https://chiosa.app/papers/ask-about-the-right-moment-training-free-object-identity-reasoning-for-video-language-models-2610-31471Chiosaen2026-10-23T15:58:10ZA tracker helps video models keep object identities through a shufflehttps://chiosa.app/papers/ask-about-the-right-moment-training-free-object-identity-reasoning-for-video-language-models-2610-31471?lang=esChiosaes2026-10-23T15:58:10ZUn rastreador ayuda a modelos de video a conservar la identidad de los objetos al barajarhttps://chiosa.app/papers/ask-about-the-right-moment-training-free-object-identity-reasoning-for-video-language-models-2610-31471?lang=frChiosafr2026-10-23T15:58:10ZUn système de suivi aide les modèles vidéo à conserver l’identité des objets pendant un mélangehttps://chiosa.app/papers/compiling-autoregressive-llm-control-into-policy-exact-parallel-inference-for-hierarchical-rag-2610-30664Chiosaen2026-10-23T15:58:10ZParallel compilation speeds finite-action language model controlhttps://chiosa.app/papers/compiling-autoregressive-llm-control-into-policy-exact-parallel-inference-for-hierarchical-rag-2610-30664?lang=itChiosait2026-10-23T15:58:10ZLa compilazione parallela accelera il controllo dei modelli linguistici ad azioni finitehttps://chiosa.app/papers/compiling-autoregressive-llm-control-into-policy-exact-parallel-inference-for-hierarchical-rag-2610-30664?lang=esChiosaes2026-10-23T15:58:10ZLa compilación paralela acelera el control de modelos de lenguaje con acciones finitashttps://chiosa.app/papers/compiling-autoregressive-llm-control-into-policy-exact-parallel-inference-for-hierarchical-rag-2610-30664?lang=frChiosafr2026-10-23T15:58:10ZLa compilation parallèle accélère le contrôle des modèles de langage à actions finieshttps://chiosa.app/papers/drill-training-world-models-to-improve-policies-not-predict-pixels-2610-30814Chiosaen2026-10-23T15:58:10ZTraining video world models for robot learning, not just clearer frameshttps://chiosa.app/papers/energy-and-latency-first-how-deployment-constraints-reverse-time-series-model-selection-2610-30965Chiosaen2026-10-23T15:58:10ZDeadline and energy reshape time-series model choicehttps://chiosa.app/papers/energy-and-latency-first-how-deployment-constraints-reverse-time-series-model-selection-2610-30965?lang=itChiosait2026-10-23T15:58:10ZScadenze ed energia cambiano la scelta dei modelli temporalihttps://chiosa.app/papers/energy-and-latency-first-how-deployment-constraints-reverse-time-series-model-selection-2610-30965?lang=esChiosaes2026-10-23T15:58:10ZPlazo y energía cambian la elección de modelos de series temporaleshttps://chiosa.app/papers/energy-and-latency-first-how-deployment-constraints-reverse-time-series-model-selection-2610-30965?lang=frChiosafr2026-10-23T15:58:10ZLe délai et l’énergie changent le choix des modèles temporelshttps://chiosa.app/papers/focusvla-future-intent-guided-dynamic-view-routing-for-vision-language-action-models-2610-31423Chiosaen2026-10-23T15:58:10ZA robot method routes camera views by future action needshttps://chiosa.app/papers/focusvla-future-intent-guided-dynamic-view-routing-for-vision-language-action-models-2610-31423?lang=esChiosaes2026-10-23T15:58:10ZUn método adapta las vistas de cámara a las acciones del robothttps://chiosa.app/papers/random-remasking-scales-masked-diffusion-inference-2610-30744Chiosaen2026-10-23T15:58:10ZRandom remasking lets diffusion models revise and improve sequenceshttps://chiosa.app/papers/random-remasking-scales-masked-diffusion-inference-2610-30744?lang=itChiosait2026-10-23T15:58:10ZIl remasking casuale permette ai modelli diffusion di rivedere e migliorare le sequenzehttps://chiosa.app/papers/random-remasking-scales-masked-diffusion-inference-2610-30744?lang=frChiosafr2026-10-23T15:58:10ZLe remasquage aléatoire aide les modèles de diffusion à réviser leurs séquenceshttps://chiosa.app/papers/rift-kv-cache-compression-for-draft-models-with-complementary-compressibility-training-2610-31098Chiosaen2026-10-23T15:58:10ZRIFT keeps draft caches useful as generation growshttps://chiosa.app/papers/rift-kv-cache-compression-for-draft-models-with-complementary-compressibility-training-2610-31098?lang=itChiosait2026-10-23T15:58:10ZRIFT mantiene utile la cache della bozza mentre il testo crescehttps://chiosa.app/papers/rift-kv-cache-compression-for-draft-models-with-complementary-compressibility-training-2610-31098?lang=esChiosaes2026-10-23T15:58:10ZRIFT mantiene útil la caché del borrador al generarhttps://chiosa.app/papers/ripple-recovery-internalization-through-process-level-policy-learning-and-effect-aware-credit-for-long-horizo-2610-31276Chiosaen2026-10-23T15:58:10ZCoding agents can learn to turn failure critiques into repairshttps://chiosa.app/papers/ripple-recovery-internalization-through-process-level-policy-learning-and-effect-aware-credit-for-long-horizo-2610-31276?lang=itChiosait2026-10-23T15:58:10ZGli agenti di programmazione possono imparare a trasformare le critiche agli errori in riparazionihttps://chiosa.app/papers/ripple-recovery-internalization-through-process-level-policy-learning-and-effect-aware-credit-for-long-horizo-2610-31276?lang=esChiosaes2026-10-23T15:58:10ZLos agentes de programación pueden aprender a convertir críticas de fallos en reparacioneshttps://chiosa.app/papers/same-entity-different-script-diagnosing-cross-script-entity-matching-failures-in-language-models-2610-31407Chiosaen2026-10-23T15:58:10ZIdentity matching weakens when names switch scriptshttps://chiosa.app/papers/same-entity-different-script-diagnosing-cross-script-entity-matching-failures-in-language-models-2610-31407?lang=itChiosait2026-10-23T15:58:10ZIl riconoscimento delle identità cala quando cambiano le scritture dei nomihttps://chiosa.app/papers/same-entity-different-script-diagnosing-cross-script-entity-matching-failures-in-language-models-2610-31407?lang=esChiosaes2026-10-23T15:58:10ZLa coincidencia de identidades falla cuando cambian los sistemas de escriturahttps://chiosa.app/papers/same-entity-different-script-diagnosing-cross-script-entity-matching-failures-in-language-models-2610-31407?lang=frChiosafr2026-10-23T15:58:10ZLa correspondance d’identité faiblit quand l’écriture des noms changehttps://chiosa.app/papers/spectralcanary-embarrassingly-simple-low-frequency-traces-for-t2i-dataset-use-auditing-2610-31659Chiosaen2026-10-23T15:58:10ZA shared image trace helps reveal when models learn a collection's stylehttps://chiosa.app/papers/spectralcanary-embarrassingly-simple-low-frequency-traces-for-t2i-dataset-use-auditing-2610-31659?lang=esChiosaes2026-10-23T15:58:10ZUna señal compartida ayuda a revelar cuándo los modelos aprenden el estilo de una colecciónhttps://chiosa.app/papers/spectralcanary-embarrassingly-simple-low-frequency-traces-for-t2i-dataset-use-auditing-2610-31659?lang=frChiosafr2026-10-23T15:58:10ZUn signal visuel commun aide à repérer quand un modèle apprend le style d’une collectionhttps://chiosa.app/papers/typed-template-fuzzing-2610-31342Chiosaen2026-10-23T15:58:10ZFuzzing from typed templates reaches many valid programshttps://chiosa.app/papers/typed-template-fuzzing-2610-31342?lang=frChiosafr2026-10-23T15:58:10ZLe fuzzing par modèles typés produit de nombreux programmes valideshttps://chiosa.app/papers/uniguard-multi-view-guardrail-learning-for-cross-interface-safety-generalization-2610-30742Chiosaen2026-10-23T15:58:10ZTraining guardrails across formats improves safety judgmentshttps://chiosa.app/papers/uniguard-multi-view-guardrail-learning-for-cross-interface-safety-generalization-2610-30742?lang=itChiosait2026-10-23T15:58:10ZAddestrare guardrail su più formati migliora i giudizi di sicurezzahttps://chiosa.app/papers/uniguard-multi-view-guardrail-learning-for-cross-interface-safety-generalization-2610-30742?lang=esChiosaes2026-10-23T15:58:10ZEntrenar barreras en varios formatos mejora los juicios de seguridadhttps://chiosa.app/papers/uniguard-multi-view-guardrail-learning-for-cross-interface-safety-generalization-2610-30742?lang=frChiosafr2026-10-23T15:58:10ZEntraîner des garde-fous sur plusieurs formats améliore les jugements de sécuritéhttps://chiosa.app/papers/when-is-a-memory-safe-enough-to-act-on-action-conditioned-verification-for-long-lived-agents-2610-31446Chiosaen2026-10-23T15:58:10ZA gate checks memories before consequential actionshttps://chiosa.app/papers/when-is-a-memory-safe-enough-to-act-on-action-conditioned-verification-for-long-lived-agents-2610-31446?lang=esChiosaes2026-10-23T15:58:10ZUna compuerta comprueba los recuerdos antes de acciones importanteshttps://chiosa.app/papers/when-is-a-memory-safe-enough-to-act-on-action-conditioned-verification-for-long-lived-agents-2610-31446?lang=frChiosafr2026-10-23T15:58:10ZUn filtre vérifie la mémoire avant une action lourde de conséquenceshttps://chiosa.app/papers/afterdeletebench-auditing-post-deletion-information-persistence-in-llm-agents-2610-31366Chiosaen2026-10-23T10:33:44ZDeleting a record can leave its secret recoverable by an agenthttps://chiosa.app/papers/afterdeletebench-auditing-post-deletion-information-persistence-in-llm-agents-2610-31366?lang=itChiosait2026-10-23T10:33:44ZEliminare un documento può lasciare il segreto recuperabile da un agentehttps://chiosa.app/papers/afterdeletebench-auditing-post-deletion-information-persistence-in-llm-agents-2610-31366?lang=esChiosaes2026-10-23T10:33:44ZBorrar un registro puede dejar su secreto al alcance de un agentehttps://chiosa.app/papers/afterdeletebench-auditing-post-deletion-information-persistence-in-llm-agents-2610-31366?lang=frChiosafr2026-10-23T10:33:44ZSupprimer un enregistrement peut laisser un secret récupérable par un agenthttps://chiosa.app/papers/agenttriage-replayable-security-diagnosis-across-heterogeneous-agent-runtimes-2610-30738Chiosaen2026-10-23T10:33:44ZAgentTriage makes agent security diagnoses replayablehttps://chiosa.app/papers/agenttriage-replayable-security-diagnosis-across-heterogeneous-agent-runtimes-2610-30738?lang=itChiosait2026-10-23T10:33:44ZAgentTriage rende riproducibili le diagnosi di sicurezza degli agentihttps://chiosa.app/papers/agenttriage-replayable-security-diagnosis-across-heterogeneous-agent-runtimes-2610-30738?lang=esChiosaes2026-10-23T10:33:44ZAgentTriage hace reproducibles los diagnósticos de seguridad de agenteshttps://chiosa.app/papers/auditing-the-hand-written-inputs-of-learned-access-control-2610-31388Chiosaen2026-10-23T10:33:44ZAudits expose risks in hand-written access controlhttps://chiosa.app/papers/auditing-the-hand-written-inputs-of-learned-access-control-2610-31388?lang=itChiosait2026-10-23T10:33:44ZGli audit rivelano rischi nelle regole manuali per il controllo degli accessihttps://chiosa.app/papers/auditing-the-hand-written-inputs-of-learned-access-control-2610-31388?lang=esChiosaes2026-10-23T10:33:44ZAuditorías revelan riesgos en el control de acceso manualhttps://chiosa.app/papers/auditing-the-hand-written-inputs-of-learned-access-control-2610-31388?lang=frChiosafr2026-10-23T10:33:44ZDes audits révèlent les risques des règles écrites à la main en contrôle d’accèshttps://chiosa.app/papers/automating-feature-flag-cleanup-at-scale-with-multi-agent-llm-systems-an-industrial-experience-at-doordash-2610-31351Chiosaen2026-10-23T10:33:44ZDoorDash system automates feature flag cleanup across codehttps://chiosa.app/papers/automating-feature-flag-cleanup-at-scale-with-multi-agent-llm-systems-an-industrial-experience-at-doordash-2610-31351?lang=itChiosait2026-10-23T10:33:44ZDoorDash automatizza la rimozione dei feature flag tra più filehttps://chiosa.app/papers/automating-feature-flag-cleanup-at-scale-with-multi-agent-llm-systems-an-industrial-experience-at-doordash-2610-31351?lang=esChiosaes2026-10-23T10:33:44ZDoorDash automatiza la limpieza de indicadores en el códigohttps://chiosa.app/papers/automating-feature-flag-cleanup-at-scale-with-multi-agent-llm-systems-an-industrial-experience-at-doordash-2610-31351?lang=frChiosafr2026-10-23T10:33:44ZDoorDash automatise le nettoyage des indicateurs dans le codehttps://chiosa.app/papers/dispersive-tree-agreements-for-multi-robot-coordination-2610-31540Chiosaen2026-10-23T10:33:44ZRobots can negotiate near-optimal plans while moving in real time (F1)https://chiosa.app/papers/dispersive-tree-agreements-for-multi-robot-coordination-2610-31540?lang=itChiosait2026-10-23T10:33:44ZI robot possono negoziare piani quasi ottimali in tempo reale (F1)https://chiosa.app/papers/dispersive-tree-agreements-for-multi-robot-coordination-2610-31540?lang=esChiosaes2026-10-23T10:33:44ZLos robots pueden negociar planes casi óptimos mientras se mueven en tiempo real (F1)https://chiosa.app/papers/evaluating-the-applicability-of-llm-inference-optimizations-on-apple-silicon-2610-31479Chiosaen2026-10-23T10:33:44ZApple Silicon speedups depend on the workload and runtimehttps://chiosa.app/papers/extreme-compression-of-per-layer-embeddings-via-cross-layer-latent-vector-quantization-2610-31309Chiosaen2026-10-23T10:33:44ZShared token codes sharply compress Gemma 4 embeddingshttps://chiosa.app/papers/extreme-compression-of-per-layer-embeddings-via-cross-layer-latent-vector-quantization-2610-31309?lang=itChiosait2026-10-23T10:33:44ZCodici condivisi comprimono le embedding di Gemma 4https://chiosa.app/papers/extreme-compression-of-per-layer-embeddings-via-cross-layer-latent-vector-quantization-2610-31309?lang=esChiosaes2026-10-23T10:33:44ZCódigos compartidos comprimen las embeddings de Gemma 4https://chiosa.app/papers/extreme-compression-of-per-layer-embeddings-via-cross-layer-latent-vector-quantization-2610-31309?lang=frChiosafr2026-10-23T10:33:44ZDes codes partagés compressent fortement les embeddings de Gemma 4https://chiosa.app/papers/foundation4d-towards-a-unified-perception-and-reconstruction-model-for-autonomous-vehicles-2610-30340Chiosaen2026-10-23T10:33:44ZOne model links driving-scene understanding with 3D reconstructionhttps://chiosa.app/papers/foundation4d-towards-a-unified-perception-and-reconstruction-model-for-autonomous-vehicles-2610-30340?lang=itChiosait2026-10-23T10:33:44ZUn modello collega la comprensione delle scene stradali alla ricostruzione 3Dhttps://chiosa.app/papers/foundation4d-towards-a-unified-perception-and-reconstruction-model-for-autonomous-vehicles-2610-30340?lang=esChiosaes2026-10-23T10:33:44ZUn modelo conecta la comprensión de escenas de conducción con la reconstrucción 3Dhttps://chiosa.app/papers/foundation4d-towards-a-unified-perception-and-reconstruction-model-for-autonomous-vehicles-2610-30340?lang=frChiosafr2026-10-23T10:33:44ZUn modèle relie compréhension des scènes de conduite et reconstruction 3Dhttps://chiosa.app/papers/invisibilization-as-a-source-of-probability-bias-in-language-models-2610-30785Chiosaen2026-10-23T10:33:44ZA benchmark reveals when language models set reports asidehttps://chiosa.app/papers/invisibilization-as-a-source-of-probability-bias-in-language-models-2610-30785?lang=itChiosait2026-10-23T10:33:44ZUn benchmark mostra quando i modelli linguistici accantonano segnalazionihttps://chiosa.app/papers/invisibilization-as-a-source-of-probability-bias-in-language-models-2610-30785?lang=frChiosafr2026-10-23T10:33:44ZUn test révèle quand les modèles de langage écartent des témoignageshttps://chiosa.app/papers/joint-or-per-option-compact-encoders-for-closed-set-decisions-from-17-million-to-1-billion-parameters-2610-30970Chiosaen2026-10-23T10:33:44ZCompact encoders face a trade-off in how they read optionshttps://chiosa.app/papers/joint-or-per-option-compact-encoders-for-closed-set-decisions-from-17-million-to-1-billion-parameters-2610-30970?lang=esChiosaes2026-10-23T10:33:44ZLos codificadores compactos afrontan un dilema al leer opcioneshttps://chiosa.app/papers/large-language-models-show-systematic-failures-in-incorporating-evolving-medical-knowledge-2610-30631Chiosaen2026-10-23T10:33:44ZModels can spot changed guidelines without fully updating advicehttps://chiosa.app/papers/large-language-models-show-systematic-failures-in-incorporating-evolving-medical-knowledge-2610-30631?lang=itChiosait2026-10-23T10:33:44ZI modelli riconoscono le linee guida aggiornate senza integrare tutte le modifichehttps://chiosa.app/papers/large-language-models-show-systematic-failures-in-incorporating-evolving-medical-knowledge-2610-30631?lang=frChiosafr2026-10-23T10:33:44ZLes modèles repèrent les directives modifiées sans toujours actualiser leurs conseilshttps://chiosa.app/papers/metacognitive-scaffolds-create-productive-friction-for-ai-assisted-knowledge-work-2610-30945Chiosaen2026-10-23T10:33:44ZAn editable writing scaffold improved brief quality and later promptshttps://chiosa.app/papers/metacognitive-scaffolds-create-productive-friction-for-ai-assisted-knowledge-work-2610-30945?lang=esChiosaes2026-10-23T10:33:44ZUn andamiaje de escritura editable mejoró los informes y las instrucciones posterioreshttps://chiosa.app/papers/metacognitive-scaffolds-create-productive-friction-for-ai-assisted-knowledge-work-2610-30945?lang=frChiosafr2026-10-23T10:33:44ZUn dispositif de guidage améliore les notes et les demandes ultérieureshttps://chiosa.app/papers/not-where-you-think-protecting-location-privacy-against-reasoning-based-geolocation-2610-30439Chiosaen2026-10-23T10:33:44ZModels can locate ordinary photos, and edits can mislead themhttps://chiosa.app/papers/not-where-you-think-protecting-location-privacy-against-reasoning-based-geolocation-2610-30439?lang=esChiosaes2026-10-23T10:33:44ZLos modelos pueden ubicar fotos comunes y las ediciones pueden engañarloshttps://chiosa.app/papers/on-the-token-collapse-phenomenon-in-textual-graph-soft-prompting-2610-31572Chiosaen2026-10-23T10:33:44ZGraph prompts can collapse into nearly identical tokenshttps://chiosa.app/papers/openattention-one-percent-is-enough-with-tail-proxies-2610-30951Chiosaen2026-10-23T10:33:44ZMean proxies help sparse attention retain accuracyhttps://chiosa.app/papers/openattention-one-percent-is-enough-with-tail-proxies-2610-30951?lang=itChiosait2026-10-23T10:33:44ZI proxy della media aiutano l'attenzione sparsa a mantenere l'accuratezzahttps://chiosa.app/papers/openattention-one-percent-is-enough-with-tail-proxies-2610-30951?lang=esChiosaes2026-10-23T10:33:44ZLos resúmenes medios ayudan a conservar la precisión en atención dispersahttps://chiosa.app/papers/openattention-one-percent-is-enough-with-tail-proxies-2610-30951?lang=frChiosafr2026-10-23T10:33:44ZDes proxies de moyenne aident l’attention clairsemée à préserver sa précisionhttps://chiosa.app/papers/opentac-an-open-ecosystem-for-customizable-scalable-and-transferable-3d-tactile-sensing-2610-31661Chiosaen2026-10-23T10:33:44ZOpenTac helps fit tactile skins to varied robot shapeshttps://chiosa.app/papers/opentac-an-open-ecosystem-for-customizable-scalable-and-transferable-3d-tactile-sensing-2610-31661?lang=itChiosait2026-10-23T10:33:44ZOpenTac adatta le skin tattili a forme diverse di robothttps://chiosa.app/papers/opentac-an-open-ecosystem-for-customizable-scalable-and-transferable-3d-tactile-sensing-2610-31661?lang=frChiosafr2026-10-23T10:33:44ZOpenTac adapte les peaux tactiles aux formes des robotshttps://chiosa.app/papers/practical-auditing-of-differentially-private-graph-release-mechanisms-methodology-toolkit-and-case-study-2610-31294Chiosaen2026-10-23T10:33:44ZAudits found privacy violations in the tested PrivDPR codehttps://chiosa.app/papers/practical-auditing-of-differentially-private-graph-release-mechanisms-methodology-toolkit-and-case-study-2610-31294?lang=itChiosait2026-10-23T10:33:44ZGli audit hanno rilevato violazioni di privacy nel codice testato di PrivDPRhttps://chiosa.app/papers/practical-auditing-of-differentially-private-graph-release-mechanisms-methodology-toolkit-and-case-study-2610-31294?lang=esChiosaes2026-10-23T10:33:44ZLa auditoría detectó fallos de privacidad en el código probado de PrivDPRhttps://chiosa.app/papers/practical-auditing-of-differentially-private-graph-release-mechanisms-methodology-toolkit-and-case-study-2610-31294?lang=frChiosafr2026-10-23T10:33:44ZL’audit révèle des violations de confidentialité dans le code PrivDPR testéhttps://chiosa.app/papers/productwebbench-evaluating-website-continuity-beyond-task-completion-2610-31118Chiosaen2026-10-23T10:33:44ZFrontend changes can pass while website behavior breakshttps://chiosa.app/papers/productwebbench-evaluating-website-continuity-beyond-task-completion-2610-31118?lang=itChiosait2026-10-23T10:33:44ZLe modifiche frontend possono superare i controlli mentre il sito si rompehttps://chiosa.app/papers/productwebbench-evaluating-website-continuity-beyond-task-completion-2610-31118?lang=esChiosaes2026-10-23T10:33:44ZLos cambios frontend pueden pasar mientras se rompe el sitiohttps://chiosa.app/papers/productwebbench-evaluating-website-continuity-beyond-task-completion-2610-31118?lang=frChiosafr2026-10-23T10:33:44ZUne modification frontend peut réussir tandis que le site se dégradehttps://chiosa.app/papers/repair-changes-which-layers-to-remove-exact-optima-for-depth-pruning-of-language-models-2610-30552Chiosaen2026-10-23T10:33:44ZLayer repair changes which transformer layers are best to removehttps://chiosa.app/papers/repair-changes-which-layers-to-remove-exact-optima-for-depth-pruning-of-language-models-2610-30552?lang=itChiosait2026-10-23T10:33:44ZLa riparazione cambia quali strati del transformer conviene rimuoverehttps://chiosa.app/papers/repair-changes-which-layers-to-remove-exact-optima-for-depth-pruning-of-language-models-2610-30552?lang=esChiosaes2026-10-23T10:33:44ZLa reparación cambia qué capas de un transformer conviene eliminarhttps://chiosa.app/papers/repair-changes-which-layers-to-remove-exact-optima-for-depth-pruning-of-language-models-2610-30552?lang=frChiosafr2026-10-23T10:33:44ZLa réparation change les couches du transformeur qu’il vaut mieux retirerhttps://chiosa.app/papers/same-luck-different-choice-twin-world-credit-assignment-for-agentic-reinforcement-learning-2610-31544Chiosaen2026-10-23T10:33:44ZTwice gives search agents credit for individual turnshttps://chiosa.app/papers/same-luck-different-choice-twin-world-credit-assignment-for-agentic-reinforcement-learning-2610-31544?lang=itChiosait2026-10-23T10:33:44ZTwice attribuisce agli agenti di ricerca il merito dei singoli turnihttps://chiosa.app/papers/same-luck-different-choice-twin-world-credit-assignment-for-agentic-reinforcement-learning-2610-31544?lang=esChiosaes2026-10-23T10:33:44ZTwice asigna crédito a cada turno de los agentes de búsquedahttps://chiosa.app/papers/same-luck-different-choice-twin-world-credit-assignment-for-agentic-reinforcement-learning-2610-31544?lang=frChiosafr2026-10-23T10:33:44ZTwice attribue aux agents de recherche le mérite de chaque tourhttps://chiosa.app/papers/scaling-laws-for-safety-probes-2610-30678Chiosaen2026-10-23T10:33:44ZScaling laws estimate safety-probe error from size and labelshttps://chiosa.app/papers/simulated-control-evals-2610-31593Chiosaen2026-10-23T10:33:44ZSimulated tests predict safeguards on deployment taskshttps://chiosa.app/papers/simulated-control-evals-2610-31593?lang=esChiosaes2026-10-23T10:33:44ZPruebas simuladas predicen salvaguardas en tareas realeshttps://chiosa.app/papers/simulated-control-evals-2610-31593?lang=frChiosafr2026-10-23T10:33:44ZDes tests simulés évaluent les garde-fous en déploiementhttps://chiosa.app/papers/the-delegation-divide-an-open-index-of-ai-agent-delegability-across-occupations-and-countries-2610-30752Chiosaen2026-10-23T10:33:44ZAn index maps which work AI agents could take over worldwidehttps://chiosa.app/papers/the-delegation-divide-an-open-index-of-ai-agent-delegability-across-occupations-and-countries-2610-30752?lang=itChiosait2026-10-23T10:33:44ZUn indice mappa quali lavori potrebbero essere svolti dagli agenti AI nel mondohttps://chiosa.app/papers/the-delegation-divide-an-open-index-of-ai-agent-delegability-across-occupations-and-countries-2610-30752?lang=esChiosaes2026-10-23T10:33:44ZUn índice traza qué trabajos podrían asumir los agentes de IA en el mundohttps://chiosa.app/papers/the-delegation-divide-an-open-index-of-ai-agent-delegability-across-occupations-and-countries-2610-30752?lang=frChiosafr2026-10-23T10:33:44ZUn indice cartographie le travail que les agents IA pourraient reprendre dans le mondehttps://chiosa.app/papers/the-value-of-system-1-models-in-medicine-2610-31315Chiosaen2026-10-23T10:33:44ZClinical model scores vary by decision typehttps://chiosa.app/papers/the-value-of-system-1-models-in-medicine-2610-31315?lang=esChiosaes2026-10-23T10:33:44ZLas puntuaciones clínicas varían según el tipo de decisiónhttps://chiosa.app/papers/the-value-of-system-1-models-in-medicine-2610-31315?lang=frChiosafr2026-10-23T10:33:44ZLes scores des modèles cliniques varient selon le type de décisionhttps://chiosa.app/papers/trace2world-reconstructing-app-worlds-from-real-interaction-traces-for-scalable-gui-agent-training-2610-30792Chiosaen2026-10-23T10:33:44ZTrace2World builds browser app worlds from interaction traceshttps://chiosa.app/papers/trace2world-reconstructing-app-worlds-from-real-interaction-traces-for-scalable-gui-agent-training-2610-30792?lang=itChiosait2026-10-23T10:33:44ZTrace2World crea ambienti browser per app da tracce di interazionehttps://chiosa.app/papers/trace2world-reconstructing-app-worlds-from-real-interaction-traces-for-scalable-gui-agent-training-2610-30792?lang=frChiosafr2026-10-23T10:33:44ZTrace2World reconstruit des applications dans un navigateur à partir de traceshttps://chiosa.app/papers/ulme-predictive-coupling-for-mutually-reinforcing-embedding-and-language-modeling-2610-30705Chiosaen2026-10-23T10:33:44ZOne language model learns retrieval and answer generationhttps://chiosa.app/papers/ulme-predictive-coupling-for-mutually-reinforcing-embedding-and-language-modeling-2610-30705?lang=itChiosait2026-10-23T10:33:44ZUn modello linguistico impara retrieval e generazione di rispostehttps://chiosa.app/papers/ulme-predictive-coupling-for-mutually-reinforcing-embedding-and-language-modeling-2610-30705?lang=frChiosafr2026-10-23T10:33:44ZUn modèle de langage apprend la recherche et la génération de réponseshttps://chiosa.app/papers/alps-visual-state-encoders-for-world-models-2610-30120Chiosaen2026-10-22T18:08:13ZOne visual encoder links scene state, language, and image detailhttps://chiosa.app/papers/did-the-problem-actually-get-harder-testing-llms-on-paired-p-and-np-complete-problems-hardened-by-adversarial-2610-30103Chiosaen2026-10-22T18:08:13ZModels solve fewer problems after a structural change makes them NP-completehttps://chiosa.app/papers/did-the-problem-actually-get-harder-testing-llms-on-paired-p-and-np-complete-problems-hardened-by-adversarial-2610-30103?lang=esChiosaes2026-10-22T18:08:13ZLos modelos resuelven menos problemas cuando un cambio estructural los vuelve NP-completoshttps://chiosa.app/papers/donic-generalist-dexterous-bimanual-tracking-control-from-human-demonstrations-2610-29997Chiosaen2026-10-22T18:08:13ZOne robot policy tracks two-hand object referenceshttps://chiosa.app/papers/donic-generalist-dexterous-bimanual-tracking-control-from-human-demonstrations-2610-29997?lang=itChiosait2026-10-22T18:08:13ZUna policy robotica segue riferimenti a oggetti con due manihttps://chiosa.app/papers/donic-generalist-dexterous-bimanual-tracking-control-from-human-demonstrations-2610-29997?lang=esChiosaes2026-10-22T18:08:13ZUna política robótica sigue referencias de objetos con dos manoshttps://chiosa.app/papers/donic-generalist-dexterous-bimanual-tracking-control-from-human-demonstrations-2610-29997?lang=frChiosafr2026-10-22T18:08:13ZUn contrôleur robotique suit des références à deux mainshttps://chiosa.app/papers/dynaweave-learning-to-adapt-through-dynamic-scenario-synthesis-2610-29443Chiosaen2026-10-22T18:08:13ZDynaWeave turns static tool tasks into changing scenarioshttps://chiosa.app/papers/dynaweave-learning-to-adapt-through-dynamic-scenario-synthesis-2610-29443?lang=esChiosaes2026-10-22T18:08:13ZDynaWeave convierte tareas estáticas con herramientas en escenarios cambianteshttps://chiosa.app/papers/encoded-but-unused-bounding-the-sources-of-vlm-errors-2610-29241Chiosaen2026-10-22T18:08:13ZMany tested object-attribute errors show detectable visual evidencehttps://chiosa.app/papers/encoded-but-unused-bounding-the-sources-of-vlm-errors-2610-29241?lang=esChiosaes2026-10-22T18:08:13ZMuchos errores evaluados sobre atributos de objetos muestran evidencia visual detectablehttps://chiosa.app/papers/environment-duels-a-microcosm-of-llm-co-evolution-2610-30116Chiosaen2026-10-22T18:08:13ZModels that solve best are not always the best task authorshttps://chiosa.app/papers/environment-duels-a-microcosm-of-llm-co-evolution-2610-30116?lang=itChiosait2026-10-22T18:08:13ZI modelli che risolvono meglio non sono sempre i migliori autori di compiti