",{},{"id":882,"data":1774,"type":236,"tunes":1776},{"text":1775,"caption":234,"alignment":235},"A microservice architecture is required to satisfy the system's scalability and organizational requirements.",{},{"id":887,"data":1778,"type":218,"tunes":1780},{"text":1779},"Instead of listing benefits of microservices, the analysis should ask what would make the claim unnecessary.",{},{"id":892,"data":1782,"type":218,"tunes":1784},{"text":1783},"If realistic load testing shows that a modular monolith satisfies the expected scale, if deployment independence is not required, and if operational complexity becomes the dominant cost, the original claim has been materially weakened.",{},{"id":897,"data":1786,"type":218,"tunes":1788},{"text":1787},"The objective is not to falsify microservices as a technology. It is to test the specific architectural claim under the project's constraints.",{},{"id":902,"data":1790,"type":42,"tunes":1792},{"text":1791,"level":252},"Strategy: What Would Make the Business Thesis Wrong?",{},{"id":907,"data":1794,"type":218,"tunes":1796},{"text":1795},"Business strategy frequently suffers from confirmation because evidence can be interpreted after the fact.",{},{"id":912,"data":1798,"type":218,"tunes":1800},{"text":1799},"A stronger process defines failure criteria before execution.",{},{"id":917,"data":1802,"type":218,"tunes":1804},{"text":1803},"If a product thesis predicts that a target segment will pay for a particular capability, the methodology should define what observable behaviour would weaken that claim: low conversion after qualified exposure, repeated rejection for the same reason, inability to sustain a target price or evidence that customers solve the problem through an alternative workflow.",{},{"id":922,"data":1806,"type":236,"tunes":1808},{"text":1807,"caption":234,"alignment":235},"A strategy becomes more testable when its success criteria are accompanied by explicit failure criteria.",{},{"id":927,"data":1810,"type":42,"tunes":1812},{"text":1811,"level":252},"Falsification Is Not the Same as Devil's Advocacy",{},{"id":932,"data":1814,"type":218,"tunes":1816},{"text":1815},"A model instructed to 'argue against this conclusion' can always generate objections.",{},{"id":937,"data":1818,"type":218,"tunes":1820},{"text":1819},"That is not yet falsification.",{},{"id":942,"data":1822,"type":218,"tunes":1824},{"text":1823},"Devil's advocacy optimizes for opposition. Falsification-oriented reasoning optimizes for informative tests.",{},{"id":947,"data":1826,"type":236,"tunes":1828},{"text":1827,"caption":234,"alignment":235},"A good counterargument sounds plausible. A good falsification test has a result that changes what we should believe.",{},{"id":952,"data":1830,"type":218,"tunes":1832},{"text":1831},"This distinction prevents the verification process from degrading into artificial debate in which one model argues for a position and another automatically argues against it.",{},{"id":957,"data":1834,"type":42,"tunes":1836},{"text":1835,"level":252},"Falsification and Prompt Invariance Work Together",{},{"id":962,"data":1838,"type":218,"tunes":1840},{"text":1839},"Prompt Invariance and falsification test different dependencies.",{},{"id":967,"data":1842,"type":769,"tunes":1856},{"content":1843,"stretched":43,"withHeadings":14},[1844,1848,1852],[1845,1846,1847],"Method","Primary Question","Detects",[1849,1850,1851],"Prompt Invariance","Does the conclusion survive alternative legitimate framings?","Dependence on prompt framing",[1853,1854,1855],"Falsification","Does the hypothesis survive evidence designed to challenge it?","Dependence on confirmation and weak testing",{},{"id":984,"data":1858,"type":218,"tunes":1860},{"text":1859},"A hypothesis can pass one test and fail the other.",{},{"id":989,"data":1862,"type":218,"tunes":1864},{"text":1863},"A model may reproduce the same incorrect conclusion under several prompt formulations, producing high framing stability but poor evidential validity. Conversely, a strong hypothesis may appear unstable because different prompts expose different subsets of incomplete evidence.",{},{"id":994,"data":1866,"type":218,"tunes":1868},{"text":1867},"Combining both methods creates a stronger sequence:",{},{"id":999,"data":1870,"type":236,"tunes":1872},{"text":1871,"caption":234,"alignment":235},"Framing variation → competing hypotheses → expected observations → counter-evidence → discriminating tests → confidence update",{},{"id":1004,"data":1874,"type":42,"tunes":1876},{"text":1875,"level":252},"Do Not Let the Same Agent Judge Its Own Test Uncritically",{},{"id":1009,"data":1878,"type":218,"tunes":1880},{"text":1879},"There is another architectural problem.",{},{"id":1014,"data":1882,"type":218,"tunes":1884},{"text":1883},"If the same model generates a hypothesis, designs the test, interprets the evidence and decides whether the hypothesis survived, its errors can propagate through every stage.",{},{"id":1019,"data":1886,"type":218,"tunes":1888},{"text":1887},"This does not make the process useless, but it motivates separation of roles.",{},{"id":1024,"data":1890,"type":236,"tunes":1892},{"text":1891,"caption":234,"alignment":235},"Hypothesis generator → test designer → evidence retriever → critic → verifier → final synthesizer",{},{"id":1029,"data":1894,"type":218,"tunes":1896},{"text":1895},"These roles do not necessarily require six different models. They can be implemented as isolated inference passes with separate context, controlled evidence and structured outputs.",{},{"id":1034,"data":1898,"type":218,"tunes":1900},{"text":1899},"The important property is procedural independence: later stages should not simply inherit the rhetorical commitment of the original answer.",{},{"id":1039,"data":1902,"type":218,"tunes":1904},{"text":1903},"This becomes the technical subject of \u003Ca href=\"{{STAJIC_ARTICLE_6_URL}}\">Designing an Epistemic Verification Layer for LLMs\u003C\u002Fa>.",{},{"id":1044,"data":1906,"type":42,"tunes":1908},{"text":1907,"level":252},"A General Falsification-Oriented AI Workflow",{},{"id":1049,"data":1910,"type":236,"tunes":1912},{"text":1911,"caption":234,"alignment":235},"Problem → evidence normalization → competing hypotheses → predictions → potential falsifiers → discriminating tests → counter-evidence search → auxiliary-assumption check → hypothesis comparison → confidence recalibration → conclusion",{},{"id":1054,"data":1914,"type":218,"tunes":1916},{"text":1915},"This workflow does not require every task to behave like laboratory science.",{},{"id":1059,"data":1918,"type":218,"tunes":1920},{"text":1919},"Instead, it extracts a general epistemic principle from falsification: explanations should be exposed to conditions under which they can lose.",{},{"id":1064,"data":1922,"type":218,"tunes":1924},{"text":1923},"The exact validator then changes by domain.",{},{"id":1069,"data":1926,"type":218,"tunes":1928},{"text":1927},"Historical analysis tests chronology, provenance and transmission. Debugging tests observable system behaviour. Architecture tests requirements and constraints. Strategy tests market assumptions and predefined failure criteria.",{},{"id":1074,"data":1930,"type":218,"tunes":1932},{"text":1931},"This domain-independent core and its domain-specific validators are developed further in \u003Ca href=\"{{STAJIC_ARTICLE_5_URL}}\">From Research Protocol to General AI Reasoning Framework\u003C\u002Fa>.",{},{"id":1079,"data":1934,"type":42,"tunes":1936},{"text":1935,"level":252},"What Falsification Cannot Do",{},{"id":1084,"data":1938,"type":485,"tunes":1949},{"meta":1939,"items":1940,"style":484},{},[1941,1942,1943,1944,1945,1946,1947,1948],"It cannot make incomplete evidence complete.","It cannot guarantee that the correct alternative hypothesis has been generated.","It cannot eliminate errors shared by the model, sources and evaluation process.","It cannot convert inherently interpretive claims into laboratory experiments.","It cannot treat every missing observation as evidence against a hypothesis.","It cannot automatically identify which auxiliary assumption failed when a prediction is contradicted.","It cannot prove a surviving hypothesis true.","It cannot replace experiments, primary sources, domain expertise or empirical measurement where those are required.",{},{"id":1098,"data":1951,"type":218,"tunes":1953},{"text":1952},"A hypothesis that survives repeated attempts at falsification is better described as \u003Cb>corroborated by the tests performed\u003C\u002Fb> than as proven.",{},{"id":1103,"data":1955,"type":42,"tunes":1957},{"text":1956,"level":252},"The Central Principle",{},{"id":1108,"data":1959,"type":218,"tunes":1961},{"text":1960},"Generative AI makes confirmation cheap.",{},{"id":1113,"data":1963,"type":218,"tunes":1965},{"text":1964},"Given a sufficiently plausible proposition, a powerful language model can usually produce arguments, analogies, supporting facts and coherent narratives around it.",{},{"id":1118,"data":1967,"type":218,"tunes":1969},{"text":1968},"That is exactly why confirmation should not be the final test.",{},{"id":1123,"data":1971,"type":236,"tunes":1973},{"text":1972,"caption":234,"alignment":235},"A reliable reasoning process should not ask only why a hypothesis might be right. It must also define how the hypothesis could be wrong.",{},{"id":1128,"data":1975,"type":218,"tunes":1977},{"text":1976},"The quality of an AI conclusion therefore depends not only on how much supporting evidence the system can retrieve, but on whether competing explanations were allowed to win.",{},{"id":1133,"data":1979,"type":218,"tunes":1981},{"text":1980},"That changes the role of the model.",{},{"id":1138,"data":1983,"type":218,"tunes":1985},{"text":1984},"It is no longer merely an answer generator.",{},{"id":1143,"data":1987,"type":218,"tunes":1989},{"text":1988},"It becomes a participant in a controlled process in which its own first explanation is provisional, testable and replaceable.",{},{"id":1148,"data":1991,"type":236,"tunes":1993},{"text":1992,"caption":234,"alignment":235},"The strongest AI answer is not the one with the most supporting arguments. It is the one whose strongest alternatives were given a fair opportunity to defeat it.",{},{"id":1153,"data":1995,"type":1155,"tunes":1996},{},{},{"id":1158,"data":1998,"type":42,"tunes":2000},{"text":1999,"level":252},"Research Context",{},{"id":1163,"data":2002,"type":218,"tunes":2004},{"text":2003},"The methodology in this article adapts ideas from philosophy of science and contemporary empirical research on language-model reasoning. Karl Popper's falsificationism emphasized that scientific claims should expose themselves to possible observations that conflict with them, while later philosophy of science made clear that practical falsification is more complicated than the simple rejection of a hypothesis after one anomalous observation.",{},{"id":1168,"data":2006,"type":218,"tunes":2008},{"text":2007},"The distinction matters for AI systems because real tests usually depend on auxiliary assumptions, evidence quality and interpretation. Falsification-oriented AI reasoning therefore uses the logic of disconfirmation without pretending that every complex analytical claim can be reduced to a single decisive experiment.",{},{"id":1173,"data":2010,"type":218,"tunes":2012},{"text":2011},"The 2026 study by Jhaveri, GX-Chen, Sucholutsky and Choi provides direct empirical motivation for this design. Across eleven LLMs, the authors found confirmation-biased hypothesis exploration in an interactive rule-discovery task. Prompting the models to consider counterexamples consistently reduced this bias and increased average rule-discovery rates from 42% to 56%.",{},{"id":1178,"data":2014,"type":218,"tunes":2016},{"text":2015},"These findings do not demonstrate that the complete methodology proposed here has been experimentally validated as one unified framework. They support a narrower and important claim: explicit interventions toward disconfirming evidence can improve LLM hypothesis exploration.",{},{"id":1183,"data":2018,"type":42,"tunes":2020},{"text":2019,"level":1186},"Selected References",{},{"id":1189,"data":2022,"type":485,"tunes":2028},{"meta":2023,"items":2024,"style":484},{},[2025,1194,2026,2027,1197],"Popper, K. R. — \u003Ci>The Logic of Scientific Discovery\u003C\u002Fi>. English edition, 1959.","Stanford Encyclopedia of Philosophy — \u003Ci>Scientific Method\u003C\u002Fi>, sections on hypothetico-deductive testing and falsificationism.","Stanford Encyclopedia of Philosophy — \u003Ci>Karl Popper\u003C\u002Fi>, discussion of basic statements, falsifiability and practical complications of falsification.",{},{"id":1200,"data":2030,"type":1155,"tunes":2031},{},{},{"id":1204,"data":2033,"type":42,"tunes":2035},{"text":2034,"level":252},"Continue the Series",{},{"id":1209,"data":2037,"type":485,"tunes":2044},{"meta":2038,"items":2039,"style":484},{},[2040,2041,2042,2043],"\u003Cb>\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>\u003C\u002Fb> — the complete methodological framework.","\u003Cb>\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>\u003C\u002Fb> — why framing and user assumptions can influence AI reasoning.","\u003Cb>\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>\u003C\u002Fb> — testing whether conclusions survive blind, inverted and adversarial framings.","\u003Cb>\u003Ca href=\"https:\u002F\u002Fstajic.de\u002Fblog\u002Ffrom-research-protocol-to-a-general-ai-reasoning-framework\">From Research Protocol to General AI Reasoning Framework\u003C\u002Fa>\u003C\u002Fb> — converting the methodology into a domain-independent reasoning process.",{},"2.31.6","AI models can generate convincing evidence for almost any plausible hypothesis. A more reliable methodology asks the opposite question: what evidence would weaken, contradict or force us to abandon the conclusion? This article develops falsification-oriented reasoning for LLMs using competing hypotheses, discriminating tests, counter-evidence and explicit rejection 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Ovaj članak analizira razliku između multimodalnog kreiranja i inteligencije na nivou delovanja, zašto je Gemini 3.5 Flash važan za agente i kodiranje, i kako ovi modeli pokreću širu promenu platforme Google I\u002FO 2026.","\u002Fuploads\u002F2026\u002F05\u002Fgoogle-io-2026-gemini-omni-and-gemini-3-5-1779227791401-6hzln1.webp","2026-05-21T11:01:00.000Z",{"id":2653,"slug":2654,"title":2655,"excerpt":2656,"featuredImage":2657,"publishedAt":2658},"456","zbt-z8102ax-hardware-packaging-review","ZBT Z8102AX recenzija hardvera i pakovanja: Snažan ruter, slaba kutija","ZBT Z8102AX ostavlja solidan prvi utisak kao tanak crni metalni 5G OpenWrt ruter sa više konektora za antene, dual-SIM slotovima, USB, LAN\u002FWAN portovima i praktičnim kompletom dodatne opreme. Hardver deluje korisno i ozbiljno, ali je pakovanje očigledno slaba tačka.","\u002Fuploads\u002F2026\u002F06\u002Fopenwrt-router-review-dual-sim-02-1781620590938-y33j4b.webp","2026-06-16T04:40:00.000Z",{"id":2660,"slug":2661,"title":2662,"excerpt":2663,"featuredImage":2664,"publishedAt":2665},"493","mlops-vs-llmops-what-changes-when-the-model-is-an-llm","MLOps vs LLMOps: Šta se menja kada je model LLM","MLOps upravlja sistemima mašinskog učenja; LLMOps proširuje te prakse na promptove, kontekst, pretragu, provajdere, alate, evaluacije i ponašanje u vreme izvršavanja oko velikih jezičkih modela.","\u002Fuploads\u002F2026\u002F10\u002Fmlops-vs-llmops-what-changes-when-the-model-is-an-llm-1791487319869-2v7hxo.webp","2026-10-08T15:20:00.000Z",{"id":2667,"slug":2668,"title":2669,"excerpt":2670,"featuredImage":2671,"publishedAt":2672},"382","a-practical-monorepo-architecture-next-js-platform-admin-fastify-api-prisma-and-nginx","Praktična monorepo arhitektura sa Next.js, Fastify, Prisma i NGINX","Istražite praktičnu monorepo arhitekturu koristeći Next.js, Fastify, Prisma i NGINX, ističući integraciju i tok rada iz stvarnog sveta.","\u002Fuploads\u002F2026\u002F01\u002Fa-practical-monorepo-architecture-next-js-platform-admin-fastify-api-prisma-and-nginx-1769885526116-q1100n.webp","2026-01-31T08:18:00.000Z",{"id":2674,"slug":2675,"title":2676,"excerpt":2677,"featuredImage":2678,"publishedAt":2679},"472","why-more-context-can-make-ai-answers-worse","Zašto više konteksta može pogoršati AI odgovore","Veći kontekstni prozor ne garantuje bolji odgovor. Ovaj članak objašnjava kako razblaživanje signala, protivrečni dokazi, zastarelo stanje, osetljivost na poziciju i kompresija sa gubicima mogu smanjiti pouzdanost veštačke inteligencije—i uvodi praktičan test pritiska konteksta.","\u002Fuploads\u002F2026\u002F09\u002Fwhy-more-context-can-make-ai-answers-worse-1790351615793-2ntv2v.webp","2026-09-25T11:51:00.000Z",{"id":2681,"slug":2682,"title":2683,"excerpt":2684,"featuredImage":2685,"publishedAt":2686},"476","mcp-vs-a2a-vs-ucp-vs-ap2-vs-a2ui-the-agent-protocol-stack-explained","MCP vs A2A vs UCP vs AP2 vs A2UI: Objašnjen stek agentskih protokola","MCP, A2A, UCP, AP2 i A2UI se često predstavljaju kao konkurentski standardi za agente. Oni uglavnom rešavaju različite probleme interoperabilnosti. Ovaj vodič mapira svaki protokol na granicu koju zapravo standardizuje—i pokazuje kako oni mogu da rade zajedno u jednom produkcionom sistemu.","\u002Fuploads\u002F2026\u002F09\u002Fmcp-vs-a2a-vs-ucp-vs-ap2-vs-a2ui-the-agent-protocol-stack-explained-1790352625869-2ezle0.webp","2026-09-25T12:09:00.000Z",{"id":2688,"slug":2689,"title":2690,"excerpt":2691,"featuredImage":2692,"publishedAt":2693},"495","sovereign-ai-control-of-models-data-infrastructure-and-dependencies","Suverena AI: Kontrola modela, podataka, infrastrukture i zavisnosti","Suverena AI se odnosi na efektivnu kontrolu nad modelima, podacima, infrastrukturom, softverom, operacijama i strateškim zavisnostima — a ne samo na to gde je AI model hostovan.","\u002Fuploads\u002F2026\u002F10\u002Fsovereign-ai-control-of-models-data-infrastructure-and-dependencies-1791488833132-niy85x.webp","2026-10-08T15:45:00.000Z",{"id":2695,"slug":2696,"title":2697,"excerpt":2698,"featuredImage":2699,"publishedAt":2700},"475","managed-agent-harness-vs-self-hosted-agent-loop-what-you-gain-what-you-lose","Upravljani harness za agente naspram samostalno hostovane petlje agenta: Šta dobijate, šta gubite","“Samostalno hostovani agent” može značiti veoma različite arhitekture. Ovaj vodič razgraničava upravljani harness, samostalno hostovano okruženje za izvršavanje i potpuno samostalno upravljanu petlju agenta—i pokazuje koja je granica kontrole timovima zapravo potrebna.","\u002Fuploads\u002F2026\u002F09\u002Fmanaged-agent-harness-vs-self-hosted-agent-loop-what-you-gain-what-you-lose-1790352403475-kj10jh.webp","2026-09-25T12:05:00.000Z",{"id":2702,"slug":2703,"title":2704,"excerpt":2705,"featuredImage":2706,"publishedAt":2707},"459","ollama-is-not-the-product-building-production-ready-open-llm-applications","Ollama nije proizvod: Izgradnja aplikacija spremnih za produkciju sa otvorenim LLM-ovima","Pokretanje lokalnog modela pomoću Ollama-e je jednostavno. Izgradnja Open-LLM aplikacije spremne za produkciju je teža: zahteva RAG, kontrolu pristupa, apstrakciju provajdera, evaluaciju, logovanje, disciplinu puštanja u rad i kontrolisani aplikativni sloj oko modela.","\u002Fuploads\u002F2026\u002F06\u002Follama-is-not-the-product-building-production-ready-open-llm-applications-1782679361640-h0usqf.webp","2026-06-28T16:39:00.000Z",{"id":2709,"slug":2710,"title":2711,"excerpt":2712,"featuredImage":2713,"publishedAt":2714},"471","how-to-know-whether-an-ai-agent-actually-used-the-right-evidence","Kako znati da li je AI agent zaista koristio prave dokaze","AI agent može citirati izvore i ipak koristiti pogrešne dokaze. Ovaj članak predstavlja praktičnu metodu za proveru potkrepljenosti tvrdnji, autoriteta izvora, primenjivosti, porekla i toga da li su dokazi zaista uticali na odgovor.","\u002Fuploads\u002F2026\u002F09\u002Fhow-to-know-whether-an-ai-agent-actually-used-the-right-evidence-1790351317188-o5z9ve.webp","2026-09-25T11:47:00.000Z","fallback",[],[]]