https://chiosa.app/papers/low-latency-mapless-esdf-queries-for-mav-reactive-collision-avoidance-2610-13158Chiosaen2026-10-14T12:56:53ZA mapless depth-image query speeds reactive drone collision checkshttps://chiosa.app/papers/low-latency-mapless-esdf-queries-for-mav-reactive-collision-avoidance-2610-13158?lang=itChiosait2026-10-14T12:56:53ZQuery dirette su immagini di profondità accelerano il rilevamento ostacoli per droni reattivihttps://chiosa.app/papers/low-latency-mapless-esdf-queries-for-mav-reactive-collision-avoidance-2610-13158?lang=esChiosaes2026-10-14T12:56:53ZConsultas de profundidad sin mapas aceleran las comprobaciones de colisión de droneshttps://chiosa.app/papers/low-latency-mapless-esdf-queries-for-mav-reactive-collision-avoidance-2610-13158?lang=frChiosafr2026-10-14T12:56:53ZUne requête d'images de profondeur sans carte accélère l'évitement d'obstacles réactif pour droneshttps://chiosa.app/papers/magicbeanstalk-escaping-the-rlvr-zero-advantage-trap-with-recursive-data-synthesis-2610-12659Chiosaen2026-10-14T12:56:53ZMagicBeanStalk builds training problems from hard seedshttps://chiosa.app/papers/magicbeanstalk-escaping-the-rlvr-zero-advantage-trap-with-recursive-data-synthesis-2610-12659?lang=esChiosaes2026-10-14T12:56:53ZMagicBeanStalk crea problemas de entrenamiento desde semillas difícileshttps://chiosa.app/papers/magicbeanstalk-escaping-the-rlvr-zero-advantage-trap-with-recursive-data-synthesis-2610-12659?lang=frChiosafr2026-10-14T12:56:53ZMagicBeanStalk construit des problèmes d'entraînement à partir de graines difficileshttps://chiosa.app/papers/reward-hacking-as-a-transmissible-strategy-in-multi-agent-coding-systems-2610-12822Chiosaen2026-10-14T12:56:53ZReward hacking can spread from one coding agent to othershttps://chiosa.app/papers/reward-hacking-as-a-transmissible-strategy-in-multi-agent-coding-systems-2610-12822?lang=itChiosait2026-10-14T12:56:53ZIl reward hacking può diffondersi da un agente di coding ad altrihttps://chiosa.app/papers/reward-hacking-as-a-transmissible-strategy-in-multi-agent-coding-systems-2610-12822?lang=frChiosafr2026-10-14T12:56:53ZLe reward hacking peut se propager d'un agent de codage à d'autreshttps://chiosa.app/papers/standing-not-truth-who-may-say-what-about-whom-in-agentic-selection-2610-17952Chiosaen2026-10-14T11:47:55ZA publisher’s identity can help agents screen claims about rivals (F1)https://chiosa.app/papers/standing-not-truth-who-may-say-what-about-whom-in-agentic-selection-2610-17952?lang=itChiosait2026-10-14T11:47:55ZL'identità di un publisher può aiutare gli agenti a filtrare le affermazioni sui rivali (F1)https://chiosa.app/papers/standing-not-truth-who-may-say-what-about-whom-in-agentic-selection-2610-17952?lang=esChiosaes2026-10-14T11:47:55ZLa identidad del editor puede ayudar a filtrar afirmaciones sobre rivales (F1)https://chiosa.app/papers/standing-not-truth-who-may-say-what-about-whom-in-agentic-selection-2610-17952?lang=frChiosafr2026-10-14T11:47:55ZL'identité d'un éditeur peut aider les agents à filtrer les affirmations sur des concurrents (F1)https://chiosa.app/papers/tablevision-recovering-verified-structure-from-historical-tables-2610-17448Chiosaen2026-10-14T11:47:55ZTableVision uses printed sums to check scanned table structurehttps://chiosa.app/papers/tablevision-recovering-verified-structure-from-historical-tables-2610-17448?lang=itChiosait2026-10-14T11:47:55ZTableVision usa le somme stampate per verificare la struttura delle tabelle scansionatehttps://chiosa.app/papers/tablevision-recovering-verified-structure-from-historical-tables-2610-17448?lang=esChiosaes2026-10-14T11:47:55ZTableVision usa sumas impresas para comprobar la estructura de tablas escaneadashttps://chiosa.app/papers/tacitvla-reasoning-vision-language-action-models-without-reasoning-latency-2610-17540Chiosaen2026-10-14T11:47:55ZTacitVLA trains on reasoning so actions need not wait for its generated texthttps://chiosa.app/papers/tacitvla-reasoning-vision-language-action-models-without-reasoning-latency-2610-17540?lang=itChiosait2026-10-14T11:47:55ZTacitVLA si addestra sul ragionamento, così le azioni non devono attendere il testo generatohttps://chiosa.app/papers/tacitvla-reasoning-vision-language-action-models-without-reasoning-latency-2610-17540?lang=esChiosaes2026-10-14T11:47:55ZTacitVLA entrena con razonamiento para que las acciones no esperen al texto generadohttps://chiosa.app/papers/tacitvla-reasoning-vision-language-action-models-without-reasoning-latency-2610-17540?lang=frChiosafr2026-10-14T11:47:55ZTacitVLA apprend du raisonnement sans faire attendre les actions pour le texte généréhttps://chiosa.app/papers/the-agent-knew-internal-states-forecast-unauthorized-actions-and-stop-them-and-the-lies-that-follow-2610-17301Chiosaen2026-10-14T11:47:55ZChecking each action can stop agents’ unauthorized actshttps://chiosa.app/papers/the-agent-knew-internal-states-forecast-unauthorized-actions-and-stop-them-and-the-lies-that-follow-2610-17301?lang=itChiosait2026-10-14T11:47:55ZControllare ogni azione può fermare le violazioni degli agentihttps://chiosa.app/papers/the-agent-knew-internal-states-forecast-unauthorized-actions-and-stop-them-and-the-lies-that-follow-2610-17301?lang=esChiosaes2026-10-14T11:47:55ZVerificar cada acción puede detener los actos no autorizados de los agenteshttps://chiosa.app/papers/the-agent-knew-internal-states-forecast-unauthorized-actions-and-stop-them-and-the-lies-that-follow-2610-17301?lang=frChiosafr2026-10-14T11:47:55ZVérifier chaque action peut stopper les actes non autorisés des agentshttps://chiosa.app/papers/twinprune-density-aware-two-phase-token-pruning-for-vision-language-models-2610-17530Chiosaen2026-10-14T11:47:55ZOne signal prunes visual tokens across two stageshttps://chiosa.app/papers/twinprune-density-aware-two-phase-token-pruning-for-vision-language-models-2610-17530?lang=itChiosait2026-10-14T11:47:55ZUn solo segnale per ridurre i token visivi in due fasihttps://chiosa.app/papers/twinprune-density-aware-two-phase-token-pruning-for-vision-language-models-2610-17530?lang=esChiosaes2026-10-14T11:47:55ZUna señal poda tokens visuales en dos etapashttps://chiosa.app/papers/twinprune-density-aware-two-phase-token-pruning-for-vision-language-models-2610-17530?lang=frChiosafr2026-10-14T11:47:55ZUn signal pour élaguer les jetons visuels à deux étapeshttps://chiosa.app/papers/two-clocks-why-larger-rollout-batches-learn-slower-but-fail-on-schedule-in-reinforcement-learning-with-verifi-2610-18853Chiosaen2026-10-14T11:47:55ZWhy larger rollouts learn slower but fail on a similar schedulehttps://chiosa.app/papers/two-clocks-why-larger-rollout-batches-learn-slower-but-fail-on-schedule-in-reinforcement-learning-with-verifi-2610-18853?lang=itChiosait2026-10-14T11:47:55ZPerché i rollout più grandi rallentano l’apprendimento ma falliscono dopo un numero simile di rispostehttps://chiosa.app/papers/two-clocks-why-larger-rollout-batches-learn-slower-but-fail-on-schedule-in-reinforcement-learning-with-verifi-2610-18853?lang=esChiosaes2026-10-14T11:47:55ZPor qué los rollouts grandes aprenden más lento y fallan en plazos similareshttps://chiosa.app/papers/two-clocks-why-larger-rollout-batches-learn-slower-but-fail-on-schedule-in-reinforcement-learning-with-verifi-2610-18853?lang=frChiosafr2026-10-14T11:47:55ZPourquoi les grands rollouts ralentissent l’apprentissage, mais échouent à peu près au même momenthttps://chiosa.app/papers/videojev-system-one-model-for-video-understanding-and-interaction-2610-18615Chiosaen2026-10-14T11:47:55ZVideoJev scores video decisions directly and reuses contexthttps://chiosa.app/papers/videojev-system-one-model-for-video-understanding-and-interaction-2610-18615?lang=itChiosait2026-10-14T11:47:55ZVideoJev valuta le decisioni video direttamente e riutilizza il contestohttps://chiosa.app/papers/videojev-system-one-model-for-video-understanding-and-interaction-2610-18615?lang=esChiosaes2026-10-14T11:47:55ZVideoJev puntúa decisiones sobre videos y reutiliza el contextohttps://chiosa.app/papers/videojev-system-one-model-for-video-understanding-and-interaction-2610-18615?lang=frChiosafr2026-10-14T11:47:55ZVideoJev note directement les décisions vidéo et réutilise le contextehttps://chiosa.app/papers/virtual-head-attention-2610-17959Chiosaen2026-10-14T11:47:55ZVirtual Head Attention cuts cache while preserving model qualityhttps://chiosa.app/papers/virtual-head-attention-2610-17959?lang=itChiosait2026-10-14T11:47:55ZVirtual Head Attention riduce la cache preservando la qualità del modellohttps://chiosa.app/papers/virtual-head-attention-2610-17959?lang=esChiosaes2026-10-14T11:47:55ZVirtual Head Attention reduce la caché sin sacrificar la calidad del modelohttps://chiosa.app/papers/virtual-head-attention-2610-17959?lang=frChiosafr2026-10-14T11:47:55ZVirtual Head Attention réduit le cache tout en préservant la qualité du modèlehttps://chiosa.app/papers/vulnbump-a-benchmark-for-fixing-vulnerable-dependencies-in-real-projects-while-keeping-the-build-and-tests-gr-2610-17374Chiosaen2026-10-14T11:47:55ZVulnerability fixes must also keep projects workinghttps://chiosa.app/papers/vulnbump-a-benchmark-for-fixing-vulnerable-dependencies-in-real-projects-while-keeping-the-build-and-tests-gr-2610-17374?lang=itChiosait2026-10-14T11:47:55ZLe correzioni di sicurezza devono mantenere funzionanti i progettihttps://chiosa.app/papers/vulnbump-a-benchmark-for-fixing-vulnerable-dependencies-in-real-projects-while-keeping-the-build-and-tests-gr-2610-17374?lang=esChiosaes2026-10-14T11:47:55ZCorregir vulnerabilidades también exige mantener los proyectos en funcionamientohttps://chiosa.app/papers/web-scale-on-a-budget-tail-aware-retrieval-evaluation-2610-17986Chiosaen2026-10-14T11:47:55ZSmall samples predict large image retrieval scoreshttps://chiosa.app/papers/web-scale-on-a-budget-tail-aware-retrieval-evaluation-2610-17986?lang=itChiosait2026-10-14T11:47:55ZPiccoli campioni per prevedere i punteggi di retrieval su larga scalahttps://chiosa.app/papers/web-scale-on-a-budget-tail-aware-retrieval-evaluation-2610-17986?lang=esChiosaes2026-10-14T11:47:55ZMuestras pequeñas predicen puntuaciones en recuperación de imágenes a gran escalahttps://chiosa.app/papers/web-scale-on-a-budget-tail-aware-retrieval-evaluation-2610-17986?lang=frChiosafr2026-10-14T11:47:55ZDe petits échantillons pour prédire les scores de recherche d'images à grande échellehttps://chiosa.app/papers/flashmask-3-efficient-and-expressive-mask-aware-distributed-attention-2610-17901Chiosaen2026-10-14T11:47:52ZFlashMask-3 speeds structured attention across GPUshttps://chiosa.app/papers/flashmask-3-efficient-and-expressive-mask-aware-distributed-attention-2610-17901?lang=frChiosafr2026-10-14T11:47:52ZFlashMask-3 accélère l'attention structurée sur plusieurs GPUhttps://chiosa.app/papers/greenwam-a-world-foundation-model-for-robotics-2610-18774Chiosaen2026-10-14T11:47:52ZOne robot model learns to predict scenes and control varied robotshttps://chiosa.app/papers/greenwam-a-world-foundation-model-for-robotics-2610-18774?lang=itChiosait2026-10-14T11:47:52ZUn solo modello impara a prevedere le scene e a controllare robot diversihttps://chiosa.app/papers/greenwam-a-world-foundation-model-for-robotics-2610-18774?lang=esChiosaes2026-10-14T11:47:52ZUn modelo robótico aprende a predecir escenas y controlar distintos robotshttps://chiosa.app/papers/greenwam-a-world-foundation-model-for-robotics-2610-18774?lang=frChiosafr2026-10-14T11:47:52ZUn modèle robotique apprend à prédire les scènes et à contrôler différents robotshttps://chiosa.app/papers/interaction-as-a-judge-2610-17895Chiosaen2026-10-14T11:47:52ZInteraction traces help judges assess software as people use ithttps://chiosa.app/papers/interaction-as-a-judge-2610-17895?lang=itChiosait2026-10-14T11:47:52ZLe interaction trace aiutano i giudici a valutare il software durante l'uso realehttps://chiosa.app/papers/interaction-as-a-judge-2610-17895?lang=esChiosaes2026-10-14T11:47:52ZLas trazas de interacción ayudan a evaluar el software durante su usohttps://chiosa.app/papers/interaction-as-a-judge-2610-17895?lang=frChiosafr2026-10-14T11:47:52ZLes traces d'interaction aident les juges à évaluer les logiciels tels qu'ils sont utiliséshttps://chiosa.app/papers/kairos-transactional-security-enforcement-at-the-system-boundary-for-llm-agents-2610-18457Chiosaen2026-10-14T11:47:52ZKairos holds uncertain agent actions for later reviewhttps://chiosa.app/papers/kairos-transactional-security-enforcement-at-the-system-boundary-for-llm-agents-2610-18457?lang=itChiosait2026-10-14T11:47:52ZKairos trattiene le azioni incerte dell'agente per una revisione successivahttps://chiosa.app/papers/kairos-transactional-security-enforcement-at-the-system-boundary-for-llm-agents-2610-18457?lang=esChiosaes2026-10-14T11:47:52ZKairos retiene acciones inciertas del agente para revisión posteriorhttps://chiosa.app/papers/kairos-transactional-security-enforcement-at-the-system-boundary-for-llm-agents-2610-18457?lang=frChiosafr2026-10-14T11:47:52ZKairos retient les actions incertaines d'un agent pour examen différéhttps://chiosa.app/papers/meta-video-efficient-standardized-evaluation-for-video-understanding-2610-18535Chiosaen2026-10-14T11:47:52ZVideo scores shift with setup, so Meta-Video records the settingshttps://chiosa.app/papers/meta-video-efficient-standardized-evaluation-for-video-understanding-2610-18535?lang=itChiosait2026-10-14T11:47:52ZI punteggi video cambiano con la configurazione: Meta-Video registra le impostazionihttps://chiosa.app/papers/meta-video-efficient-standardized-evaluation-for-video-understanding-2610-18535?lang=esChiosaes2026-10-14T11:47:52ZLa configuración cambia las puntuaciones de video; Meta-Video la registrahttps://chiosa.app/papers/monarch-muon-structure-aware-muon-for-fast-and-memory-efficient-pretraining-2610-17429Chiosaen2026-10-14T11:47:52ZStructured weights make Muon training cheaper at scalehttps://chiosa.app/papers/monarch-muon-structure-aware-muon-for-fast-and-memory-efficient-pretraining-2610-17429?lang=itChiosait2026-10-14T11:47:52ZI pesi strutturati rendono il training con Muon più economico su larga scalahttps://chiosa.app/papers/monarch-muon-structure-aware-muon-for-fast-and-memory-efficient-pretraining-2610-17429?lang=esChiosaes2026-10-14T11:47:52ZLos pesos estructurados abaratan el entrenamiento con Muon a gran escalahttps://chiosa.app/papers/monarch-muon-structure-aware-muon-for-fast-and-memory-efficient-pretraining-2610-17429?lang=frChiosafr2026-10-14T11:47:52ZDes poids structurés pour réduire le coût de Muon à grande échellehttps://chiosa.app/papers/rethinking-projector-training-in-multimodal-llms-2610-18598Chiosaen2026-10-14T11:47:52ZA projector can align vision and language without captionshttps://chiosa.app/papers/rethinking-projector-training-in-multimodal-llms-2610-18598?lang=itChiosait2026-10-14T11:47:52ZUn projector può allineare visione e linguaggio senza didascaliehttps://chiosa.app/papers/rethinking-projector-training-in-multimodal-llms-2610-18598?lang=esChiosaes2026-10-14T11:47:52ZUn proyector puede alinear visión y lenguaje sin pies de fotohttps://chiosa.app/papers/rethinking-projector-training-in-multimodal-llms-2610-18598?lang=frChiosafr2026-10-14T11:47:52ZUn projecteur peut aligner vision et langage sans légendes d'imageshttps://chiosa.app/papers/revisiting-the-16x-pretraining-compute-gap-between-diffusion-and-autoregressive-language-models-2610-17590Chiosaen2026-10-14T11:47:52ZA revised test narrows the compute gap for diffusion modelshttps://chiosa.app/papers/revisiting-the-16x-pretraining-compute-gap-between-diffusion-and-autoregressive-language-models-2610-17590?lang=itChiosait2026-10-14T11:47:52ZUn confronto aggiornato riduce il divario di calcolo per i modelli a diffusionehttps://chiosa.app/papers/revisiting-the-16x-pretraining-compute-gap-between-diffusion-and-autoregressive-language-models-2610-17590?lang=esChiosaes2026-10-14T11:47:52ZUna comparación revisada reduce la brecha de cómputo de los modelos de difusiónhttps://chiosa.app/papers/revisiting-the-16x-pretraining-compute-gap-between-diffusion-and-autoregressive-language-models-2610-17590?lang=frChiosafr2026-10-14T11:47:52ZUn test révisé réduit l'écart de calcul pour les modèles de diffusionhttps://chiosa.app/papers/same-attack-different-judge-a-first-controlled-adversarial-comparison-of-jev-llm-judges-and-purpose-built-saf-2610-17820Chiosaen2026-10-14T11:47:52ZJev’s resistance to attacks changes across taskshttps://chiosa.app/papers/same-attack-different-judge-a-first-controlled-adversarial-comparison-of-jev-llm-judges-and-purpose-built-saf-2610-17820?lang=esChiosaes2026-10-14T11:47:52ZLa resistencia de Jev a los ataques cambia según la tareahttps://chiosa.app/papers/simplifying-and-improving-on-policy-distillation-for-few-step-diffusion-2610-18472Chiosaen2026-10-14T11:47:52ZA regression method distills generative models into fewer stepshttps://chiosa.app/papers/simplifying-and-improving-on-policy-distillation-for-few-step-diffusion-2610-18472?lang=esChiosaes2026-10-14T11:47:52ZUn método de regresión destila modelos generativos en menos pasoshttps://chiosa.app/papers/active-noise-cancellation-extends-the-benefit-of-ai-speech-enhancement-in-real-hearing-aid-recordings-2610-17287Chiosaen2026-10-14T11:47:48ZANC lets hearing-aid speech enhancement reach lower frequencieshttps://chiosa.app/papers/active-noise-cancellation-extends-the-benefit-of-ai-speech-enhancement-in-real-hearing-aid-recordings-2610-17287?lang=itChiosait2026-10-14T11:47:48ZCon ANC, il miglioramento del parlato raggiunge frequenze più bassehttps://chiosa.app/papers/active-noise-cancellation-extends-the-benefit-of-ai-speech-enhancement-in-real-hearing-aid-recordings-2610-17287?lang=esChiosaes2026-10-14T11:47:48ZLa cancelación de ruido activa permite que la mejora de voz llegue a frecuencias más bajas en audífonoshttps://chiosa.app/papers/active-noise-cancellation-extends-the-benefit-of-ai-speech-enhancement-in-real-hearing-aid-recordings-2610-17287?lang=frChiosafr2026-10-14T11:47:48ZL’ANC étend l’amélioration de la parole des aides auditives vers les basses fréquenceshttps://chiosa.app/papers/aegisasync-symbolic-async-event-simulation-for-risks-in-python-asynchronous-programs-2610-18831Chiosaen2026-10-14T11:47:48ZAegisAsync traces Python async behavior to flag riskshttps://chiosa.app/papers/aegisasync-symbolic-async-event-simulation-for-risks-in-python-asynchronous-programs-2610-18831?lang=itChiosait2026-10-14T11:47:48ZAegisAsync traccia il comportamento async in Python per rilevare i rischihttps://chiosa.app/papers/aegisasync-symbolic-async-event-simulation-for-risks-in-python-asynchronous-programs-2610-18831?lang=esChiosaes2026-10-14T11:47:48ZAegisAsync rastrea el comportamiento asíncrono de Python para detectar riesgoshttps://chiosa.app/papers/aegisasync-symbolic-async-event-simulation-for-risks-in-python-asynchronous-programs-2610-18831?lang=frChiosafr2026-10-14T11:47:48ZAegisAsync suit le comportement asynchrone en Python pour repérer des risqueshttps://chiosa.app/papers/attune-agentic-throughput-tuning-for-llm-training-2610-17528Chiosaen2026-10-14T11:47:48ZA coding agent can tune training recipes while checks guard correctnesshttps://chiosa.app/papers/attune-agentic-throughput-tuning-for-llm-training-2610-17528?lang=itChiosait2026-10-14T11:47:48ZUn agente di coding ottimizza le configurazioni di addestramento con controlli di correttezzahttps://chiosa.app/papers/attune-agentic-throughput-tuning-for-llm-training-2610-17528?lang=esChiosaes2026-10-14T11:47:48ZUn agente de programación puede ajustar recetas de entrenamiento con controles de validezhttps://chiosa.app/papers/attune-agentic-throughput-tuning-for-llm-training-2610-17528?lang=frChiosafr2026-10-14T11:47:48ZUn agent de programmation peut régler les recettes d’entraînement sous contrôle de validitéhttps://chiosa.app/papers/bifocal-training-free-mixed-granularity-visual-token-pruning-for-multimodal-llms-2610-18399Chiosaen2026-10-14T11:47:48ZBifocal preserves image detail and context while trimming visual inputhttps://chiosa.app/papers/bifocal-training-free-mixed-granularity-visual-token-pruning-for-multimodal-llms-2610-18399?lang=itChiosait2026-10-14T11:47:48ZBifocal conserva dettagli e contesto riducendo l’input visivohttps://chiosa.app/papers/bifocal-training-free-mixed-granularity-visual-token-pruning-for-multimodal-llms-2610-18399?lang=esChiosaes2026-10-14T11:47:48ZBifocal conserva detalle y contexto al reducir la entrada visualhttps://chiosa.app/papers/bifocal-training-free-mixed-granularity-visual-token-pruning-for-multimodal-llms-2610-18399?lang=frChiosafr2026-10-14T11:47:48ZBifocal préserve les détails et le contexte de l’image en réduisant les unités visuelleshttps://chiosa.app/papers/calqa-conformal-screening-of-forecast-ensemble-configurations-from-one-mergeable-state-2610-13249Chiosaen2026-10-14T11:47:48ZA compact screen speeds forecast-ensemble searcheshttps://chiosa.app/papers/calqa-conformal-screening-of-forecast-ensemble-configurations-from-one-mergeable-state-2610-13249?lang=itChiosait2026-10-14T11:47:48ZUn riepilogo compatto accelera la ricerca sugli ensemble di previsionihttps://chiosa.app/papers/calqa-conformal-screening-of-forecast-ensemble-configurations-from-one-mergeable-state-2610-13249?lang=esChiosaes2026-10-14T11:47:48ZUn resumen compacto acelera la búsqueda de pronósticos combinadoshttps://chiosa.app/papers/calqa-conformal-screening-of-forecast-ensemble-configurations-from-one-mergeable-state-2610-13249?lang=frChiosafr2026-10-14T11:47:48ZUn filtre compact accélère la recherche de poids pour les prévisionshttps://chiosa.app/papers/compositional-rl-evaluating-and-training-llms-for-collaborative-reasoning-2610-17456Chiosaen2026-10-14T11:47:48ZSolo scores often miss which models help a teamhttps://chiosa.app/papers/compositional-rl-evaluating-and-training-llms-for-collaborative-reasoning-2610-17456?lang=itChiosait2026-10-14T11:47:48ZI punteggi individuali spesso non rivelano quali modelli aiutano il gruppohttps://chiosa.app/papers/compositional-rl-evaluating-and-training-llms-for-collaborative-reasoning-2610-17456?lang=esChiosaes2026-10-14T11:47:48ZLa puntuación individual suele ocultar qué modelos ayudan al equipohttps://chiosa.app/papers/compositional-rl-evaluating-and-training-llms-for-collaborative-reasoning-2610-17456?lang=frChiosafr2026-10-14T11:47:48ZLes scores individuels indiquent mal quels modèles aident l’équipehttps://chiosa.app/papers/dpoison-vl-clean-preference-poisoning-in-vision-language-direct-preference-optimization-2610-18609Chiosaen2026-10-14T11:47:48ZSmall image changes can steer preference traininghttps://chiosa.app/papers/dpoison-vl-clean-preference-poisoning-in-vision-language-direct-preference-optimization-2610-18609?lang=esChiosaes2026-10-14T11:47:48ZPequeños cambios en imágenes pueden orientar el entrenamiento por preferenciashttps://chiosa.app/papers/dpoison-vl-clean-preference-poisoning-in-vision-language-direct-preference-optimization-2610-18609?lang=frChiosafr2026-10-14T11:47:48ZDe légères modifications d’image peuvent orienter l’entraînement par préférenceshttps://chiosa.app/papers/enabling-token-wise-importance-sampling-in-off-policy-rlvr-with-proximal-kl-regularization-2610-18738Chiosaen2026-10-14T11:47:48ZA token-wise update keeps stale-response training aimed at its targethttps://chiosa.app/papers/enabling-token-wise-importance-sampling-in-off-policy-rlvr-with-proximal-kl-regularization-2610-18738?lang=itChiosait2026-10-14T11:47:48ZUn aggiornamento per token mantiene l'obiettivo con risposte precedentihttps://chiosa.app/papers/enabling-token-wise-importance-sampling-in-off-policy-rlvr-with-proximal-kl-regularization-2610-18738?lang=esChiosaes2026-10-14T11:47:48ZUna actualización por token mantiene el objetivo al entrenar con respuestas antiguashttps://chiosa.app/papers/enabling-token-wise-importance-sampling-in-off-policy-rlvr-with-proximal-kl-regularization-2610-18738?lang=frChiosafr2026-10-14T11:47:48ZUne mise à jour token par token maintient l'entraînement sur réponses obsolètes orienté vers sa ciblehttps://chiosa.app/papers/evidence-first-agentic-verification-2610-17724Chiosaen2026-10-14T11:47:48ZSauron checks evidence before judging agent taskshttps://chiosa.app/papers/evidence-first-agentic-verification-2610-17724?lang=itChiosait2026-10-14T11:47:48ZSauron verifica le prove prima di giudicare le attività degli agentihttps://chiosa.app/papers/evidence-first-agentic-verification-2610-17724?lang=esChiosaes2026-10-14T11:47:48ZSauron verifica pruebas antes de evaluar tareas de agenteshttps://chiosa.app/papers/evidence-first-agentic-verification-2610-17724?lang=frChiosafr2026-10-14T11:47:48ZSauron vérifie les preuves avant de juger les tâches agentshttps://chiosa.app/papers/fan-out-or-relay-ownership-aware-routing-for-llm-agents-querying-siloed-tables-2610-18157Chiosaen2026-10-14T11:47:48ZA schema draft helps route questions across private data ownershttps://chiosa.app/papers/fan-out-or-relay-ownership-aware-routing-for-llm-agents-querying-siloed-tables-2610-18157?lang=itChiosait2026-10-14T11:47:48ZUna bozza basata sugli schemi guida le domande tra proprietari di dati privatihttps://chiosa.app/papers/fan-out-or-relay-ownership-aware-routing-for-llm-agents-querying-siloed-tables-2610-18157?lang=esChiosaes2026-10-14T11:47:48ZUn borrador de consulta ayuda a dirigir preguntas entre propietarios de datos privadoshttps://chiosa.app/papers/fan-out-or-relay-ownership-aware-routing-for-llm-agents-querying-siloed-tables-2610-18157?lang=frChiosafr2026-10-14T11:47:48ZUn brouillon de schéma aide à orienter les questions vers des détenteurs de données privées