https://chiosa.app/papers/can-ai-forecast-the-extremes-evaluating-probabilistic-weather-models-on-tropical-cyclones-2610-22077Chiosaen2026-10-16T15:24:26ZCyclone forecasts can locate storms while missing strengthhttps://chiosa.app/papers/can-ai-forecast-the-extremes-evaluating-probabilistic-weather-models-on-tropical-cyclones-2610-22077?lang=itChiosait2026-10-16T15:24:26ZLe previsioni dei cicloni localizzano le tempeste, ma ne sottostimano l’intensitàhttps://chiosa.app/papers/can-ai-forecast-the-extremes-evaluating-probabilistic-weather-models-on-tropical-cyclones-2610-22077?lang=frChiosafr2026-10-16T15:24:26ZLes modèles localisent les cyclones, mais manquent leur intensitéhttps://chiosa.app/papers/fde-gym-the-last-mile-of-ai-delivery-2610-22350Chiosaen2026-10-16T15:24:26ZA customer-delivery benchmark finds agents struggle to build effective systemshttps://chiosa.app/papers/fde-gym-the-last-mile-of-ai-delivery-2610-22350?lang=itChiosait2026-10-16T15:24:26ZUn benchmark sulla delivery ai clienti mostra le difficoltà degli agentihttps://chiosa.app/papers/fde-gym-the-last-mile-of-ai-delivery-2610-22350?lang=esChiosaes2026-10-16T15:24:26ZUna prueba de entrega para clientes revela dificultades para crear sistemas eficaceshttps://chiosa.app/papers/fde-gym-the-last-mile-of-ai-delivery-2610-22350?lang=frChiosafr2026-10-16T15:24:26ZUn benchmark de livraison client révèle les difficultés des agents à construire des systèmes efficaceshttps://chiosa.app/papers/anvil-scaling-embodied-spatial-intelligence-through-knowledge-recirculation-2610-21882Chiosaen2026-10-16T11:56:00ZAnvil links spatial learning across related taskshttps://chiosa.app/papers/anvil-scaling-embodied-spatial-intelligence-through-knowledge-recirculation-2610-21882?lang=esChiosaes2026-10-16T11:56:00ZAnvil conecta el aprendizaje espacial entre tareas relacionadashttps://chiosa.app/papers/anvil-scaling-embodied-spatial-intelligence-through-knowledge-recirculation-2610-21882?lang=frChiosafr2026-10-16T11:56:00ZAnvil relie l’apprentissage spatial entre tâches procheshttps://chiosa.app/papers/bitsparse-compact-activation-storage-for-relu-feed-forward-networks-2610-22256Chiosaen2026-10-16T11:56:00ZBitSparse stores sparse feed-forward activations with less memoryhttps://chiosa.app/papers/bitsparse-compact-activation-storage-for-relu-feed-forward-networks-2610-22256?lang=itChiosait2026-10-16T11:56:00ZBitSparse riduce la memoria per archiviare attivazioni feed-forward sparsehttps://chiosa.app/papers/bitsparse-compact-activation-storage-for-relu-feed-forward-networks-2610-22256?lang=esChiosaes2026-10-16T11:56:00ZBitSparse reduce la memoria al guardar activaciones feed-forward dispersashttps://chiosa.app/papers/bitsparse-compact-activation-storage-for-relu-feed-forward-networks-2610-22256?lang=frChiosafr2026-10-16T11:56:00ZBitSparse stocke les activations feed-forward parcimonieuses en utilisant moins de mémoirehttps://chiosa.app/papers/combining-llm-and-dynamic-analysis-for-universal-testing-of-static-application-security-testing-tools-2610-21309Chiosaen2026-10-16T11:56:00ZLLM4SAST finds faults in six Java and Rust code analyzershttps://chiosa.app/papers/combining-llm-and-dynamic-analysis-for-universal-testing-of-static-application-security-testing-tools-2610-21309?lang=itChiosait2026-10-16T11:56:00ZLLM4SAST segnala difetti in sei analizzatori di codice per Java e Rusthttps://chiosa.app/papers/combining-llm-and-dynamic-analysis-for-universal-testing-of-static-application-security-testing-tools-2610-21309?lang=esChiosaes2026-10-16T11:56:00ZLLM4SAST detecta fallos en seis analizadores de código para Java y Rusthttps://chiosa.app/papers/flashattention-with-statistics-low-overhead-attention-features-for-compact-hallucination-detectors-2610-21483Chiosaen2026-10-16T11:56:00ZAttention statistics can be extracted without storing full matriceshttps://chiosa.app/papers/flashattention-with-statistics-low-overhead-attention-features-for-compact-hallucination-detectors-2610-21483?lang=itChiosait2026-10-16T11:56:00ZEstrarre statistiche di attenzione senza memorizzare matrici completehttps://chiosa.app/papers/flashattention-with-statistics-low-overhead-attention-features-for-compact-hallucination-detectors-2610-21483?lang=esChiosaes2026-10-16T11:56:00ZExtraer estadísticas de atención sin guardar matrices completashttps://chiosa.app/papers/flashattention-with-statistics-low-overhead-attention-features-for-compact-hallucination-detectors-2610-21483?lang=frChiosafr2026-10-16T11:56:00ZExtraire les statistiques d’attention sans stocker les matrices complèteshttps://chiosa.app/papers/intern-decision-efficient-multimodal-decision-making-with-autoregressive-masked-language-modeling-2610-22131Chiosaen2026-10-16T11:56:00ZIntern-Decision scores structured choices in one passhttps://chiosa.app/papers/intern-decision-efficient-multimodal-decision-making-with-autoregressive-masked-language-modeling-2610-22131?lang=itChiosait2026-10-16T11:56:00ZIntern-Decision valuta scelte strutturate in un passaggiohttps://chiosa.app/papers/intern-decision-efficient-multimodal-decision-making-with-autoregressive-masked-language-modeling-2610-22131?lang=esChiosaes2026-10-16T11:56:00ZIntern-Decision puntúa opciones estructuradas en una pasadahttps://chiosa.app/papers/intern-decision-efficient-multimodal-decision-making-with-autoregressive-masked-language-modeling-2610-22131?lang=frChiosafr2026-10-16T11:56:00ZIntern-Decision évalue les choix structurés en un passagehttps://chiosa.app/papers/language-is-a-security-surface-for-agent-guards-2610-22244Chiosaen2026-10-16T11:56:00ZTranslated requests can change agent guards’ safety verdictshttps://chiosa.app/papers/language-is-a-security-surface-for-agent-guards-2610-22244?lang=itChiosait2026-10-16T11:56:00ZLe richieste tradotte possono cambiare i verdetti di sicurezza dei controlli per agentihttps://chiosa.app/papers/language-is-a-security-surface-for-agent-guards-2610-22244?lang=esChiosaes2026-10-16T11:56:00ZLas peticiones traducidas pueden cambiar los veredictos de seguridad de los protectores de agenteshttps://chiosa.app/papers/language-is-a-security-surface-for-agent-guards-2610-22244?lang=frChiosafr2026-10-16T11:56:00ZLes demandes traduites peuvent modifier les verdicts de sécurité des garde-fous d’agentshttps://chiosa.app/papers/multi-sft-permissively-licensed-multilingual-instruction-following-2610-22086Chiosaen2026-10-16T11:56:00ZMulti-SFT builds permissive instruction data across five languageshttps://chiosa.app/papers/multi-sft-permissively-licensed-multilingual-instruction-following-2610-22086?lang=frChiosafr2026-10-16T11:56:00ZMulti-SFT crée des données d’instructions permissives dans cinq langueshttps://chiosa.app/papers/opengcua-toward-a-fully-open-stack-for-generalist-computer-use-2610-22200Chiosaen2026-10-16T11:56:00ZOpenGCUA lets computer agents coordinate screen and shell actions in codehttps://chiosa.app/papers/opengcua-toward-a-fully-open-stack-for-generalist-computer-use-2610-22200?lang=esChiosaes2026-10-16T11:56:00ZOpenGCUA permite que agentes informáticos coordinen acciones de pantalla y shell mediante códigohttps://chiosa.app/papers/shortcut-sensitivity-in-agent-safety-evaluations-2610-22243Chiosaen2026-10-16T11:56:00ZA three-level audit tests when safety cues sway guard decisionshttps://chiosa.app/papers/shortcut-sensitivity-in-agent-safety-evaluations-2610-22243?lang=itChiosait2026-10-16T11:56:00ZUn audit in tre livelli verifica quando gli indizi di sicurezza influenzano i guardhttps://chiosa.app/papers/shortcut-sensitivity-in-agent-safety-evaluations-2610-22243?lang=esChiosaes2026-10-16T11:56:00ZUna auditoría en tres niveles prueba cuándo las pistas de seguridad alteran decisiones de guardshttps://chiosa.app/papers/shortcut-sensitivity-in-agent-safety-evaluations-2610-22243?lang=frChiosafr2026-10-16T11:56:00ZUn audit en trois niveaux teste l’influence des indices de sécurité sur les garde-foushttps://chiosa.app/papers/skip-the-compressor-coding-agents-fix-more-issues-when-they-pick-whole-files-2610-21073Chiosaen2026-10-16T11:56:00ZWhole-file context helps coding agents resolve more issueshttps://chiosa.app/papers/skip-the-compressor-coding-agents-fix-more-issues-when-they-pick-whole-files-2610-21073?lang=itChiosait2026-10-16T11:56:00ZIl contesto con file interi aiuta gli agenti a risolvere più problemihttps://chiosa.app/papers/skip-the-compressor-coding-agents-fix-more-issues-when-they-pick-whole-files-2610-21073?lang=esChiosaes2026-10-16T11:56:00ZEl contexto con archivos completos ayuda a resolver más problemas de códigohttps://chiosa.app/papers/skip-the-compressor-coding-agents-fix-more-issues-when-they-pick-whole-files-2610-21073?lang=frChiosafr2026-10-16T11:56:00ZLes fichiers entiers aident les agents à résoudre plus de problèmeshttps://chiosa.app/papers/spark-0-scaling-spatially-aligned-tactile-pretraining-for-robot-dexterous-manipulation-2610-22342Chiosaen2026-10-16T11:56:00ZRobot touch training improves control with and without touchhttps://chiosa.app/papers/spark-0-scaling-spatially-aligned-tactile-pretraining-for-robot-dexterous-manipulation-2610-22342?lang=itChiosait2026-10-16T11:56:00ZL’addestramento tattile migliora il controllo robotico con e senza tattohttps://chiosa.app/papers/spark-0-scaling-spatially-aligned-tactile-pretraining-for-robot-dexterous-manipulation-2610-22342?lang=esChiosaes2026-10-16T11:56:00ZEl entrenamiento robótico con tacto mejora el control con y sin tactohttps://chiosa.app/papers/spark-0-scaling-spatially-aligned-tactile-pretraining-for-robot-dexterous-manipulation-2610-22342?lang=frChiosafr2026-10-16T11:56:00ZLe toucher aide les robots à manipuler avec ou sans données tactileshttps://chiosa.app/papers/speculative-forcing-for-efficient-autoregressive-video-generation-2610-22347Chiosaen2026-10-16T11:56:00ZVideo generators can stop refining tokens that have settledhttps://chiosa.app/papers/speculative-forcing-for-efficient-autoregressive-video-generation-2610-22347?lang=itChiosait2026-10-16T11:56:00ZI generatori video possono smettere di perfezionare i token stabilihttps://chiosa.app/papers/speculative-forcing-for-efficient-autoregressive-video-generation-2610-22347?lang=esChiosaes2026-10-16T11:56:00ZLos generadores de video pueden dejar de refinar tokens estabilizadoshttps://chiosa.app/papers/surgstreamer-proactive-request-driven-monitoring-of-surgical-video-streams-with-structured-state-and-a-reusab-2610-21895Chiosaen2026-10-16T11:56:00ZSurgStreamer times alerts for events in surgical videohttps://chiosa.app/papers/survicl-a-foundation-model-for-in-context-survival-analysis-2610-22242Chiosaen2026-10-16T11:56:00ZA survival model predicts event curves from censored datahttps://chiosa.app/papers/survicl-a-foundation-model-for-in-context-survival-analysis-2610-22242?lang=itChiosait2026-10-16T11:56:00ZUn modello prevede curve degli eventi da dati censuratihttps://chiosa.app/papers/survicl-a-foundation-model-for-in-context-survival-analysis-2610-22242?lang=esChiosaes2026-10-16T11:56:00ZUn modelo predice curvas de supervivencia con datos censuradoshttps://chiosa.app/papers/when-does-post-call-begin-an-availability-contract-for-earnings-call-nlp-benchmarks-2610-21659Chiosaen2026-10-16T11:56:00ZA reporting framework shows how return timing shifts financial NLP labelshttps://chiosa.app/papers/when-does-post-call-begin-an-availability-contract-for-earnings-call-nlp-benchmarks-2610-21659?lang=esChiosaes2026-10-16T11:56:00ZUn marco de presentación muestra cómo el horario del rendimiento cambia las etiquetas de lenguaje financierohttps://chiosa.app/papers/when-does-post-call-begin-an-availability-contract-for-earnings-call-nlp-benchmarks-2610-21659?lang=frChiosafr2026-10-16T11:56:00ZUn cadre de compte rendu montre comment le moment choisi modifie les étiquettes financières