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 conséquences