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evidencia","how-far-is-an-ai-claim-from-the-evidence-evidence-distance","{\"time\":1792096946414,\"blocks\":[{\"id\":\"3yy7ldVlJW\",\"type\":\"header\",\"data\":{\"text\":\"Pregunta\",\"level\":2},\"tunes\":{}},{\"id\":\"DFH7zjvRs1\",\"type\":\"paragraph\",\"data\":{\"text\":\"Dos respuestas de IA pueden citar fuentes y aun así diferir enormemente en cuán directamente esas fuentes respaldan las afirmaciones que se hacen. ¿Cómo podemos describir esa diferencia?\"},\"tunes\":{}},{\"id\":\"W6hSwB47eE\",\"type\":\"paragraph\",\"data\":{\"text\":\"Este artículo utiliza el término Distancia de Evidencia para la distancia entre una afirmación y la evidencia directamente inspeccionable que la respalda.\"},\"tunes\":{}},{\"id\":\"mQSSJXx70y\",\"type\":\"quote\",\"data\":{\"text\":\"La Distancia de Evidencia es el número y la fuerza de las transformaciones, suposiciones y pasos inferenciales que separan una afirmación de la evidencia que puede verificarla directamente.\",\"caption\":\"Definición de trabajo — un concepto de razonamiento introducido aquí, no una métrica estandarizada.\",\"alignment\":\"left\"},\"tunes\":{}},{\"id\":\"EJLXZ4tek3\",\"type\":\"tableOfContents\",\"data\":{\"title\":\"Contents\",\"minLevel\":2,\"maxLevel\":3},\"tunes\":{}},{\"id\":\"UiksEpLxHy\",\"type\":\"header\",\"data\":{\"text\":\"Qué significa esto realmente\",\"level\":2},\"tunes\":{}},{\"id\":\"THxsig54tU\",\"type\":\"paragraph\",\"data\":{\"text\":\"Una cita demuestra que se hizo referencia a una fuente. No demuestra automáticamente que la fuente respalde directamente la oración junto a la cita.\"},\"tunes\":{}},{\"id\":\"EBUIHqCDTL\",\"type\":\"paragraph\",\"data\":{\"text\":\"Entre una observación original y una respuesta final de IA puede haber extracción, análisis sintáctico, fragmentación, recuperación, resumen, interpretación, agregación e inferencia.\"},\"tunes\":{}},{\"id\":\"YH0Z1_l-Qd\",\"type\":\"code\",\"data\":{\"code\":\"Primary evidence\\n   ↓\\nExtracted data\\n   ↓\\nRetrieved passage\\n   ↓\\nSummary\\n   ↓\\nInterpretation\\n   ↓\\nInference\\n   ↓\\nFinal claim\"},\"tunes\":{}},{\"id\":\"3sxybgrazw\",\"type\":\"paragraph\",\"data\":{\"text\":\"Cada paso puede ser legítimo. Pero cada paso también puede introducir distorsión.\"},\"tunes\":{}},{\"id\":\"x9g-QMXJHt\",\"type\":\"header\",\"data\":{\"text\":\"Ejemplo más simple\",\"level\":2},\"tunes\":{}},{\"id\":\"QKiVZxGaXs\",\"type\":\"paragraph\",\"data\":{\"text\":\"Supongamos que un sensor de temperatura reporta 38.2 °C.\"},\"tunes\":{}},{\"id\":\"P2W-fmoXxv\",\"type\":\"table\",\"data\":{\"withHeadings\":true,\"stretched\":false,\"content\":[[\"Afirmación\",\"Relación con la evidencia\",\"Distancia ilustrativa\"],[\"El sensor reporta 38.2 °C.\",\"Lectura directa\",\"Baja\"],[\"El sistema está funcionando más caliente que ayer.\",\"Requiere comparación con la lectura de ayer\",\"Moderada\"],[\"El sistema de enfriamiento está fallando.\",\"Requiere inferencia causal y explicaciones alternativas\",\"Alta\"]]},\"tunes\":{}},{\"id\":\"qgutUwvtGI\",\"type\":\"paragraph\",\"data\":{\"text\":\"El valor original del sensor respalda directamente la primera afirmación. Por sí solo no prueba la tercera.\"},\"tunes\":{}},{\"id\":\"u3Z0LSHvJI\",\"type\":\"code\",\"data\":{\"code\":\"Evidence exists\\n        ≠\\nEvidence directly supports every derived claim\"},\"tunes\":{}},{\"id\":\"6Wjj4bc2Ur\",\"type\":\"header\",\"data\":{\"text\":\"Dónde deja de funcionar el ejemplo\",\"level\":2},\"tunes\":{}},{\"id\":\"5HdfsRFKIz\",\"type\":\"paragraph\",\"data\":{\"text\":\"La Distancia de Evidencia no es simplemente un conteo de oraciones o pasos de procesamiento.\"},\"tunes\":{}},{\"id\":\"Ps2IHyYWy5\",\"type\":\"paragraph\",\"data\":{\"text\":\"Una transformación matemáticamente válida puede preservar la evidencia casi perfectamente, mientras que una sola interpretación especulativa puede crear un salto epistémico mucho mayor.\"},\"tunes\":{}},{\"id\":\"lfb_ikSI4h\",\"type\":\"paragraph\",\"data\":{\"text\":\"Por lo tanto, el concepto incluye tanto la distancia por transformación como la distancia por inferencia.\"},\"tunes\":{}},{\"id\":\"byp_y9f1xC\",\"type\":\"header\",\"data\":{\"text\":\"Respuesta directa\",\"level\":2},\"tunes\":{}},{\"id\":\"xjVlZfKRMH\",\"type\":\"paragraph\",\"data\":{\"text\":\"La distancia de evidencia aumenta cuando una afirmación depende de más transformaciones, más suposiciones, una procedencia más débil o una inferencia más fuerte de lo que la evidencia subyacente justifica directamente.\"},\"tunes\":{}},{\"id\":\"dtJcSurNQp\",\"type\":\"code\",\"data\":{\"code\":\"Short distance:\\nsource → claim\\n\\nLonger distance:\\nsource → extraction → summary → assumption → inference → claim\"},\"tunes\":{}},{\"id\":\"sF2BXEXTQA\",\"type\":\"paragraph\",\"data\":{\"text\":\"Una distancia mayor no hace automáticamente falsa una afirmación. Significa que la cadena de razonamiento requiere más validación.\"},\"tunes\":{}},{\"id\":\"HhBVUD4vvj\",\"type\":\"header\",\"data\":{\"text\":\"Por qué esto importa para la IA\",\"level\":2},\"tunes\":{}},{\"id\":\"hEL-KWDago\",\"type\":\"paragraph\",\"data\":{\"text\":\"Los LLM son particularmente buenos produciendo puentes fluidos entre piezas de información. Esa capacidad es útil, pero la fluidez puede ocultar cuánto se ha alejado la conclusión de la evidencia recuperada directamente.\"},\"tunes\":{}},{\"id\":\"YTJbtIaZY9\",\"type\":\"paragraph\",\"data\":{\"text\":\"Por lo tanto, una respuesta puede parecer bien fundamentada mientras contiene afirmaciones cuyo respaldo existe solo a través de varios pasos inferenciales no declarados.\"},\"tunes\":{}},{\"id\":\"j82nQVh6el\",\"type\":\"linkTool\",\"data\":{\"link\":\"https:\u002F\u002Fstajic.de\u002Fes\u002Fblog\u002Ffrom-research-protocol-to-a-general-ai-reasoning-framework\",\"meta\":{\"image\":{\"url\":\"\"},\"title\":\"Del protocolo de investigación a un marco general de razonamiento con IA\",\"description\":\"El razonamiento confiable requiere una separación explícita de evidencia, suposiciones, hipótesis y conclusiones.\"}},\"tunes\":{}},{\"id\":\"kI-hwlGq0H\",\"type\":\"header\",\"data\":{\"text\":\"Cuatro componentes de la distancia de evidencia\",\"level\":2},\"tunes\":{}},{\"id\":\"zjTK6tIWeV\",\"type\":\"header\",\"data\":{\"text\":\"1. Distancia de transformación\",\"level\":3},\"tunes\":{}},{\"id\":\"xGAseW8ryS\",\"type\":\"paragraph\",\"data\":{\"text\":\"¿Cuántas veces se ha convertido, resumido, normalizado o reformateado la evidencia original antes de llegar al modelo?\"},\"tunes\":{}},{\"id\":\"Er1KLn3mH1\",\"type\":\"header\",\"data\":{\"text\":\"2. Distancia inferencial\",\"level\":3},\"tunes\":{}},{\"id\":\"dK_FMsmj6f\",\"type\":\"paragraph\",\"data\":{\"text\":\"¿Cuántos pasos de razonamiento se requieren antes de que la evidencia respalde la conclusión final?\"},\"tunes\":{}},{\"id\":\"Fco5XqYtL7\",\"type\":\"header\",\"data\":{\"text\":\"3. Distancia de procedencia\",\"level\":3},\"tunes\":{}},{\"id\":\"2NvrSAECAj\",\"type\":\"paragraph\",\"data\":{\"text\":\"¿Todavía se puede rastrear la declaración recuperada hasta una fuente original identificable, o ha pasado por resúmenes y copias cuyo origen no está claro?\"},\"tunes\":{}},{\"id\":\"dcOlel19xg\",\"type\":\"header\",\"data\":{\"text\":\"4. Distancia de aplicabilidad\",\"level\":3},\"tunes\":{}},{\"id\":\"C2yjysA5SQ\",\"type\":\"paragraph\",\"data\":{\"text\":\"¿Cuánto razonamiento se requiere para establecer que la evidencia se aplica a esta versión exacta, usuario, jurisdicción, período de tiempo o situación?\"},\"tunes\":{}},{\"id\":\"idrHOTH5oi\",\"type\":\"header\",\"data\":{\"text\":\"Distancia de evidencia en RAG\",\"level\":2},\"tunes\":{}},{\"id\":\"Ij6olPwxPk\",\"type\":\"paragraph\",\"data\":{\"text\":\"RAG puede acortar una distancia importante: la distancia entre un modelo y el conocimiento externo.\"},\"tunes\":{}},{\"id\":\"tzoj249ypN\",\"type\":\"paragraph\",\"data\":{\"text\":\"No acorta automáticamente la distancia entre un pasaje recuperado y la afirmación final.\"},\"tunes\":{}},{\"id\":\"CrBtJYomgr\",\"type\":\"code\",\"data\":{\"code\":\"RAG solves:\\nmodel ↔ external information\\n\\nRAG does not automatically solve:\\nevidence ↔ justified conclusion\"},\"tunes\":{}},{\"id\":\"buAec_1hS8\",\"type\":\"linkTool\",\"data\":{\"link\":\"https:\u002F\u002Fstajic.de\u002Fes\u002Fblog\u002Fwhere-does-an-llm-get-its-data-rag-data-sources-in-python\",\"meta\":{\"image\":{\"url\":\"\"},\"title\":\"¿De dónde obtiene sus datos un LLM? Fuentes de datos de RAG en Python\",\"description\":\"La recuperación trae información externa al contexto. La Distancia de Evidencia pregunta qué sucede entre esa información y la afirmación que finalmente se produce.\"}},\"tunes\":{}},{\"id\":\"WaSr8UDfW5\",\"type\":\"header\",\"data\":{\"text\":\"Conexión con el Disparador de Recuperación\",\"level\":2},\"tunes\":{}},{\"id\":\"vWGO7iDMVw\",\"type\":\"paragraph\",\"data\":{\"text\":\"El Disparador de Recuperación pregunta si el conocimiento interno del modelo es suficiente.\"},\"tunes\":{}},{\"id\":\"lXQzdcZt2k\",\"type\":\"paragraph\",\"data\":{\"text\":\"La Distancia de Evidencia plantea una pregunta posterior: después de que se ha recuperado la evidencia, ¿qué tan directamente respalda lo que el sistema quiere afirmar?\"},\"tunes\":{}},{\"id\":\"RYiRLTFD_j\",\"type\":\"code\",\"data\":{\"code\":\"Need evidence?\\n   ↓\\nRetrieval Trigger\\n   ↓\\nRetrieve\\n   ↓\\nHow directly does evidence support the claim?\\n   ↓\\nEvidence Distance\"},\"tunes\":{}},{\"id\":\"uV7bAKLqyr\",\"type\":\"linkTool\",\"data\":{\"link\":\"https:\u002F\u002Fstajic.de\u002Fes\u002Fblog\u002Fwhen-should-an-ai-stop-trusting-its-own-knowledge-the-retrieval-trigger\",\"meta\":{\"image\":{\"url\":\"\"},\"title\":\"¿Cuándo debería una IA dejar de confiar en su propio conocimiento? — El Disparador de Recuperación\",\"description\":\"El disparador decide cuándo se necesita evidencia. La Distancia de Evidencia evalúa qué tan lejos va la afirmación final más allá de esa evidencia.\"}},\"tunes\":{}},{\"id\":\"-R3qa8KEBP\",\"type\":\"header\",\"data\":{\"text\":\"Un Diagnóstico Práctico\",\"level\":2},\"tunes\":{}},{\"id\":\"P1bb8kWXaY\",\"type\":\"paragraph\",\"data\":{\"text\":\"Para una afirmación importante generada por IA, reconstruye la cadena de respaldo hacia atrás.\"},\"tunes\":{}},{\"id\":\"POnrJXPEA9\",\"type\":\"code\",\"data\":{\"code\":\"CLAIM\\n  ↑\\nWhich inference produced it?\\n  ↑\\nWhich retrieved evidence supports that inference?\\n  ↑\\nWhich source produced that evidence?\\n  ↑\\nCan the original source still be inspected?\"},\"tunes\":{}},{\"id\":\"Yott2Cf5EO\",\"type\":\"paragraph\",\"data\":{\"text\":\"Si la cadena se rompe, el sistema ha encontrado un problema de trazabilidad de la evidencia.\"},\"tunes\":{}},{\"id\":\"OVCbPtBZ1d\",\"type\":\"paragraph\",\"data\":{\"text\":\"Si la cadena permanece intacta pero requiere varias suposiciones sólidas, la Distancia de Evidencia sigue siendo alta.\"},\"tunes\":{}},{\"id\":\"gb29DddHWp\",\"type\":\"header\",\"data\":{\"text\":\"Ejemplo: Datos de Producto\",\"level\":2},\"tunes\":{}},{\"id\":\"FIxrywhzzq\",\"type\":\"paragraph\",\"data\":{\"text\":\"Supongamos que una especificación de fabricante dice que una computadora portátil pesa 1,48 kg.\"},\"tunes\":{}},{\"id\":\"E5hSIoDZcQ\",\"type\":\"code\",\"data\":{\"code\":\"Manufacturer specification\\n→ 1.48 kg\\n→ \\\"The laptop weighs 1.48 kg\\\"\\n\\nLow Evidence Distance\"},\"tunes\":{}},{\"id\":\"iJ_Gyu7H7h\",\"type\":\"paragraph\",\"data\":{\"text\":\"Ahora considera:\"},\"tunes\":{}},{\"id\":\"2EXMzRG3Iy\",\"type\":\"code\",\"data\":{\"code\":\"Manufacturer specification\\n→ 1.48 kg\\n→ comparison with three competitors\\n→ assumption about travel behavior\\n→ \\\"This is the best laptop for frequent travelers\\\"\\n\\nMuch higher Evidence Distance\"},\"tunes\":{}},{\"id\":\"YC1ar2w8nT\",\"type\":\"paragraph\",\"data\":{\"text\":\"La especificación es real. La recomendación todavía está a varios pasos de razonamiento de distancia de ella.\"},\"tunes\":{}},{\"id\":\"907TCgXcDH\",\"type\":\"header\",\"data\":{\"text\":\"Ejemplo: Evidencia científica\",\"level\":2},\"tunes\":{}},{\"id\":\"iHn1xN4C0A\",\"type\":\"paragraph\",\"data\":{\"text\":\"Un artículo puede reportar una asociación observada. Una respuesta de IA puede comprimir eso incorrectamente en una afirmación causal.\"},\"tunes\":{}},{\"id\":\"kK96hlJPEL\",\"type\":\"code\",\"data\":{\"code\":\"Observed association\\n        ↓\\nInterpretation\\n        ↓\\nUnstated causal assumption\\n        ↓\\n\\\"X causes Y\\\"\"},\"tunes\":{}},{\"id\":\"tb3xwgWIZf\",\"type\":\"paragraph\",\"data\":{\"text\":\"El problema no es que no exista evidencia. El problema es que la afirmación final cruza una distancia evidencial mayor de la que el estudio respalda directamente.\"},\"tunes\":{}},{\"id\":\"5Wbn0eEZVR\",\"type\":\"header\",\"data\":{\"text\":\"Conexión con el contexto listo para agentes\",\"level\":2},\"tunes\":{}},{\"id\":\"u8mj_KJGth\",\"type\":\"paragraph\",\"data\":{\"text\":\"Los objetos de información estructurados pueden reducir la Distancia Evidencial evitable al preservar la identidad de la fuente, las unidades, las marcas de tiempo, los conflictos y la validez en lugar de forzar al modelo a reconstruirlos a partir de texto aplanado.\"},\"tunes\":{}},{\"id\":\"YgePKgz42S\",\"type\":\"linkTool\",\"data\":{\"link\":\"https:\u002F\u002Fstajic.de\u002Fes\u002Fportfolio\u002Ffrom-raw-data-to-verifiable-ai-context-a-practical-architecture-for-agent-ready-information\",\"meta\":{\"image\":{\"url\":\"\"},\"title\":\"De datos brutos a contexto de IA verificable: una arquitectura práctica para información lista para agentes\",\"description\":\"Preservar la procedencia y la validez antes de la recuperación reduce el número de pasos de reconstrucción no respaldados requeridos después.\"}},\"tunes\":{}},{\"id\":\"2XDizSeF8F\",\"type\":\"header\",\"data\":{\"text\":\"Una escala de trabajo simple\",\"level\":2},\"tunes\":{}},{\"id\":\"JNM1yoaqxn\",\"type\":\"table\",\"data\":{\"withHeadings\":true,\"stretched\":false,\"content\":[[\"Nivel\",\"Descripción\"],[\"ED0\",\"Hecho de la fuente reportado directamente\"],[\"ED1\",\"Transformación determinista simple o comparación directa\"],[\"ED2\",\"Un paso interpretativo significativo\"],[\"ED3\",\"Múltiples suposiciones o fuentes combinadas\"],[\"ED4\",\"Conclusión causal, predictiva o prescriptiva fuerte más allá de la evidencia directa\"]]},\"tunes\":{}},{\"id\":\"RKzhFVNKcX\",\"type\":\"paragraph\",\"data\":{\"text\":\"Esta escala es ilustrativa, no un estándar científico. Su propósito es forzar una pregunta explícita: ¿Cuánto tuvimos que agregar entre la evidencia y la afirmación?\"},\"tunes\":{}},{\"id\":\"Bst0OEoDYx\",\"type\":\"header\",\"data\":{\"text\":\"Conceptos erróneos\",\"level\":2},\"tunes\":{}},{\"id\":\"BkEQqALYWU\",\"type\":\"paragraph\",\"data\":{\"text\":\"Una cita significa que la Distancia Evidencial es baja. No. Una cita puede estar junto a una afirmación que excede sustancialmente lo que establece la fuente citada.\"},\"tunes\":{}},{\"id\":\"1dfoC3Xg1y\",\"type\":\"paragraph\",\"data\":{\"text\":\"Una Distancia Evidencial larga significa que la afirmación es incorrecta. No. Las conclusiones complejas pueden estar bien respaldadas, pero requieren una cadena de razonamiento más fuerte.\"},\"tunes\":{}},{\"id\":\"WQaE09Mu8s\",\"type\":\"paragraph\",\"data\":{\"text\":\"Las fuentes primarias eliminan la inferencia. No. La evidencia primaria todavía puede requerir interpretación.\"},\"tunes\":{}},{\"id\":\"DZGQyHJM7P\",\"type\":\"paragraph\",\"data\":{\"text\":\"Más fuentes reducen automáticamente la distancia. Diez repeticiones débiles de la misma afirmación no respaldada no crean evidencia directa.\"},\"tunes\":{}},{\"id\":\"7RdSxvU2cZ\",\"type\":\"header\",\"data\":{\"text\":\"Caso límite: Datos derivados\",\"level\":2},\"tunes\":{}},{\"id\":\"4leCmVV9i2\",\"type\":\"paragraph\",\"data\":{\"text\":\"Algunos valores derivados son más fiables que las descripciones humanas sin procesar. Un total calculado correctamente a partir de transacciones verificadas puede ser altamente confiable aunque esté a una transformación de distancia de los registros originales.\"},\"tunes\":{}},{\"id\":\"xJEGno15VD\",\"type\":\"paragraph\",\"data\":{\"text\":\"Por lo tanto, la Distancia de Evidencia mide la separación epistémica, no una preferencia simplista por datos no modificados.\"},\"tunes\":{}},{\"id\":\"yv0t-7epB1\",\"type\":\"header\",\"data\":{\"text\":\"Limitaciones\",\"level\":2},\"tunes\":{}},{\"id\":\"mbvy18WTDc\",\"type\":\"paragraph\",\"data\":{\"text\":\"La Distancia de Evidencia es un diagnóstico conceptual, no una puntuación cuantitativa universal.\"},\"tunes\":{}},{\"id\":\"cDMG-iQaBo\",\"type\":\"paragraph\",\"data\":{\"text\":\"Diferentes dominios requieren un tratamiento diferente de la inferencia. La prueba matemática, la evidencia clínica, los registros de software, el análisis de mercado y la interpretación histórica no pueden compartir un único umbral numérico universal.\"},\"tunes\":{}},{\"id\":\"o-VneTWprI\",\"type\":\"header\",\"data\":{\"text\":\"¿Qué cambiaría esta respuesta?\",\"level\":2},\"tunes\":{}},{\"id\":\"HkpDJxX3a9\",\"type\":\"paragraph\",\"data\":{\"text\":\"Los futuros sistemas de IA podrían generar grafos de procedencia explícitos, vínculos entre afirmaciones y evidencia verificables por máquina y rastros de razonamiento tipificados. Eso podría hacer que partes de la Distancia de Evidencia sean medibles automáticamente.\"},\"tunes\":{}},{\"id\":\"7KAXu_OXvQ\",\"type\":\"paragraph\",\"data\":{\"text\":\"El problema subyacente permanece: una afirmación puede alejarse más de su evidencia incluso mientras cada oración intermedia suena plausible.\"},\"tunes\":{}},{\"id\":\"N7lvDNCSwe\",\"type\":\"header\",\"data\":{\"text\":\"Conclusión\",\"level\":2},\"tunes\":{}},{\"id\":\"jXtqrl3puF\",\"type\":\"paragraph\",\"data\":{\"text\":\"La distinción importante no es meramente entre respuestas con fuentes y sin fuentes.\"},\"tunes\":{}},{\"id\":\"dMncz_725K\",\"type\":\"paragraph\",\"data\":{\"text\":\"Es entre afirmaciones que permanecen cerca de evidencia directamente inspeccionable y afirmaciones que dependen de cadenas cada vez más largas de interpretación y suposición.\"},\"tunes\":{}},{\"id\":\"xomimuOHS1\",\"type\":\"code\",\"data\":{\"code\":\"Evidence\\n   ↓\\nEvidence Distance\\n   ↓\\nReasoning\\n   ↓\\nAnswer Validity Boundary\"},\"tunes\":{}},{\"id\":\"ou36yORvOG\",\"type\":\"paragraph\",\"data\":{\"text\":\"La Distancia de Evidencia nos da un lenguaje para describir ese espacio.\"},\"tunes\":{}},{\"id\":\"9dB_8VqIod\",\"type\":\"header\",\"data\":{\"text\":\"Fuentes primarias\",\"level\":2},\"tunes\":{}},{\"id\":\"MizlIejidy\",\"type\":\"paragraph\",\"data\":{\"text\":\"Patrick Lewis et al., \u003Ca href=\\\"https:\u002F\u002Farxiv.org\u002Fabs\u002F2005.11401\\\" target=\\\"_blank\\\">Retrieval-Augmented Generation for Knowledge-Intensive NLP Tasks\u003C\u002Fa> (2020).\"},\"tunes\":{}},{\"id\":\"bUEQE2C5uT\",\"type\":\"paragraph\",\"data\":{\"text\":\"Nelson F. Liu et al., \u003Ca href=\\\"https:\u002F\u002Farxiv.org\u002Fabs\u002F2307.03172\\\" target=\\\"_blank\\\">Lost in the Middle: How Language Models Use Long Contexts\u003C\u002Fa> (2023).\"},\"tunes\":{}},{\"id\":\"To1TrW0-V9\",\"type\":\"paragraph\",\"data\":{\"text\":\"W3C, \u003Ca href=\\\"https:\u002F\u002Fwww.w3.org\u002FTR\u002Fprov-overview\u002F\\\" target=\\\"_blank\\\">PROV Overview\u003C\u002Fa>. Un modelo estandarizado para representar relaciones de procedencia.\"},\"tunes\":{}}],\"version\":\"2.31.6\"}",{"time":228,"blocks":229,"version":741},1792096946414,[230,236,242,247,255,262,267,272,277,283,288,293,298,320,325,330,335,340,345,350,355,360,365,370,375,380,385,396,401,406,411,416,421,426,431,436,441,446,451,456,461,470,475,480,485,490,499,504,509,514,519,524,529,534,539,544,549,554,559,564,569,574,579,584,593,598,621,626,631,636,641,646,651,656,661,666,671,676,681,686,691,696,701,706,711,716,721,726,731,736],{"id":231,"data":232,"type":42,"tunes":235},"3yy7ldVlJW",{"text":233,"level":234},"Pregunta",2,{},{"id":237,"data":238,"type":240,"tunes":241},"DFH7zjvRs1",{"text":239},"Dos respuestas de IA pueden citar fuentes y aun así diferir enormemente en cuán directamente esas fuentes respaldan las afirmaciones que se hacen. ¿Cómo podemos describir esa diferencia?","paragraph",{},{"id":243,"data":244,"type":240,"tunes":246},"W6hSwB47eE",{"text":245},"Este artículo utiliza el término Distancia de Evidencia para la distancia entre una afirmación y la evidencia directamente inspeccionable que la respalda.",{},{"id":248,"data":249,"type":253,"tunes":254},"mQSSJXx70y",{"text":250,"caption":251,"alignment":252},"La Distancia de Evidencia es el número y la fuerza de las transformaciones, suposiciones y pasos inferenciales que separan una afirmación de la evidencia que puede verificarla directamente.","Definición de trabajo — un concepto de razonamiento introducido aquí, no una métrica estandarizada.","left","quote",{},{"id":256,"data":257,"type":260,"tunes":261},"EJLXZ4tek3",{"title":258,"maxLevel":259,"minLevel":234},"Contents",3,"tableOfContents",{},{"id":263,"data":264,"type":42,"tunes":266},"UiksEpLxHy",{"text":265,"level":234},"Qué significa esto realmente",{},{"id":268,"data":269,"type":240,"tunes":271},"THxsig54tU",{"text":270},"Una cita demuestra que se hizo referencia a una fuente. No demuestra automáticamente que la fuente respalde directamente la oración junto a la cita.",{},{"id":273,"data":274,"type":240,"tunes":276},"EBUIHqCDTL",{"text":275},"Entre una observación original y una respuesta final de IA puede haber extracción, análisis sintáctico, fragmentación, recuperación, resumen, interpretación, agregación e inferencia.",{},{"id":278,"data":279,"type":281,"tunes":282},"YH0Z1_l-Qd",{"code":280},"Primary evidence\n   ↓\nExtracted data\n   ↓\nRetrieved passage\n   ↓\nSummary\n   ↓\nInterpretation\n   ↓\nInference\n   ↓\nFinal claim","code",{},{"id":284,"data":285,"type":240,"tunes":287},"3sxybgrazw",{"text":286},"Cada paso puede ser legítimo. Pero cada paso también puede introducir distorsión.",{},{"id":289,"data":290,"type":42,"tunes":292},"x9g-QMXJHt",{"text":291,"level":234},"Ejemplo más simple",{},{"id":294,"data":295,"type":240,"tunes":297},"QKiVZxGaXs",{"text":296},"Supongamos que un sensor de temperatura reporta 38.2 °C.",{},{"id":299,"data":300,"type":318,"tunes":319},"P2W-fmoXxv",{"content":301,"stretched":43,"withHeadings":14},[302,306,310,314],[303,304,305],"Afirmación","Relación con la evidencia","Distancia ilustrativa",[307,308,309],"El sensor reporta 38.2 °C.","Lectura directa","Baja",[311,312,313],"El sistema está funcionando más caliente que ayer.","Requiere comparación con la lectura de ayer","Moderada",[315,316,317],"El sistema de enfriamiento está fallando.","Requiere inferencia causal y explicaciones alternativas","Alta","table",{},{"id":321,"data":322,"type":240,"tunes":324},"qgutUwvtGI",{"text":323},"El valor original del sensor respalda directamente la primera afirmación. Por sí solo no prueba la tercera.",{},{"id":326,"data":327,"type":281,"tunes":329},"u3Z0LSHvJI",{"code":328},"Evidence exists\n        ≠\nEvidence directly supports every derived claim",{},{"id":331,"data":332,"type":42,"tunes":334},"6Wjj4bc2Ur",{"text":333,"level":234},"Dónde deja de funcionar el ejemplo",{},{"id":336,"data":337,"type":240,"tunes":339},"5HdfsRFKIz",{"text":338},"La Distancia de Evidencia no es simplemente un conteo de oraciones o pasos de procesamiento.",{},{"id":341,"data":342,"type":240,"tunes":344},"Ps2IHyYWy5",{"text":343},"Una transformación matemáticamente válida puede preservar la evidencia casi perfectamente, mientras que una sola interpretación especulativa puede crear un salto epistémico mucho mayor.",{},{"id":346,"data":347,"type":240,"tunes":349},"lfb_ikSI4h",{"text":348},"Por lo tanto, el concepto incluye tanto la distancia por transformación como la distancia por inferencia.",{},{"id":351,"data":352,"type":42,"tunes":354},"byp_y9f1xC",{"text":353,"level":234},"Respuesta directa",{},{"id":356,"data":357,"type":240,"tunes":359},"xjVlZfKRMH",{"text":358},"La distancia de evidencia aumenta cuando una afirmación depende de más transformaciones, más suposiciones, una procedencia más débil o una inferencia más fuerte de lo que la evidencia subyacente justifica directamente.",{},{"id":361,"data":362,"type":281,"tunes":364},"dtJcSurNQp",{"code":363},"Short distance:\nsource → claim\n\nLonger distance:\nsource → extraction → summary → assumption → inference → claim",{},{"id":366,"data":367,"type":240,"tunes":369},"sF2BXEXTQA",{"text":368},"Una distancia mayor no hace automáticamente falsa una afirmación. Significa que la cadena de razonamiento requiere más validación.",{},{"id":371,"data":372,"type":42,"tunes":374},"HhBVUD4vvj",{"text":373,"level":234},"Por qué esto importa para la IA",{},{"id":376,"data":377,"type":240,"tunes":379},"hEL-KWDago",{"text":378},"Los LLM son particularmente buenos produciendo puentes fluidos entre piezas de información. Esa capacidad es útil, pero la fluidez puede ocultar cuánto se ha alejado la conclusión de la evidencia recuperada directamente.",{},{"id":381,"data":382,"type":240,"tunes":384},"YTJbtIaZY9",{"text":383},"Por lo tanto, una respuesta puede parecer bien fundamentada mientras contiene afirmaciones cuyo respaldo existe solo a través de varios pasos inferenciales no declarados.",{},{"id":386,"data":387,"type":394,"tunes":395},"j82nQVh6el",{"link":388,"meta":389},"https:\u002F\u002Fstajic.de\u002Fes\u002Fblog\u002Ffrom-research-protocol-to-a-general-ai-reasoning-framework",{"image":390,"title":392,"description":393},{"url":391},"","Del protocolo de investigación a un marco general de razonamiento con IA","El razonamiento confiable requiere una separación explícita de evidencia, suposiciones, hipótesis y conclusiones.","linkTool",{},{"id":397,"data":398,"type":42,"tunes":400},"kI-hwlGq0H",{"text":399,"level":234},"Cuatro componentes de la distancia de evidencia",{},{"id":402,"data":403,"type":42,"tunes":405},"zjTK6tIWeV",{"text":404,"level":259},"1. Distancia de transformación",{},{"id":407,"data":408,"type":240,"tunes":410},"xGAseW8ryS",{"text":409},"¿Cuántas veces se ha convertido, resumido, normalizado o reformateado la evidencia original antes de llegar al modelo?",{},{"id":412,"data":413,"type":42,"tunes":415},"Er1KLn3mH1",{"text":414,"level":259},"2. Distancia inferencial",{},{"id":417,"data":418,"type":240,"tunes":420},"dK_FMsmj6f",{"text":419},"¿Cuántos pasos de razonamiento se requieren antes de que la evidencia respalde la conclusión final?",{},{"id":422,"data":423,"type":42,"tunes":425},"Fco5XqYtL7",{"text":424,"level":259},"3. Distancia de procedencia",{},{"id":427,"data":428,"type":240,"tunes":430},"2NvrSAECAj",{"text":429},"¿Todavía se puede rastrear la declaración recuperada hasta una fuente original identificable, o ha pasado por resúmenes y copias cuyo origen no está claro?",{},{"id":432,"data":433,"type":42,"tunes":435},"dcOlel19xg",{"text":434,"level":259},"4. Distancia de aplicabilidad",{},{"id":437,"data":438,"type":240,"tunes":440},"C2yjysA5SQ",{"text":439},"¿Cuánto razonamiento se requiere para establecer que la evidencia se aplica a esta versión exacta, usuario, jurisdicción, período de tiempo o situación?",{},{"id":442,"data":443,"type":42,"tunes":445},"idrHOTH5oi",{"text":444,"level":234},"Distancia de evidencia en RAG",{},{"id":447,"data":448,"type":240,"tunes":450},"Ij6olPwxPk",{"text":449},"RAG puede acortar una distancia importante: la distancia entre un modelo y el conocimiento externo.",{},{"id":452,"data":453,"type":240,"tunes":455},"tzoj249ypN",{"text":454},"No acorta automáticamente la distancia entre un pasaje recuperado y la afirmación final.",{},{"id":457,"data":458,"type":281,"tunes":460},"CrBtJYomgr",{"code":459},"RAG solves:\nmodel ↔ external information\n\nRAG does not automatically solve:\nevidence ↔ justified conclusion",{},{"id":462,"data":463,"type":394,"tunes":469},"buAec_1hS8",{"link":464,"meta":465},"https:\u002F\u002Fstajic.de\u002Fes\u002Fblog\u002Fwhere-does-an-llm-get-its-data-rag-data-sources-in-python",{"image":466,"title":467,"description":468},{"url":391},"¿De dónde obtiene sus datos un LLM? Fuentes de datos de RAG en Python","La recuperación trae información externa al contexto. La Distancia de Evidencia pregunta qué sucede entre esa información y la afirmación que finalmente se produce.",{},{"id":471,"data":472,"type":42,"tunes":474},"WaSr8UDfW5",{"text":473,"level":234},"Conexión con el Disparador de Recuperación",{},{"id":476,"data":477,"type":240,"tunes":479},"vWGO7iDMVw",{"text":478},"El Disparador de Recuperación pregunta si el conocimiento interno del modelo es suficiente.",{},{"id":481,"data":482,"type":240,"tunes":484},"lXQzdcZt2k",{"text":483},"La Distancia de Evidencia plantea una pregunta posterior: después de que se ha recuperado la evidencia, ¿qué tan directamente respalda lo que el sistema quiere afirmar?",{},{"id":486,"data":487,"type":281,"tunes":489},"RYiRLTFD_j",{"code":488},"Need evidence?\n   ↓\nRetrieval Trigger\n   ↓\nRetrieve\n   ↓\nHow directly does evidence support the claim?\n   ↓\nEvidence Distance",{},{"id":491,"data":492,"type":394,"tunes":498},"uV7bAKLqyr",{"link":493,"meta":494},"https:\u002F\u002Fstajic.de\u002Fes\u002Fblog\u002Fwhen-should-an-ai-stop-trusting-its-own-knowledge-the-retrieval-trigger",{"image":495,"title":496,"description":497},{"url":391},"¿Cuándo debería una IA dejar de confiar en su propio conocimiento? — El Disparador de Recuperación","El disparador decide cuándo se necesita evidencia. La Distancia de Evidencia evalúa qué tan lejos va la afirmación final más allá de esa evidencia.",{},{"id":500,"data":501,"type":42,"tunes":503},"-R3qa8KEBP",{"text":502,"level":234},"Un Diagnóstico Práctico",{},{"id":505,"data":506,"type":240,"tunes":508},"P1bb8kWXaY",{"text":507},"Para una afirmación importante generada por IA, reconstruye la cadena de respaldo hacia atrás.",{},{"id":510,"data":511,"type":281,"tunes":513},"POnrJXPEA9",{"code":512},"CLAIM\n  ↑\nWhich inference produced it?\n  ↑\nWhich retrieved evidence supports that inference?\n  ↑\nWhich source produced that evidence?\n  ↑\nCan the original source still be inspected?",{},{"id":515,"data":516,"type":240,"tunes":518},"Yott2Cf5EO",{"text":517},"Si la cadena se rompe, el sistema ha encontrado un problema de trazabilidad de la evidencia.",{},{"id":520,"data":521,"type":240,"tunes":523},"OVCbPtBZ1d",{"text":522},"Si la cadena permanece intacta pero requiere varias suposiciones sólidas, la Distancia de Evidencia sigue siendo alta.",{},{"id":525,"data":526,"type":42,"tunes":528},"gb29DddHWp",{"text":527,"level":234},"Ejemplo: Datos de Producto",{},{"id":530,"data":531,"type":240,"tunes":533},"FIxrywhzzq",{"text":532},"Supongamos que una especificación de fabricante dice que una computadora portátil pesa 1,48 kg.",{},{"id":535,"data":536,"type":281,"tunes":538},"E5hSIoDZcQ",{"code":537},"Manufacturer specification\n→ 1.48 kg\n→ \"The laptop weighs 1.48 kg\"\n\nLow Evidence Distance",{},{"id":540,"data":541,"type":240,"tunes":543},"iJ_Gyu7H7h",{"text":542},"Ahora considera:",{},{"id":545,"data":546,"type":281,"tunes":548},"2EXMzRG3Iy",{"code":547},"Manufacturer specification\n→ 1.48 kg\n→ comparison with three competitors\n→ assumption about travel behavior\n→ \"This is the best laptop for frequent travelers\"\n\nMuch higher Evidence Distance",{},{"id":550,"data":551,"type":240,"tunes":553},"YC1ar2w8nT",{"text":552},"La especificación es real. La recomendación todavía está a varios pasos de razonamiento de distancia de ella.",{},{"id":555,"data":556,"type":42,"tunes":558},"907TCgXcDH",{"text":557,"level":234},"Ejemplo: Evidencia científica",{},{"id":560,"data":561,"type":240,"tunes":563},"iHn1xN4C0A",{"text":562},"Un artículo puede reportar una asociación observada. Una respuesta de IA puede comprimir eso incorrectamente en una afirmación causal.",{},{"id":565,"data":566,"type":281,"tunes":568},"kK96hlJPEL",{"code":567},"Observed association\n        ↓\nInterpretation\n        ↓\nUnstated causal assumption\n        ↓\n\"X causes Y\"",{},{"id":570,"data":571,"type":240,"tunes":573},"tb3xwgWIZf",{"text":572},"El problema no es que no exista evidencia. El problema es que la afirmación final cruza una distancia evidencial mayor de la que el estudio respalda directamente.",{},{"id":575,"data":576,"type":42,"tunes":578},"5Wbn0eEZVR",{"text":577,"level":234},"Conexión con el contexto listo para agentes",{},{"id":580,"data":581,"type":240,"tunes":583},"u8mj_KJGth",{"text":582},"Los objetos de información estructurados pueden reducir la Distancia Evidencial evitable al preservar la identidad de la fuente, las unidades, las marcas de tiempo, los conflictos y la validez en lugar de forzar al modelo a reconstruirlos a partir de texto aplanado.",{},{"id":585,"data":586,"type":394,"tunes":592},"YgePKgz42S",{"link":587,"meta":588},"https:\u002F\u002Fstajic.de\u002Fes\u002Fportfolio\u002Ffrom-raw-data-to-verifiable-ai-context-a-practical-architecture-for-agent-ready-information",{"image":589,"title":590,"description":591},{"url":391},"De datos brutos a contexto de IA verificable: una arquitectura práctica para información lista para agentes","Preservar la procedencia y la validez antes de la recuperación reduce el número de pasos de reconstrucción no respaldados requeridos después.",{},{"id":594,"data":595,"type":42,"tunes":597},"2XDizSeF8F",{"text":596,"level":234},"Una escala de trabajo simple",{},{"id":599,"data":600,"type":318,"tunes":620},"JNM1yoaqxn",{"content":601,"stretched":43,"withHeadings":14},[602,605,608,611,614,617],[603,604],"Nivel","Descripción",[606,607],"ED0","Hecho de la fuente reportado directamente",[609,610],"ED1","Transformación determinista simple o comparación directa",[612,613],"ED2","Un paso interpretativo significativo",[615,616],"ED3","Múltiples suposiciones o fuentes combinadas",[618,619],"ED4","Conclusión causal, predictiva o prescriptiva fuerte más allá de la evidencia directa",{},{"id":622,"data":623,"type":240,"tunes":625},"RKzhFVNKcX",{"text":624},"Esta escala es ilustrativa, no un estándar científico. Su propósito es forzar una pregunta explícita: ¿Cuánto tuvimos que agregar entre la evidencia y la afirmación?",{},{"id":627,"data":628,"type":42,"tunes":630},"Bst0OEoDYx",{"text":629,"level":234},"Conceptos erróneos",{},{"id":632,"data":633,"type":240,"tunes":635},"BkEQqALYWU",{"text":634},"Una cita significa que la Distancia Evidencial es baja. No. Una cita puede estar junto a una afirmación que excede sustancialmente lo que establece la fuente citada.",{},{"id":637,"data":638,"type":240,"tunes":640},"1dfoC3Xg1y",{"text":639},"Una Distancia Evidencial larga significa que la afirmación es incorrecta. No. Las conclusiones complejas pueden estar bien respaldadas, pero requieren una cadena de razonamiento más fuerte.",{},{"id":642,"data":643,"type":240,"tunes":645},"WQaE09Mu8s",{"text":644},"Las fuentes primarias eliminan la inferencia. No. La evidencia primaria todavía puede requerir interpretación.",{},{"id":647,"data":648,"type":240,"tunes":650},"DZGQyHJM7P",{"text":649},"Más fuentes reducen automáticamente la distancia. Diez repeticiones débiles de la misma afirmación no respaldada no crean evidencia directa.",{},{"id":652,"data":653,"type":42,"tunes":655},"7RdSxvU2cZ",{"text":654,"level":234},"Caso límite: Datos derivados",{},{"id":657,"data":658,"type":240,"tunes":660},"4leCmVV9i2",{"text":659},"Algunos valores derivados son más fiables que las descripciones humanas sin procesar. Un total calculado correctamente a partir de transacciones verificadas puede ser altamente confiable aunque esté a una transformación de distancia de los registros originales.",{},{"id":662,"data":663,"type":240,"tunes":665},"xJEGno15VD",{"text":664},"Por lo tanto, la Distancia de Evidencia mide la separación epistémica, no una preferencia simplista por datos no modificados.",{},{"id":667,"data":668,"type":42,"tunes":670},"yv0t-7epB1",{"text":669,"level":234},"Limitaciones",{},{"id":672,"data":673,"type":240,"tunes":675},"mbvy18WTDc",{"text":674},"La Distancia de Evidencia es un diagnóstico conceptual, no una puntuación cuantitativa universal.",{},{"id":677,"data":678,"type":240,"tunes":680},"cDMG-iQaBo",{"text":679},"Diferentes dominios requieren un tratamiento diferente de la inferencia. La prueba matemática, la evidencia clínica, los registros de software, el análisis de mercado y la interpretación histórica no pueden compartir un único umbral numérico universal.",{},{"id":682,"data":683,"type":42,"tunes":685},"o-VneTWprI",{"text":684,"level":234},"¿Qué cambiaría esta respuesta?",{},{"id":687,"data":688,"type":240,"tunes":690},"HkpDJxX3a9",{"text":689},"Los futuros sistemas de IA podrían generar grafos de procedencia explícitos, vínculos entre afirmaciones y evidencia verificables por máquina y rastros de razonamiento tipificados. Eso podría hacer que partes de la Distancia de Evidencia sean medibles automáticamente.",{},{"id":692,"data":693,"type":240,"tunes":695},"7KAXu_OXvQ",{"text":694},"El problema subyacente permanece: una afirmación puede alejarse más de su evidencia incluso mientras cada oración intermedia suena plausible.",{},{"id":697,"data":698,"type":42,"tunes":700},"N7lvDNCSwe",{"text":699,"level":234},"Conclusión",{},{"id":702,"data":703,"type":240,"tunes":705},"jXtqrl3puF",{"text":704},"La distinción importante no es meramente entre respuestas con fuentes y sin fuentes.",{},{"id":707,"data":708,"type":240,"tunes":710},"dMncz_725K",{"text":709},"Es entre afirmaciones que permanecen cerca de evidencia directamente inspeccionable y afirmaciones que dependen de cadenas cada vez más largas de interpretación y suposición.",{},{"id":712,"data":713,"type":281,"tunes":715},"xomimuOHS1",{"code":714},"Evidence\n   ↓\nEvidence Distance\n   ↓\nReasoning\n   ↓\nAnswer Validity Boundary",{},{"id":717,"data":718,"type":240,"tunes":720},"ou36yORvOG",{"text":719},"La Distancia de Evidencia nos da un lenguaje para describir ese espacio.",{},{"id":722,"data":723,"type":42,"tunes":725},"9dB_8VqIod",{"text":724,"level":234},"Fuentes primarias",{},{"id":727,"data":728,"type":240,"tunes":730},"MizlIejidy",{"text":729},"Patrick Lewis et al., \u003Ca href=\"https:\u002F\u002Farxiv.org\u002Fabs\u002F2005.11401\" target=\"_blank\">Retrieval-Augmented Generation for Knowledge-Intensive NLP Tasks\u003C\u002Fa> (2020).",{},{"id":732,"data":733,"type":240,"tunes":735},"bUEQE2C5uT",{"text":734},"Nelson F. Liu et al., \u003Ca href=\"https:\u002F\u002Farxiv.org\u002Fabs\u002F2307.03172\" target=\"_blank\">Lost in the Middle: How Language Models Use Long Contexts\u003C\u002Fa> (2023).",{},{"id":737,"data":738,"type":240,"tunes":740},"To1TrW0-V9",{"text":739},"W3C, \u003Ca href=\"https:\u002F\u002Fwww.w3.org\u002FTR\u002Fprov-overview\u002F\" target=\"_blank\">PROV Overview\u003C\u002Fa>. Un modelo estandarizado para representar relaciones de procedencia.",{},"2.31.6","No todas las respuestas respaldadas están igualmente cerca de su evidencia. La Distancia a la Evidencia es una forma práctica de describir cuántas transformaciones, resúmenes, suposiciones y pasos inferenciales separan una afirmación de IA de la evidencia directamente inspeccionable.","\u002Fuploads\u002F2026\u002F10\u002Fhow-far-is-an-ai-claim-from-the-evidence-evidence-distance-1792095863209-3c1mog.webp","how-far-is-an-ai-claim-from-the-evidence-evidence-distance-1792095863209-3c1mog","PUBLISHED","2026-10-15T00:23:00.000Z","2026-10-15T20:23:08.634Z","2026-10-15T20:43:52.893Z",{"en":750,"de":751,"sr":752,"es":753,"fr":754,"it":755,"ru":756,"zh":757},"\u002Fblog\u002Fhow-far-is-an-ai-claim-from-the-evidence-evidence-distance","\u002Fde\u002Fblog\u002Fhow-far-is-an-ai-claim-from-the-evidence-evidence-distance","\u002Fsr\u002Fblog\u002Fhow-far-is-an-ai-claim-from-the-evidence-evidence-distance","\u002Fes\u002Fblog\u002Fhow-far-is-an-ai-claim-from-the-evidence-evidence-distance","\u002Ffr\u002Fblog\u002Fhow-far-is-an-ai-claim-from-the-evidence-evidence-distance","\u002Fit\u002Fblog\u002Fhow-far-is-an-ai-claim-from-the-evidence-evidence-distance","\u002Fru\u002Fblog\u002Fhow-far-is-an-ai-claim-from-the-evidence-evidence-distance","\u002Fzh\u002Fblog\u002Fhow-far-is-an-ai-claim-from-the-evidence-evidence-distance",[759,763],{"id":760,"name":761,"slug":762},68,"Riesgos, controles y evidencias","risks-and-controls",{"id":764,"name":765,"slug":766},49,"Controles y evidencias","controls",{"id":768,"login":769,"email":391,"displayName":770},"20","rooth8233","Aleksandar Stajić",[772,1263],{"lang":773,"title":774,"content":775,"contentJson":776,"excerpt":1262},"en","How Far Is an AI Claim From the Evidence? — Evidence Distance","{\"time\":1792096795538,\"blocks\":[{\"id\":\"g-2Ciyj8LJ\",\"type\":\"header\",\"data\":{\"text\":\"Question\",\"level\":2},\"tunes\":{}},{\"id\":\"SQWD7UraiX\",\"type\":\"paragraph\",\"data\":{\"text\":\"Two AI answers can both cite sources and still differ greatly in how directly those sources support the claims being made. How can we describe that difference?\"},\"tunes\":{}},{\"id\":\"Sj1fx2865j\",\"type\":\"paragraph\",\"data\":{\"text\":\"This article uses the term Evidence Distance for the distance between a claim and the directly inspectable evidence that supports it.\"},\"tunes\":{}},{\"id\":\"d5th-A-mq5\",\"type\":\"quote\",\"data\":{\"text\":\"Evidence Distance is the number and strength of transformations, assumptions and inferential steps separating a claim from the evidence that can directly verify it.\",\"caption\":\"Working definition — a reasoning concept introduced here, not a standardized metric.\",\"alignment\":\"left\"},\"tunes\":{}},{\"id\":\"X81jXC72FC\",\"type\":\"tableOfContents\",\"data\":{\"title\":\"Contents\",\"minLevel\":2,\"maxLevel\":3},\"tunes\":{}},{\"id\":\"_pZcnPEoil\",\"type\":\"header\",\"data\":{\"text\":\"What This Really Means\",\"level\":2},\"tunes\":{}},{\"id\":\"F5odO7xyha\",\"type\":\"paragraph\",\"data\":{\"text\":\"A citation proves that a source was referenced. It does not automatically prove that the source directly supports the sentence beside the citation.\"},\"tunes\":{}},{\"id\":\"8Gqlg-ArJS\",\"type\":\"paragraph\",\"data\":{\"text\":\"Between an original observation and a final AI answer there may be extraction, parsing, chunking, retrieval, summarization, interpretation, aggregation and inference.\"},\"tunes\":{}},{\"id\":\"lXkL5LV9ab\",\"type\":\"code\",\"data\":{\"code\":\"Primary evidence\\n   ↓\\nExtracted data\\n   ↓\\nRetrieved passage\\n   ↓\\nSummary\\n   ↓\\nInterpretation\\n   ↓\\nInference\\n   ↓\\nFinal claim\"},\"tunes\":{}},{\"id\":\"Z-8GPQIpCI\",\"type\":\"paragraph\",\"data\":{\"text\":\"Every step can be legitimate. But every step can also introduce distortion.\"},\"tunes\":{}},{\"id\":\"aXMwTGZ8Ia\",\"type\":\"header\",\"data\":{\"text\":\"Simplest Example\",\"level\":2},\"tunes\":{}},{\"id\":\"bZlU7KD3M2\",\"type\":\"paragraph\",\"data\":{\"text\":\"Suppose a temperature sensor reports 38.2 °C.\"},\"tunes\":{}},{\"id\":\"SP87UUPaU3\",\"type\":\"table\",\"data\":{\"withHeadings\":true,\"stretched\":false,\"content\":[[\"Claim\",\"Relationship to evidence\",\"Illustrative distance\"],[\"The sensor reports 38.2 °C.\",\"Direct reading\",\"Low\"],[\"The system is running hotter than yesterday.\",\"Requires comparison with yesterday's reading\",\"Moderate\"],[\"The cooling system is failing.\",\"Requires causal inference and alternative explanations\",\"High\"]]},\"tunes\":{}},{\"id\":\"eYar059d69\",\"type\":\"paragraph\",\"data\":{\"text\":\"The original sensor value directly supports the first claim. It does not by itself prove the third.\"},\"tunes\":{}},{\"id\":\"eCnFlByvHt\",\"type\":\"code\",\"data\":{\"code\":\"Evidence exists\\n        ≠\\nEvidence directly supports every derived claim\"},\"tunes\":{}},{\"id\":\"aXGkK6BpkF\",\"type\":\"header\",\"data\":{\"text\":\"Where the Example Stops Working\",\"level\":2},\"tunes\":{}},{\"id\":\"lAwb9QZ_yB\",\"type\":\"paragraph\",\"data\":{\"text\":\"Evidence Distance is not simply a count of sentences or processing steps.\"},\"tunes\":{}},{\"id\":\"-SzYko9oIH\",\"type\":\"paragraph\",\"data\":{\"text\":\"One mathematically valid transformation may preserve evidence almost perfectly, while a single speculative interpretation may create a much larger epistemic jump.\"},\"tunes\":{}},{\"id\":\"XOQloBndLw\",\"type\":\"paragraph\",\"data\":{\"text\":\"The concept therefore includes both distance by transformation and distance by inference.\"},\"tunes\":{}},{\"id\":\"EOXbS4TAOR\",\"type\":\"header\",\"data\":{\"text\":\"Direct Answer\",\"level\":2},\"tunes\":{}},{\"id\":\"HcVL8vGM74\",\"type\":\"paragraph\",\"data\":{\"text\":\"Evidence Distance increases when a claim depends on more transformations, more assumptions, weaker provenance or stronger inference than the underlying evidence directly warrants.\"},\"tunes\":{}},{\"id\":\"_f7WXjUOsS\",\"type\":\"code\",\"data\":{\"code\":\"Short distance:\\nsource → claim\\n\\nLonger distance:\\nsource → extraction → summary → assumption → inference → claim\"},\"tunes\":{}},{\"id\":\"zUEC-cDhqm\",\"type\":\"paragraph\",\"data\":{\"text\":\"A longer distance does not automatically make a claim false. It means the reasoning chain requires more validation.\"},\"tunes\":{}},{\"id\":\"ix7TUGNz_W\",\"type\":\"header\",\"data\":{\"text\":\"Why This Matters for AI\",\"level\":2},\"tunes\":{}},{\"id\":\"wrAy5PLtLB\",\"type\":\"paragraph\",\"data\":{\"text\":\"LLMs are particularly good at producing fluent bridges between pieces of information. That capability is useful, but fluency can hide how far the conclusion has moved beyond the directly retrieved evidence.\"},\"tunes\":{}},{\"id\":\"jJseLuTWdv\",\"type\":\"paragraph\",\"data\":{\"text\":\"An answer can therefore look well sourced while containing claims whose support exists only through several unstated inferential steps.\"},\"tunes\":{}},{\"id\":\"ox8fok9dzs\",\"type\":\"linkTool\",\"data\":{\"link\":\"https:\u002F\u002Fstajic.de\u002Fblog\u002Ffrom-research-protocol-to-a-general-ai-reasoning-framework\",\"meta\":{\"image\":{\"url\":\"\"},\"title\":\"From Research Protocol to a General AI Reasoning Framework\",\"description\":\"Reliable reasoning requires explicit separation of evidence, assumptions, hypotheses and conclusions.\"}},\"tunes\":{}},{\"id\":\"6ipse6YG1U\",\"type\":\"header\",\"data\":{\"text\":\"Four Components of Evidence Distance\",\"level\":2},\"tunes\":{}},{\"id\":\"P4xQ7o96dR\",\"type\":\"header\",\"data\":{\"text\":\"1. Transformation Distance\",\"level\":3},\"tunes\":{}},{\"id\":\"dvN8TUNEYG\",\"type\":\"paragraph\",\"data\":{\"text\":\"How many times has the original evidence been converted, summarized, normalized or reformatted before reaching the model?\"},\"tunes\":{}},{\"id\":\"dXofACF9_x\",\"type\":\"header\",\"data\":{\"text\":\"2. Inferential Distance\",\"level\":3},\"tunes\":{}},{\"id\":\"kkEzV3ydId\",\"type\":\"paragraph\",\"data\":{\"text\":\"How many reasoning steps are required before the evidence supports the final conclusion?\"},\"tunes\":{}},{\"id\":\"PPYB0RuX5_\",\"type\":\"header\",\"data\":{\"text\":\"3. Provenance Distance\",\"level\":3},\"tunes\":{}},{\"id\":\"cAVBYebJr7\",\"type\":\"paragraph\",\"data\":{\"text\":\"Can the retrieved statement still be traced to an identifiable original source, or has it passed through summaries and copies whose origin is unclear?\"},\"tunes\":{}},{\"id\":\"254N_99Z7X\",\"type\":\"header\",\"data\":{\"text\":\"4. Applicability Distance\",\"level\":3},\"tunes\":{}},{\"id\":\"to3KWv5WSK\",\"type\":\"paragraph\",\"data\":{\"text\":\"How much reasoning is required to establish that the evidence applies to this exact version, user, jurisdiction, time period or situation?\"},\"tunes\":{}},{\"id\":\"3WnVPQ6wqw\",\"type\":\"header\",\"data\":{\"text\":\"Evidence Distance in RAG\",\"level\":2},\"tunes\":{}},{\"id\":\"r8s-75EBhA\",\"type\":\"paragraph\",\"data\":{\"text\":\"RAG can shorten one important distance: the distance between a model and external knowledge.\"},\"tunes\":{}},{\"id\":\"I_8h52A3Yc\",\"type\":\"paragraph\",\"data\":{\"text\":\"It does not automatically shorten the distance between a retrieved passage and the final claim.\"},\"tunes\":{}},{\"id\":\"jDDRUu2FUK\",\"type\":\"code\",\"data\":{\"code\":\"RAG solves:\\nmodel ↔ external information\\n\\nRAG does not automatically solve:\\nevidence ↔ justified conclusion\"},\"tunes\":{}},{\"id\":\"rm14qIVB1M\",\"type\":\"linkTool\",\"data\":{\"link\":\"https:\u002F\u002Fstajic.de\u002Fblog\u002Fwhere-does-an-llm-get-its-data-rag-data-sources-in-python\",\"meta\":{\"image\":{\"url\":\"\"},\"title\":\"Where Does an LLM Get Its Data? RAG Data Sources in Python\",\"description\":\"Retrieval brings external information into context. Evidence Distance asks what happens between that information and the claim eventually produced.\"}},\"tunes\":{}},{\"id\":\"CRUZDXYloa\",\"type\":\"header\",\"data\":{\"text\":\"Connection to the Retrieval Trigger\",\"level\":2},\"tunes\":{}},{\"id\":\"-TPiYJ569c\",\"type\":\"paragraph\",\"data\":{\"text\":\"The Retrieval Trigger asks whether internal model knowledge is sufficient.\"},\"tunes\":{}},{\"id\":\"_VjLi5fs4x\",\"type\":\"paragraph\",\"data\":{\"text\":\"Evidence Distance asks a later question: after evidence has been retrieved, how directly does it support what the system wants to claim?\"},\"tunes\":{}},{\"id\":\"EImPw6i617\",\"type\":\"code\",\"data\":{\"code\":\"Need evidence?\\n   ↓\\nRetrieval Trigger\\n   ↓\\nRetrieve\\n   ↓\\nHow directly does evidence support the claim?\\n   ↓\\nEvidence Distance\"},\"tunes\":{}},{\"id\":\"ZqyhB2bFtR\",\"type\":\"linkTool\",\"data\":{\"link\":\"https:\u002F\u002Fstajic.de\u002Fblog\u002Fwhen-should-an-ai-stop-trusting-its-own-knowledge-the-retrieval-trigger\",\"meta\":{\"image\":{\"url\":\"\"},\"title\":\"When Should an AI Stop Trusting Its Own Knowledge? — The Retrieval Trigger\",\"description\":\"The trigger decides when evidence is needed. Evidence Distance evaluates how far the eventual claim moves beyond that evidence.\"}},\"tunes\":{}},{\"id\":\"ST2vnm6vwF\",\"type\":\"header\",\"data\":{\"text\":\"A Practical Diagnostic\",\"level\":2},\"tunes\":{}},{\"id\":\"qyoHcDcTKb\",\"type\":\"paragraph\",\"data\":{\"text\":\"For an important AI-generated claim, reconstruct the support chain backwards.\"},\"tunes\":{}},{\"id\":\"e3fDmLk0fe\",\"type\":\"code\",\"data\":{\"code\":\"CLAIM\\n  ↑\\nWhich inference produced it?\\n  ↑\\nWhich retrieved evidence supports that inference?\\n  ↑\\nWhich source produced that evidence?\\n  ↑\\nCan the original source still be inspected?\"},\"tunes\":{}},{\"id\":\"iQ60c7NRaE\",\"type\":\"paragraph\",\"data\":{\"text\":\"If the chain breaks, the system has encountered an evidence-traceability problem.\"},\"tunes\":{}},{\"id\":\"YQAKlNZ2gM\",\"type\":\"paragraph\",\"data\":{\"text\":\"If the chain remains intact but requires several strong assumptions, the Evidence Distance is still high.\"},\"tunes\":{}},{\"id\":\"clN7jFkUBf\",\"type\":\"header\",\"data\":{\"text\":\"Example: Product Data\",\"level\":2},\"tunes\":{}},{\"id\":\"CRF-A9O7Ot\",\"type\":\"paragraph\",\"data\":{\"text\":\"Suppose a manufacturer specification says a laptop weighs 1.48 kg.\"},\"tunes\":{}},{\"id\":\"JF3z6aF2q_\",\"type\":\"code\",\"data\":{\"code\":\"Manufacturer specification\\n→ 1.48 kg\\n→ \\\"The laptop weighs 1.48 kg\\\"\\n\\nLow Evidence Distance\"},\"tunes\":{}},{\"id\":\"vTMOOgkLPb\",\"type\":\"paragraph\",\"data\":{\"text\":\"Now consider:\"},\"tunes\":{}},{\"id\":\"Tuhfzor1yp\",\"type\":\"code\",\"data\":{\"code\":\"Manufacturer specification\\n→ 1.48 kg\\n→ comparison with three competitors\\n→ assumption about travel behavior\\n→ \\\"This is the best laptop for frequent travelers\\\"\\n\\nMuch higher Evidence Distance\"},\"tunes\":{}},{\"id\":\"JHFRGIgUJD\",\"type\":\"paragraph\",\"data\":{\"text\":\"The specification is real. The recommendation is still several reasoning steps away from it.\"},\"tunes\":{}},{\"id\":\"2lKERCrnW1\",\"type\":\"header\",\"data\":{\"text\":\"Example: Scientific Evidence\",\"level\":2},\"tunes\":{}},{\"id\":\"HA8DBYwUuK\",\"type\":\"paragraph\",\"data\":{\"text\":\"A paper may report an observed association. An AI answer can incorrectly compress that into a causal statement.\"},\"tunes\":{}},{\"id\":\"D9C2dVB7j_\",\"type\":\"code\",\"data\":{\"code\":\"Observed association\\n        ↓\\nInterpretation\\n        ↓\\nUnstated causal assumption\\n        ↓\\n\\\"X causes Y\\\"\"},\"tunes\":{}},{\"id\":\"CKNjK25pkL\",\"type\":\"paragraph\",\"data\":{\"text\":\"The problem is not that no evidence exists. The problem is that the final statement crosses a larger evidential distance than the study directly supports.\"},\"tunes\":{}},{\"id\":\"sR430N3cDd\",\"type\":\"header\",\"data\":{\"text\":\"Connection to Agent-Ready Context\",\"level\":2},\"tunes\":{}},{\"id\":\"v77YyG55ko\",\"type\":\"paragraph\",\"data\":{\"text\":\"Structured information objects can reduce avoidable Evidence Distance by preserving source identity, units, timestamps, conflicts and validity instead of forcing the model to reconstruct them from flattened text.\"},\"tunes\":{}},{\"id\":\"EX8darCIjd\",\"type\":\"linkTool\",\"data\":{\"link\":\"https:\u002F\u002Fstajic.de\u002Fportfolio\u002Ffrom-raw-data-to-verifiable-ai-context-a-practical-architecture-for-agent-ready-information\",\"meta\":{\"image\":{\"url\":\"\"},\"title\":\"From Raw Data to Verifiable AI Context: A Practical Architecture for Agent-Ready Information\",\"description\":\"Preserving provenance and validity before retrieval reduces the number of unsupported reconstruction steps required later.\"}},\"tunes\":{}},{\"id\":\"dsfuoURj1e\",\"type\":\"header\",\"data\":{\"text\":\"A Simple Working Scale\",\"level\":2},\"tunes\":{}},{\"id\":\"BLi7Yth64O\",\"type\":\"table\",\"data\":{\"withHeadings\":true,\"stretched\":false,\"content\":[[\"Level\",\"Description\"],[\"ED0\",\"Directly reported source fact\"],[\"ED1\",\"Simple deterministic transformation or direct comparison\"],[\"ED2\",\"One meaningful interpretive step\"],[\"ED3\",\"Multiple assumptions or combined sources\"],[\"ED4\",\"Strong causal, predictive or prescriptive conclusion beyond direct evidence\"]]},\"tunes\":{}},{\"id\":\"8y8bzCcHBs\",\"type\":\"paragraph\",\"data\":{\"text\":\"This scale is illustrative, not a scientific standard. Its purpose is to force an explicit question: How much did we have to add between the evidence and the claim?\"},\"tunes\":{}},{\"id\":\"aFK0YiAlYO\",\"type\":\"header\",\"data\":{\"text\":\"Misconceptions\",\"level\":2},\"tunes\":{}},{\"id\":\"07KSk6xDx-\",\"type\":\"paragraph\",\"data\":{\"text\":\"A citation means Evidence Distance is low. No. A citation can sit beside a claim that substantially exceeds what the cited source establishes.\"},\"tunes\":{}},{\"id\":\"t_cf-Zfan9\",\"type\":\"paragraph\",\"data\":{\"text\":\"Long Evidence Distance means the claim is wrong. No. Complex conclusions can be well supported, but they require a stronger reasoning chain.\"},\"tunes\":{}},{\"id\":\"Pu9PRX67Q6\",\"type\":\"paragraph\",\"data\":{\"text\":\"Primary sources eliminate inference. No. Primary evidence can still require interpretation.\"},\"tunes\":{}},{\"id\":\"oF1xNsOHHq\",\"type\":\"paragraph\",\"data\":{\"text\":\"More sources automatically reduce distance. Ten weak repetitions of the same unsupported claim do not create direct evidence.\"},\"tunes\":{}},{\"id\":\"IkolK5baph\",\"type\":\"header\",\"data\":{\"text\":\"Edge Case: Derived Data\",\"level\":2},\"tunes\":{}},{\"id\":\"jRH36fwdR3\",\"type\":\"paragraph\",\"data\":{\"text\":\"Some derived values are more reliable than raw human descriptions. A correctly calculated total from verified transactions can be highly dependable even though it is one transformation away from the original records.\"},\"tunes\":{}},{\"id\":\"SXbdKiNDst\",\"type\":\"paragraph\",\"data\":{\"text\":\"Evidence Distance therefore measures epistemic separation, not a simplistic preference for untouched data.\"},\"tunes\":{}},{\"id\":\"mUbvdnFChM\",\"type\":\"header\",\"data\":{\"text\":\"Limitations\",\"level\":2},\"tunes\":{}},{\"id\":\"N_EskxFOep\",\"type\":\"paragraph\",\"data\":{\"text\":\"Evidence Distance is a conceptual diagnostic, not a universal quantitative score.\"},\"tunes\":{}},{\"id\":\"nW_-E08So_\",\"type\":\"paragraph\",\"data\":{\"text\":\"Different domains require different treatment of inference. Mathematical proof, clinical evidence, software logs, market analysis and historical interpretation cannot share one universal numerical threshold.\"},\"tunes\":{}},{\"id\":\"_Bd95wXkri\",\"type\":\"header\",\"data\":{\"text\":\"What Would Change This Answer?\",\"level\":2},\"tunes\":{}},{\"id\":\"OqCaMc2_OX\",\"type\":\"paragraph\",\"data\":{\"text\":\"Future AI systems may generate explicit provenance graphs, machine-checkable claim-evidence links and typed reasoning traces. That could make parts of Evidence Distance measurable automatically.\"},\"tunes\":{}},{\"id\":\"58Hf3O9-Iz\",\"type\":\"paragraph\",\"data\":{\"text\":\"The underlying problem remains: a claim can move farther from its evidence even while every intermediate sentence sounds plausible.\"},\"tunes\":{}},{\"id\":\"8WOq5oE_e_\",\"type\":\"header\",\"data\":{\"text\":\"Conclusion\",\"level\":2},\"tunes\":{}},{\"id\":\"1pr1TfwnKc\",\"type\":\"paragraph\",\"data\":{\"text\":\"The important distinction is not merely between sourced and unsourced answers.\"},\"tunes\":{}},{\"id\":\"54CysT_NXa\",\"type\":\"paragraph\",\"data\":{\"text\":\"It is between claims that remain close to directly inspectable evidence and claims that depend on increasingly long chains of interpretation and assumption.\"},\"tunes\":{}},{\"id\":\"Qrjo3WEUSL\",\"type\":\"code\",\"data\":{\"code\":\"Evidence\\n   ↓\\nEvidence Distance\\n   ↓\\nReasoning\\n   ↓\\nAnswer Validity Boundary\"},\"tunes\":{}},{\"id\":\"6ir8aIAfd3\",\"type\":\"paragraph\",\"data\":{\"text\":\"Evidence Distance gives us a language for describing that space.\"},\"tunes\":{}},{\"id\":\"vPhupXcbeN\",\"type\":\"header\",\"data\":{\"text\":\"Primary Sources\",\"level\":2},\"tunes\":{}},{\"id\":\"BaO4JmWRQL\",\"type\":\"paragraph\",\"data\":{\"text\":\"Patrick Lewis et al., \u003Ca href=\\\"https:\u002F\u002Farxiv.org\u002Fabs\u002F2005.11401\\\" target=\\\"_blank\\\">Retrieval-Augmented Generation for Knowledge-Intensive NLP Tasks\u003C\u002Fa> (2020).\"},\"tunes\":{}},{\"id\":\"eg4Z4G6Cgs\",\"type\":\"paragraph\",\"data\":{\"text\":\"Nelson F. Liu et al., \u003Ca href=\\\"https:\u002F\u002Farxiv.org\u002Fabs\u002F2307.03172\\\" target=\\\"_blank\\\">Lost in the Middle: How Language Models Use Long Contexts\u003C\u002Fa> (2023).\"},\"tunes\":{}},{\"id\":\"BJH7zUd4T8\",\"type\":\"paragraph\",\"data\":{\"text\":\"W3C, \u003Ca href=\\\"https:\u002F\u002Fwww.w3.org\u002FTR\u002Fprov-overview\u002F\\\" target=\\\"_blank\\\">PROV Overview\u003C\u002Fa>. A standardized model for representing provenance relationships.\"},\"tunes\":{}}],\"version\":\"2.31.6\"}",{"time":777,"blocks":778,"version":741},1792096795538,[779,784,789,794,800,804,809,814,819,823,828,833,838,859,864,868,873,878,883,888,893,898,902,907,912,917,922,931,936,941,946,951,956,961,966,971,976,981,986,991,995,1004,1009,1014,1019,1023,1032,1037,1042,1046,1051,1056,1061,1066,1070,1075,1079,1084,1089,1094,1098,1103,1108,1113,1122,1127,1145,1150,1155,1160,1165,1170,1175,1180,1185,1190,1195,1200,1205,1210,1215,1220,1225,1230,1235,1239,1244,1249,1253,1257],{"id":780,"data":781,"type":42,"tunes":783},"g-2Ciyj8LJ",{"text":782,"level":234},"Question",{},{"id":785,"data":786,"type":240,"tunes":788},"SQWD7UraiX",{"text":787},"Two AI answers can both cite sources and still differ greatly in how directly those sources support the claims being made. How can we describe that difference?",{},{"id":790,"data":791,"type":240,"tunes":793},"Sj1fx2865j",{"text":792},"This article uses the term Evidence Distance for the distance between a claim and the directly inspectable evidence that supports it.",{},{"id":795,"data":796,"type":253,"tunes":799},"d5th-A-mq5",{"text":797,"caption":798,"alignment":252},"Evidence Distance is the number and strength of transformations, assumptions and inferential steps separating a claim from the evidence that can directly verify it.","Working definition — a reasoning concept introduced here, not a standardized metric.",{},{"id":801,"data":802,"type":260,"tunes":803},"X81jXC72FC",{"title":258,"maxLevel":259,"minLevel":234},{},{"id":805,"data":806,"type":42,"tunes":808},"_pZcnPEoil",{"text":807,"level":234},"What This Really Means",{},{"id":810,"data":811,"type":240,"tunes":813},"F5odO7xyha",{"text":812},"A citation proves that a source was referenced. It does not automatically prove that the source directly supports the sentence beside the citation.",{},{"id":815,"data":816,"type":240,"tunes":818},"8Gqlg-ArJS",{"text":817},"Between an original observation and a final AI answer there may be extraction, parsing, chunking, retrieval, summarization, interpretation, aggregation and inference.",{},{"id":820,"data":821,"type":281,"tunes":822},"lXkL5LV9ab",{"code":280},{},{"id":824,"data":825,"type":240,"tunes":827},"Z-8GPQIpCI",{"text":826},"Every step can be legitimate. But every step can also introduce distortion.",{},{"id":829,"data":830,"type":42,"tunes":832},"aXMwTGZ8Ia",{"text":831,"level":234},"Simplest Example",{},{"id":834,"data":835,"type":240,"tunes":837},"bZlU7KD3M2",{"text":836},"Suppose a temperature sensor reports 38.2 °C.",{},{"id":839,"data":840,"type":318,"tunes":858},"SP87UUPaU3",{"content":841,"stretched":43,"withHeadings":14},[842,846,850,854],[843,844,845],"Claim","Relationship to evidence","Illustrative distance",[847,848,849],"The sensor reports 38.2 °C.","Direct reading","Low",[851,852,853],"The system is running hotter than yesterday.","Requires comparison with yesterday's reading","Moderate",[855,856,857],"The cooling system is failing.","Requires causal inference and alternative explanations","High",{},{"id":860,"data":861,"type":240,"tunes":863},"eYar059d69",{"text":862},"The original sensor value directly supports the first claim. It does not by itself prove the third.",{},{"id":865,"data":866,"type":281,"tunes":867},"eCnFlByvHt",{"code":328},{},{"id":869,"data":870,"type":42,"tunes":872},"aXGkK6BpkF",{"text":871,"level":234},"Where the Example Stops Working",{},{"id":874,"data":875,"type":240,"tunes":877},"lAwb9QZ_yB",{"text":876},"Evidence Distance is not simply a count of sentences or processing steps.",{},{"id":879,"data":880,"type":240,"tunes":882},"-SzYko9oIH",{"text":881},"One mathematically valid transformation may preserve evidence almost perfectly, while a single speculative interpretation may create a much larger epistemic jump.",{},{"id":884,"data":885,"type":240,"tunes":887},"XOQloBndLw",{"text":886},"The concept therefore includes both distance by transformation and distance by inference.",{},{"id":889,"data":890,"type":42,"tunes":892},"EOXbS4TAOR",{"text":891,"level":234},"Direct Answer",{},{"id":894,"data":895,"type":240,"tunes":897},"HcVL8vGM74",{"text":896},"Evidence Distance increases when a claim depends on more transformations, more assumptions, weaker provenance or stronger inference than the underlying evidence directly warrants.",{},{"id":899,"data":900,"type":281,"tunes":901},"_f7WXjUOsS",{"code":363},{},{"id":903,"data":904,"type":240,"tunes":906},"zUEC-cDhqm",{"text":905},"A longer distance does not automatically make a claim false. It means the reasoning chain requires more validation.",{},{"id":908,"data":909,"type":42,"tunes":911},"ix7TUGNz_W",{"text":910,"level":234},"Why This Matters for AI",{},{"id":913,"data":914,"type":240,"tunes":916},"wrAy5PLtLB",{"text":915},"LLMs are particularly good at producing fluent bridges between pieces of information. That capability is useful, but fluency can hide how far the conclusion has moved beyond the directly retrieved evidence.",{},{"id":918,"data":919,"type":240,"tunes":921},"jJseLuTWdv",{"text":920},"An answer can therefore look well sourced while containing claims whose support exists only through several unstated inferential steps.",{},{"id":923,"data":924,"type":394,"tunes":930},"ox8fok9dzs",{"link":925,"meta":926},"https:\u002F\u002Fstajic.de\u002Fblog\u002Ffrom-research-protocol-to-a-general-ai-reasoning-framework",{"image":927,"title":928,"description":929},{"url":391},"From Research Protocol to a General AI Reasoning Framework","Reliable reasoning requires explicit separation of evidence, assumptions, hypotheses and conclusions.",{},{"id":932,"data":933,"type":42,"tunes":935},"6ipse6YG1U",{"text":934,"level":234},"Four Components of Evidence Distance",{},{"id":937,"data":938,"type":42,"tunes":940},"P4xQ7o96dR",{"text":939,"level":259},"1. Transformation Distance",{},{"id":942,"data":943,"type":240,"tunes":945},"dvN8TUNEYG",{"text":944},"How many times has the original evidence been converted, summarized, normalized or reformatted before reaching the model?",{},{"id":947,"data":948,"type":42,"tunes":950},"dXofACF9_x",{"text":949,"level":259},"2. Inferential Distance",{},{"id":952,"data":953,"type":240,"tunes":955},"kkEzV3ydId",{"text":954},"How many reasoning steps are required before the evidence supports the final conclusion?",{},{"id":957,"data":958,"type":42,"tunes":960},"PPYB0RuX5_",{"text":959,"level":259},"3. Provenance Distance",{},{"id":962,"data":963,"type":240,"tunes":965},"cAVBYebJr7",{"text":964},"Can the retrieved statement still be traced to an identifiable original source, or has it passed through summaries and copies whose origin is unclear?",{},{"id":967,"data":968,"type":42,"tunes":970},"254N_99Z7X",{"text":969,"level":259},"4. Applicability Distance",{},{"id":972,"data":973,"type":240,"tunes":975},"to3KWv5WSK",{"text":974},"How much reasoning is required to establish that the evidence applies to this exact version, user, jurisdiction, time period or situation?",{},{"id":977,"data":978,"type":42,"tunes":980},"3WnVPQ6wqw",{"text":979,"level":234},"Evidence Distance in RAG",{},{"id":982,"data":983,"type":240,"tunes":985},"r8s-75EBhA",{"text":984},"RAG can shorten one important distance: the distance between a model and external knowledge.",{},{"id":987,"data":988,"type":240,"tunes":990},"I_8h52A3Yc",{"text":989},"It does not automatically shorten the distance between a retrieved passage and the final claim.",{},{"id":992,"data":993,"type":281,"tunes":994},"jDDRUu2FUK",{"code":459},{},{"id":996,"data":997,"type":394,"tunes":1003},"rm14qIVB1M",{"link":998,"meta":999},"https:\u002F\u002Fstajic.de\u002Fblog\u002Fwhere-does-an-llm-get-its-data-rag-data-sources-in-python",{"image":1000,"title":1001,"description":1002},{"url":391},"Where Does an LLM Get Its Data? RAG Data Sources in Python","Retrieval brings external information into context. Evidence Distance asks what happens between that information and the claim eventually produced.",{},{"id":1005,"data":1006,"type":42,"tunes":1008},"CRUZDXYloa",{"text":1007,"level":234},"Connection to the Retrieval Trigger",{},{"id":1010,"data":1011,"type":240,"tunes":1013},"-TPiYJ569c",{"text":1012},"The Retrieval Trigger asks whether internal model knowledge is sufficient.",{},{"id":1015,"data":1016,"type":240,"tunes":1018},"_VjLi5fs4x",{"text":1017},"Evidence Distance asks a later question: after evidence has been retrieved, how directly does it support what the system wants to claim?",{},{"id":1020,"data":1021,"type":281,"tunes":1022},"EImPw6i617",{"code":488},{},{"id":1024,"data":1025,"type":394,"tunes":1031},"ZqyhB2bFtR",{"link":1026,"meta":1027},"https:\u002F\u002Fstajic.de\u002Fblog\u002Fwhen-should-an-ai-stop-trusting-its-own-knowledge-the-retrieval-trigger",{"image":1028,"title":1029,"description":1030},{"url":391},"When Should an AI Stop Trusting Its Own Knowledge? — The Retrieval Trigger","The trigger decides when evidence is needed. Evidence Distance evaluates how far the eventual claim moves beyond that evidence.",{},{"id":1033,"data":1034,"type":42,"tunes":1036},"ST2vnm6vwF",{"text":1035,"level":234},"A Practical Diagnostic",{},{"id":1038,"data":1039,"type":240,"tunes":1041},"qyoHcDcTKb",{"text":1040},"For an important AI-generated claim, reconstruct the support chain backwards.",{},{"id":1043,"data":1044,"type":281,"tunes":1045},"e3fDmLk0fe",{"code":512},{},{"id":1047,"data":1048,"type":240,"tunes":1050},"iQ60c7NRaE",{"text":1049},"If the chain breaks, the system has encountered an evidence-traceability problem.",{},{"id":1052,"data":1053,"type":240,"tunes":1055},"YQAKlNZ2gM",{"text":1054},"If the chain remains intact but requires several strong assumptions, the Evidence Distance is still high.",{},{"id":1057,"data":1058,"type":42,"tunes":1060},"clN7jFkUBf",{"text":1059,"level":234},"Example: Product Data",{},{"id":1062,"data":1063,"type":240,"tunes":1065},"CRF-A9O7Ot",{"text":1064},"Suppose a manufacturer specification says a laptop weighs 1.48 kg.",{},{"id":1067,"data":1068,"type":281,"tunes":1069},"JF3z6aF2q_",{"code":537},{},{"id":1071,"data":1072,"type":240,"tunes":1074},"vTMOOgkLPb",{"text":1073},"Now consider:",{},{"id":1076,"data":1077,"type":281,"tunes":1078},"Tuhfzor1yp",{"code":547},{},{"id":1080,"data":1081,"type":240,"tunes":1083},"JHFRGIgUJD",{"text":1082},"The specification is real. The recommendation is still several reasoning steps away from it.",{},{"id":1085,"data":1086,"type":42,"tunes":1088},"2lKERCrnW1",{"text":1087,"level":234},"Example: Scientific Evidence",{},{"id":1090,"data":1091,"type":240,"tunes":1093},"HA8DBYwUuK",{"text":1092},"A paper may report an observed association. An AI answer can incorrectly compress that into a causal statement.",{},{"id":1095,"data":1096,"type":281,"tunes":1097},"D9C2dVB7j_",{"code":567},{},{"id":1099,"data":1100,"type":240,"tunes":1102},"CKNjK25pkL",{"text":1101},"The problem is not that no evidence exists. The problem is that the final statement crosses a larger evidential distance than the study directly supports.",{},{"id":1104,"data":1105,"type":42,"tunes":1107},"sR430N3cDd",{"text":1106,"level":234},"Connection to Agent-Ready Context",{},{"id":1109,"data":1110,"type":240,"tunes":1112},"v77YyG55ko",{"text":1111},"Structured information objects can reduce avoidable Evidence Distance by preserving source identity, units, timestamps, conflicts and validity instead of forcing the model to reconstruct them from flattened text.",{},{"id":1114,"data":1115,"type":394,"tunes":1121},"EX8darCIjd",{"link":1116,"meta":1117},"https:\u002F\u002Fstajic.de\u002Fportfolio\u002Ffrom-raw-data-to-verifiable-ai-context-a-practical-architecture-for-agent-ready-information",{"image":1118,"title":1119,"description":1120},{"url":391},"From Raw Data to Verifiable AI Context: A Practical Architecture for Agent-Ready Information","Preserving provenance and validity before retrieval reduces the number of unsupported reconstruction steps required later.",{},{"id":1123,"data":1124,"type":42,"tunes":1126},"dsfuoURj1e",{"text":1125,"level":234},"A Simple Working Scale",{},{"id":1128,"data":1129,"type":318,"tunes":1144},"BLi7Yth64O",{"content":1130,"stretched":43,"withHeadings":14},[1131,1134,1136,1138,1140,1142],[1132,1133],"Level","Description",[606,1135],"Directly reported source fact",[609,1137],"Simple deterministic transformation or direct comparison",[612,1139],"One meaningful interpretive step",[615,1141],"Multiple assumptions or combined sources",[618,1143],"Strong causal, predictive or prescriptive conclusion beyond direct evidence",{},{"id":1146,"data":1147,"type":240,"tunes":1149},"8y8bzCcHBs",{"text":1148},"This scale is illustrative, not a scientific standard. Its purpose is to force an explicit question: How much did we have to add between the evidence and the claim?",{},{"id":1151,"data":1152,"type":42,"tunes":1154},"aFK0YiAlYO",{"text":1153,"level":234},"Misconceptions",{},{"id":1156,"data":1157,"type":240,"tunes":1159},"07KSk6xDx-",{"text":1158},"A citation means Evidence Distance is low. No. A citation can sit beside a claim that substantially exceeds what the cited source establishes.",{},{"id":1161,"data":1162,"type":240,"tunes":1164},"t_cf-Zfan9",{"text":1163},"Long Evidence Distance means the claim is wrong. No. Complex conclusions can be well supported, but they require a stronger reasoning chain.",{},{"id":1166,"data":1167,"type":240,"tunes":1169},"Pu9PRX67Q6",{"text":1168},"Primary sources eliminate inference. No. Primary evidence can still require interpretation.",{},{"id":1171,"data":1172,"type":240,"tunes":1174},"oF1xNsOHHq",{"text":1173},"More sources automatically reduce distance. Ten weak repetitions of the same unsupported claim do not create direct evidence.",{},{"id":1176,"data":1177,"type":42,"tunes":1179},"IkolK5baph",{"text":1178,"level":234},"Edge Case: Derived Data",{},{"id":1181,"data":1182,"type":240,"tunes":1184},"jRH36fwdR3",{"text":1183},"Some derived values are more reliable than raw human descriptions. A correctly calculated total from verified transactions can be highly dependable even though it is one transformation away from the original records.",{},{"id":1186,"data":1187,"type":240,"tunes":1189},"SXbdKiNDst",{"text":1188},"Evidence Distance therefore measures epistemic separation, not a simplistic preference for untouched data.",{},{"id":1191,"data":1192,"type":42,"tunes":1194},"mUbvdnFChM",{"text":1193,"level":234},"Limitations",{},{"id":1196,"data":1197,"type":240,"tunes":1199},"N_EskxFOep",{"text":1198},"Evidence Distance is a conceptual diagnostic, not a universal quantitative score.",{},{"id":1201,"data":1202,"type":240,"tunes":1204},"nW_-E08So_",{"text":1203},"Different domains require different treatment of inference. Mathematical proof, clinical evidence, software logs, market analysis and historical interpretation cannot share one universal numerical threshold.",{},{"id":1206,"data":1207,"type":42,"tunes":1209},"_Bd95wXkri",{"text":1208,"level":234},"What Would Change This Answer?",{},{"id":1211,"data":1212,"type":240,"tunes":1214},"OqCaMc2_OX",{"text":1213},"Future AI systems may generate explicit provenance graphs, machine-checkable claim-evidence links and typed reasoning traces. That could make parts of Evidence Distance measurable automatically.",{},{"id":1216,"data":1217,"type":240,"tunes":1219},"58Hf3O9-Iz",{"text":1218},"The underlying problem remains: a claim can move farther from its evidence even while every intermediate sentence sounds plausible.",{},{"id":1221,"data":1222,"type":42,"tunes":1224},"8WOq5oE_e_",{"text":1223,"level":234},"Conclusion",{},{"id":1226,"data":1227,"type":240,"tunes":1229},"1pr1TfwnKc",{"text":1228},"The important distinction is not merely between sourced and unsourced answers.",{},{"id":1231,"data":1232,"type":240,"tunes":1234},"54CysT_NXa",{"text":1233},"It is between claims that remain close to directly inspectable evidence and claims that depend on increasingly long chains of interpretation and assumption.",{},{"id":1236,"data":1237,"type":281,"tunes":1238},"Qrjo3WEUSL",{"code":714},{},{"id":1240,"data":1241,"type":240,"tunes":1243},"6ir8aIAfd3",{"text":1242},"Evidence Distance gives us a language for describing that space.",{},{"id":1245,"data":1246,"type":42,"tunes":1248},"vPhupXcbeN",{"text":1247,"level":234},"Primary Sources",{},{"id":1250,"data":1251,"type":240,"tunes":1252},"BaO4JmWRQL",{"text":729},{},{"id":1254,"data":1255,"type":240,"tunes":1256},"eg4Z4G6Cgs",{"text":734},{},{"id":1258,"data":1259,"type":240,"tunes":1261},"BJH7zUd4T8",{"text":1260},"W3C, \u003Ca href=\"https:\u002F\u002Fwww.w3.org\u002FTR\u002Fprov-overview\u002F\" target=\"_blank\">PROV Overview\u003C\u002Fa>. A standardized model for representing provenance relationships.",{},"Not all supported answers are equally close to their evidence. Evidence Distance is a practical way to describe how many transformations, summaries, assumptions and inferential steps separate an AI claim from directly inspectable evidence.",{"lang":7,"title":224,"content":226,"contentJson":1264,"excerpt":742},{"time":228,"blocks":1265,"version":741},[1266,1269,1272,1275,1278,1281,1284,1287,1290,1293,1296,1299,1302,1310,1313,1316,1319,1322,1325,1328,1331,1334,1337,1340,1343,1346,1349,1354,1357,1360,1363,1366,1369,1372,1375,1378,1381,1384,1387,1390,1393,1398,1401,1404,1407,1410,1415,1418,1421,1424,1427,1430,1433,1436,1439,1442,1445,1448,1451,1454,1457,1460,1463,1466,1471,1474,1484,1487,1490,1493,1496,1499,1502,1505,1508,1511,1514,1517,1520,1523,1526,1529,1532,1535,1538,1541,1544,1547,1550,1553],{"id":231,"data":1267,"type":42,"tunes":1268},{"text":233,"level":234},{},{"id":237,"data":1270,"type":240,"tunes":1271},{"text":239},{},{"id":243,"data":1273,"type":240,"tunes":1274},{"text":245},{},{"id":248,"data":1276,"type":253,"tunes":1277},{"text":250,"caption":251,"alignment":252},{},{"id":256,"data":1279,"type":260,"tunes":1280},{"title":258,"maxLevel":259,"minLevel":234},{},{"id":263,"data":1282,"type":42,"tunes":1283},{"text":265,"level":234},{},{"id":268,"data":1285,"type":240,"tunes":1286},{"text":270},{},{"id":273,"data":1288,"type":240,"tunes":1289},{"text":275},{},{"id":278,"data":1291,"type":281,"tunes":1292},{"code":280},{},{"id":284,"data":1294,"type":240,"tunes":1295},{"text":286},{},{"id":289,"data":1297,"type":42,"tunes":1298},{"text":291,"level":234},{},{"id":294,"data":1300,"type":240,"tunes":1301},{"text":296},{},{"id":299,"data":1303,"type":318,"tunes":1309},{"content":1304,"stretched":43,"withHeadings":14},[1305,1306,1307,1308],[303,304,305],[307,308,309],[311,312,313],[315,316,317],{},{"id":321,"data":1311,"type":240,"tunes":1312},{"text":323},{},{"id":326,"data":1314,"type":281,"tunes":1315},{"code":328},{},{"id":331,"data":1317,"type":42,"tunes":1318},{"text":333,"level":234},{},{"id":336,"data":1320,"type":240,"tunes":1321},{"text":338},{},{"id":341,"data":1323,"type":240,"tunes":1324},{"text":343},{},{"id":346,"data":1326,"type":240,"tunes":1327},{"text":348},{},{"id":351,"data":1329,"type":42,"tunes":1330},{"text":353,"level":234},{},{"id":356,"data":1332,"type":240,"tunes":1333},{"text":358},{},{"id":361,"data":1335,"type":281,"tunes":1336},{"code":363},{},{"id":366,"data":1338,"type":240,"tunes":1339},{"text":368},{},{"id":371,"data":1341,"type":42,"tunes":1342},{"text":373,"level":234},{},{"id":376,"data":1344,"type":240,"tunes":1345},{"text":378},{},{"id":381,"data":1347,"type":240,"tunes":1348},{"text":383},{},{"id":386,"data":1350,"type":394,"tunes":1353},{"link":388,"meta":1351},{"image":1352,"title":392,"description":393},{"url":391},{},{"id":397,"data":1355,"type":42,"tunes":1356},{"text":399,"level":234},{},{"id":402,"data":1358,"type":42,"tunes":1359},{"text":404,"level":259},{},{"id":407,"data":1361,"type":240,"tunes":1362},{"text":409},{},{"id":412,"data":1364,"type":42,"tunes":1365},{"text":414,"level":259},{},{"id":417,"data":1367,"type":240,"tunes":1368},{"text":419},{},{"id":422,"data":1370,"type":42,"tunes":1371},{"text":424,"level":259},{},{"id":427,"data":1373,"type":240,"tunes":1374},{"text":429},{},{"id":432,"data":1376,"type":42,"tunes":1377},{"text":434,"level":259},{},{"id":437,"data":1379,"type":240,"tunes":1380},{"text":439},{},{"id":442,"data":1382,"type":42,"tunes":1383},{"text":444,"level":234},{},{"id":447,"data":1385,"type":240,"tunes":1386},{"text":449},{},{"id":452,"data":1388,"type":240,"tunes":1389},{"text":454},{},{"id":457,"data":1391,"type":281,"tunes":1392},{"code":459},{},{"id":462,"data":1394,"type":394,"tunes":1397},{"link":464,"meta":1395},{"image":1396,"title":467,"description":468},{"url":391},{},{"id":471,"data":1399,"type":42,"tunes":1400},{"text":473,"level":234},{},{"id":476,"data":1402,"type":240,"tunes":1403},{"text":478},{},{"id":481,"data":1405,"type":240,"tunes":1406},{"text":483},{},{"id":486,"data":1408,"type":281,"tunes":1409},{"code":488},{},{"id":491,"data":1411,"type":394,"tunes":1414},{"link":493,"meta":1412},{"image":1413,"title":496,"description":497},{"url":391},{},{"id":500,"data":1416,"type":42,"tunes":1417},{"text":502,"level":234},{},{"id":505,"data":1419,"type":240,"tunes":1420},{"text":507},{},{"id":510,"data":1422,"type":281,"tunes":1423},{"code":512},{},{"id":515,"data":1425,"type":240,"tunes":1426},{"text":517},{},{"id":520,"data":1428,"type":240,"tunes":1429},{"text":522},{},{"id":525,"data":1431,"type":42,"tunes":1432},{"text":527,"level":234},{},{"id":530,"data":1434,"type":240,"tunes":1435},{"text":532},{},{"id":535,"data":1437,"type":281,"tunes":1438},{"code":537},{},{"id":540,"data":1440,"type":240,"tunes":1441},{"text":542},{},{"id":545,"data":1443,"type":281,"tunes":1444},{"code":547},{},{"id":550,"data":1446,"type":240,"tunes":1447},{"text":552},{},{"id":555,"data":1449,"type":42,"tunes":1450},{"text":557,"level":234},{},{"id":560,"data":1452,"type":240,"tunes":1453},{"text":562},{},{"id":565,"data":1455,"type":281,"tunes":1456},{"code":567},{},{"id":570,"data":1458,"type":240,"tunes":1459},{"text":572},{},{"id":575,"data":1461,"t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