https://chiosa.app/papers/agent-world-model-in-the-wild-computer-use-agent-environment-synthesis-2610-26828Chiosaen2026-10-20T18:07:34ZWildAWM creates computer worlds for cross-interface agent traininghttps://chiosa.app/papers/agr-agentic-generative-retrieval-with-tool-augmented-search-2610-26831Chiosaen2026-10-20T18:07:34ZSearch retrieval can request useful features as it generates resultshttps://chiosa.app/papers/agr-agentic-generative-retrieval-with-tool-augmented-search-2610-26831?lang=itChiosait2026-10-20T18:07:34ZLa ricerca può richiedere caratteristiche utili mentre genera risultatihttps://chiosa.app/papers/brushoff-stroke-based-re-rendering-as-a-watermark-removal-attack-on-painting-2610-26682Chiosaen2026-10-20T18:07:34ZBrush strokes remove marks that common edits leave behindhttps://chiosa.app/papers/cartender-verifiable-language-conditioned-driving-by-learning-the-drives-not-taken-2610-26390Chiosaen2026-10-20T18:07:34ZA driving model responds better when it learns alternate pathshttps://chiosa.app/papers/harnesspro-recursive-harness-self-improvement-with-pareto-optimization-for-performance-cost-trade-offs-2610-26770Chiosaen2026-10-20T18:07:34ZHarnessPro improves agent success at lower costhttps://chiosa.app/papers/harnesspro-recursive-harness-self-improvement-with-pareto-optimization-for-performance-cost-trade-offs-2610-26770?lang=itChiosait2026-10-20T18:07:34ZHarnessPro aumenta il successo degli agenti a costo inferiorehttps://chiosa.app/papers/monsoon-scalable-spontaneous-speech-collection-for-low-resource-asr-in-50-languages-2610-26776Chiosaen2026-10-20T18:07:34ZMonsoon builds speech data that improves conversational recognitionhttps://chiosa.app/papers/monsoon-scalable-spontaneous-speech-collection-for-low-resource-asr-in-50-languages-2610-26776?lang=esChiosaes2026-10-20T18:07:34ZMonsoon crea datos de habla que mejoran el reconocimiento conversacionalhttps://chiosa.app/papers/scaling-parameter-efficient-fine-tuning-for-brain-encoding-2610-26802Chiosaen2026-10-20T18:07:34ZSmall adapters improve brain-response predictions across tested modelshttps://chiosa.app/papers/scaling-parameter-efficient-fine-tuning-for-brain-encoding-2610-26802?lang=frChiosafr2026-10-20T18:07:34ZDe petits adaptateurs améliorent les prédictions cérébrales des modèles testéshttps://chiosa.app/papers/state-world-models-execute-the-rules-generate-the-pixels-2610-26811Chiosaen2026-10-20T18:07:34ZA video framework applies world rules before rendering actionshttps://chiosa.app/papers/state-world-models-execute-the-rules-generate-the-pixels-2610-26811?lang=itChiosait2026-10-20T18:07:34ZUn framework video applica le regole del mondo prima di renderizzare le azionihttps://chiosa.app/papers/state-world-models-execute-the-rules-generate-the-pixels-2610-26811?lang=frChiosafr2026-10-20T18:07:34ZUn cadre applique les règles du monde avant de générer les actionshttps://chiosa.app/papers/tandem-serving-hybrid-linear-attention-gui-agents-on-mobile-npus-2610-26660Chiosaen2026-10-20T18:07:34ZTandem speeds hybrid phone-agent model servinghttps://chiosa.app/papers/text-to-image-generative-modeling-without-language-pretraining-2610-26863Chiosaen2026-10-20T18:07:34ZText-to-image models can learn without text-only pretraininghttps://chiosa.app/papers/tokengate-adapting-llms-for-fast-and-memory-efficient-prefill-with-learned-token-pruning-2610-26754Chiosaen2026-10-20T18:07:34ZTokenGate cuts long-prompt processing while preserving accuracyhttps://chiosa.app/papers/a-cue-s-hidden-price-auditing-seller-influence-on-ai-shoppers-2610-25843Chiosaen2026-10-20T08:16:02ZA price sweep reveals how seller cues sway AI shoppinghttps://chiosa.app/papers/beyond-the-record-evaluating-probabilistic-ai-weather-forecasts-deep-in-the-tail-2610-25545Chiosaen2026-10-20T08:16:02ZAI weather forecasts retain skill beyond local heat recordshttps://chiosa.app/papers/claim-laundering-when-agent-decision-models-trust-records-over-reality-2610-25947Chiosaen2026-10-20T08:16:02ZA model can see a failed step yet trust the agent’s recordhttps://chiosa.app/papers/claim-laundering-when-agent-decision-models-trust-records-over-reality-2610-25947?lang=itChiosait2026-10-20T08:16:02ZUn modello può rilevare un passaggio fallito e fidarsi comunque del registro dell’agentehttps://chiosa.app/papers/clean-forcing-drift-resistant-autoregressive-video-diffusion-with-a-frozen-base-2610-25634Chiosaen2026-10-20T08:16:02ZA small corrector reduces drift in long generated videoshttps://chiosa.app/papers/concerns-in-benchmarking-jailbreaks-an-empirical-study-of-attack-success-rates-2610-26595Chiosaen2026-10-20T08:16:02ZJailbreak rankings shift with evaluators and attempt settingshttps://chiosa.app/papers/concerns-in-benchmarking-jailbreaks-an-empirical-study-of-attack-success-rates-2610-26595?lang=itChiosait2026-10-20T08:16:02ZLe classifiche dei jailbreak cambiano con valutatori e tentativihttps://chiosa.app/papers/concerns-in-benchmarking-jailbreaks-an-empirical-study-of-attack-success-rates-2610-26595?lang=esChiosaes2026-10-20T08:16:02ZLas clasificaciones de jailbreak cambian según el evaluador y los intentoshttps://chiosa.app/papers/concerns-in-benchmarking-jailbreaks-an-empirical-study-of-attack-success-rates-2610-26595?lang=frChiosafr2026-10-20T08:16:02ZLe classement des jailbreaks varie selon l’évaluateur et les tentativeshttps://chiosa.app/papers/connection-escrow-holding-ai-agent-pod-egress-in-the-kernel-until-an-isolated-verifier-rules-2610-26501Chiosaen2026-10-20T08:16:02ZA kernel gate ties agent approvals to each connectionhttps://chiosa.app/papers/consistent-llm-call-handling-across-java-frameworks-a-reference-implementation-and-benchmark-2610-25480Chiosaen2026-10-20T08:16:02ZA shared HTTP guard makes Java model calls more consistenthttps://chiosa.app/papers/learn-the-interface-not-the-implementation-output-representation-dominates-training-regime-in-llm-based-rtl-g-2610-25881Chiosaen2026-10-20T08:16:02ZCompact configurations improve hardware generation in one SECDED taskhttps://chiosa.app/papers/learning-from-fleet-experience-a-collective-learning-paradigm-for-robots-2610-25736Chiosaen2026-10-20T08:16:02ZRobot fleets improve a shared policy from task experiencehttps://chiosa.app/papers/logit-averaging-during-training-sets-a-new-state-of-the-art-for-non-invasive-speech-bci-2610-26347Chiosaen2026-10-20T08:16:02ZTraining-time averaging leads across most MEG taskshttps://chiosa.app/papers/neural-video-coding-under-limited-delay-theoretical-optimality-and-system-design-2610-25729Chiosaen2026-10-20T08:16:02ZFuture-frame clues help neural codecs compress videohttps://chiosa.app/papers/one-fact-many-places-redundant-evidence-in-long-visual-documents-breaks-single-gold-evaluation-of-retrieval-c-2610-26401Chiosaen2026-10-20T08:16:02ZRepeated facts can distort document evidence testshttps://chiosa.app/papers/one-fact-many-places-redundant-evidence-in-long-visual-documents-breaks-single-gold-evaluation-of-retrieval-c-2610-26401?lang=frChiosafr2026-10-20T08:16:02ZLes faits répétés peuvent fausser les tests de preuve documentairehttps://chiosa.app/papers/openvlharness-real-world-visual-agentic-tool-use-with-multimodal-memory-2610-26860Chiosaen2026-10-20T08:16:02ZA system helps visual agents gather and reuse evidencehttps://chiosa.app/papers/openvlharness-real-world-visual-agentic-tool-use-with-multimodal-memory-2610-26860?lang=itChiosait2026-10-20T08:16:02ZUn sistema aiuta gli agenti visivi a raccogliere e riutilizzare evidenzehttps://chiosa.app/papers/parallel-language-models-via-variational-decoding-2610-26693Chiosaen2026-10-20T08:16:02ZParallel language models coordinate several token predictions at oncehttps://chiosa.app/papers/parallel-language-models-via-variational-decoding-2610-26693?lang=itChiosait2026-10-20T08:16:02ZI modelli linguistici paralleli coordinano più previsioni di token insiemehttps://chiosa.app/papers/reasoning-aware-multicalibrated-reliability-predictors-for-language-models-2610-26645Chiosaen2026-10-20T08:16:02ZReasoning traces help reveal hidden calibration errorshttps://chiosa.app/papers/reasoning-aware-multicalibrated-reliability-predictors-for-language-models-2610-26645?lang=esChiosaes2026-10-20T08:16:02ZLas trazas de razonamiento ayudan a revelar errores de calibración ocultoshttps://chiosa.app/papers/same-simulation-different-evidence-where-retrieved-tool-output-costs-llm-agents-accuracy-2610-26836Chiosaen2026-10-20T08:16:02ZHow simulator report delivery changes agents’ answershttps://chiosa.app/papers/same-simulation-different-evidence-where-retrieved-tool-output-costs-llm-agents-accuracy-2610-26836?lang=itChiosait2026-10-20T08:16:02ZCome la consegna dei report del simulatore cambia le risposte degli agentihttps://chiosa.app/papers/same-simulation-different-evidence-where-retrieved-tool-output-costs-llm-agents-accuracy-2610-26836?lang=esChiosaes2026-10-20T08:16:02ZCómo cambia las respuestas de los agentes la entrega del informe del simuladorhttps://chiosa.app/papers/same-simulation-different-evidence-where-retrieved-tool-output-costs-llm-agents-accuracy-2610-26836?lang=frChiosafr2026-10-20T08:16:02ZComment le mode de transmission du rapport change les réponses des agentshttps://chiosa.app/papers/selling-in-the-wild-benchmark-and-randomized-field-evidence-for-a-sales-agent-trained-on-commercial-conversat-2610-25461Chiosaen2026-10-20T08:16:02ZJavo raises sales outcomes in live website testshttps://chiosa.app/papers/streamahead-real-time-streaming-video-question-answering-via-adaptive-precomputation-2610-25551Chiosaen2026-10-20T08:16:02ZStreamAhead adjusts video processing before questions arrivehttps://chiosa.app/papers/strucchunk-structure-aware-retrieval-for-legal-document-rag-2610-26313Chiosaen2026-10-20T08:16:02ZLegal search improves when chunks preserve section structurehttps://chiosa.app/papers/swe-residual-evaluating-starting-state-robustness-of-coding-agents-2610-25899Chiosaen2026-10-20T08:16:02ZInherited software work exposes a gap in coding agentshttps://chiosa.app/papers/the-scheduler-is-a-hidden-retriever-how-asynchrony-shapes-evidence-acquisition-in-search-agents-2610-26310Chiosaen2026-10-20T08:16:02ZWhen search results arrive in a different order, agents seek different evidencehttps://chiosa.app/papers/token-set-choice-confounds-pope-a-systematic-audit-of-yes-no-extraction-in-vlm-hallucination-evaluation-2610-25964Chiosaen2026-10-20T08:16:02ZToken choice can change measured hallucination scoreshttps://chiosa.app/papers/token-set-choice-confounds-pope-a-systematic-audit-of-yes-no-extraction-in-vlm-hallucination-evaluation-2610-25964?lang=itChiosait2026-10-20T08:16:02ZLa scelta dei token può cambiare i punteggi di allucinazione misuratihttps://chiosa.app/papers/towards-architectural-level-safety-control-relocating-hazardous-knowledge-into-removable-experts-in-mixture-o-2610-25791Chiosaen2026-10-20T08:16:02ZREX moves hazardous knowledge into removable expertshttps://chiosa.app/papers/truncate-or-not-a-scaling-study-of-looped-transformers-with-truncated-backpropagation-2610-26030Chiosaen2026-10-20T08:16:02ZTwo fully trained loops can match large models with fewer parametershttps://chiosa.app/papers/veruseval-evaluating-llm-generated-verus-specifications-beyond-verifier-acceptance-2610-26464Chiosaen2026-10-20T08:16:02ZVerifier acceptance can miss weak generated contractshttps://chiosa.app/papers/when-coding-agents-outpace-review-evaluating-prd-driven-development-in-mature-enterprise-repositories-2610-26394Chiosaen2026-10-20T08:16:02ZPACE-Bench checks whether coding agents deliver full requestshttps://chiosa.app/papers/when-coding-agents-outpace-review-evaluating-prd-driven-development-in-mature-enterprise-repositories-2610-26394?lang=itChiosait2026-10-20T08:16:02ZPACE-Bench verifica se gli agenti di coding soddisfano le richieste completehttps://chiosa.app/papers/when-coding-agents-outpace-review-evaluating-prd-driven-development-in-mature-enterprise-repositories-2610-26394?lang=esChiosaes2026-10-20T08:16:02ZPACE-Bench comprueba si los agentes de programación entregan solicitudes completashttps://chiosa.app/papers/when-guidance-becomes-a-guard-early-interception-of-unsafe-inputs-in-text-to-image-generation-2610-26452Chiosaen2026-10-20T08:16:02ZEarly signals can flag risky image promptshttps://chiosa.app/papers/whose-credential-is-it-how-owner-attribution-in-agent-memoryshapes-cross-user-account-misuse-2610-25707Chiosaen2026-10-20T08:16:02ZHow shared memory can lead agents into the wrong accounthttps://chiosa.app/papers/yutela-a-scalable-edge-cloud-aiot-system-for-multi-site-video-anomaly-detection-with-scene-specific-adaptatio-2610-26468Chiosaen2026-10-20T08:16:02ZA camera system shares edge work and adapts models centrallyhttps://chiosa.app/papers/yutela-a-scalable-edge-cloud-aiot-system-for-multi-site-video-anomaly-detection-with-scene-specific-adaptatio-2610-26468?lang=frChiosafr2026-10-20T08:16:02ZUn système de caméras partage le calcul local et adapte ses modèles à distancehttps://chiosa.app/papers/bagualu-2-taming-dynamic-memory-fluctuation-for-efficient-moe-model-training-over-10-000-gpus-2610-23064Chiosaen2026-10-19T10:29:55ZA training system adapts memory to rare expert-load spikeshttps://chiosa.app/papers/blitz-accelerating-the-diffusion-modules-of-alphafold3-and-boltz-2-2610-23703Chiosaen2026-10-19T10:29:55ZBlitz cuts protein structure sampling to eight stepshttps://chiosa.app/papers/blitz-accelerating-the-diffusion-modules-of-alphafold3-and-boltz-2-2610-23703?lang=itChiosait2026-10-19T10:29:55ZBlitz riduce a otto i passi di campionamento delle proteinehttps://chiosa.app/papers/blitz-accelerating-the-diffusion-modules-of-alphafold3-and-boltz-2-2610-23703?lang=esChiosaes2026-10-19T10:29:55ZBlitz reduce a ocho pasos el muestreo de estructurashttps://chiosa.app/papers/causal-value-attribution-of-marketing-labels-in-llm-generated-ads-a-deconfounder-approach-2610-23025Chiosaen2026-10-19T10:29:55ZA causal method traces ad engagement to chosen labelshttps://chiosa.app/papers/disentangling-language-effects-on-tool-admission-and-post-retrieval-safety-2610-23093Chiosaen2026-10-19T10:29:55ZNon-English requests more often enter retrievalhttps://chiosa.app/papers/disentangling-language-effects-on-tool-admission-and-post-retrieval-safety-2610-23093?lang=itChiosait2026-10-19T10:29:55ZLe richieste non inglesi accedono più spesso al retrievalhttps://chiosa.app/papers/equivalence-testing-and-run-count-design-for-stochastic-machine-learning-experiments-2610-23700Chiosaen2026-10-19T10:29:55ZA run-count method for testing training equivalencehttps://chiosa.app/papers/equivalence-testing-and-run-count-design-for-stochastic-machine-learning-experiments-2610-23700?lang=esChiosaes2026-10-19T10:29:55ZUn método para calcular ejecuciones y probar equivalenciahttps://chiosa.app/papers/equivalence-testing-and-run-count-design-for-stochastic-machine-learning-experiments-2610-23700?lang=frChiosafr2026-10-19T10:29:55ZUne méthode pour planifier les tests d’équivalence entre entraînementshttps://chiosa.app/papers/futransformer-fp8-prefill-and-int4-decode-for-llama-style-inference-on-hopper-2610-23796Chiosaen2026-10-19T10:29:55ZSeparate GPU paths speed prompt and token processinghttps://chiosa.app/papers/futransformer-fp8-prefill-and-int4-decode-for-llama-style-inference-on-hopper-2610-23796?lang=esChiosaes2026-10-19T10:29:55ZRutas separadas en GPU aceleran prompts y tokenshttps://chiosa.app/papers/futransformer-fp8-prefill-and-int4-decode-for-llama-style-inference-on-hopper-2610-23796?lang=frChiosafr2026-10-19T10:29:55ZDes chemins GPU distincts accélèrent invites et jetonshttps://chiosa.app/papers/jarvis-coordination-deserves-a-brain-of-its-own-in-agentic-interactive-models-2610-23804Chiosaen2026-10-19T10:29:55ZJarvis keeps background tasks aligned with live conversationshttps://chiosa.app/papers/jarvis-coordination-deserves-a-brain-of-its-own-in-agentic-interactive-models-2610-23804?lang=itChiosait2026-10-19T10:29:55ZJarvis allinea il lavoro in background alle conversazioni attivehttps://chiosa.app/papers/jarvis-coordination-deserves-a-brain-of-its-own-in-agentic-interactive-models-2610-23804?lang=esChiosaes2026-10-19T10:29:55ZJarvis mantiene las tareas en segundo plano alineadas con la conversaciónhttps://chiosa.app/papers/jarvis-coordination-deserves-a-brain-of-its-own-in-agentic-interactive-models-2610-23804?lang=frChiosafr2026-10-19T10:29:55ZJarvis maintient les tâches en arrière-plan en phase avec les conversationshttps://chiosa.app/papers/lemon-equivariant-learning-over-attention-maps-for-zero-shot-misbehavior-monitoring-on-new-llms-2610-22723Chiosaen2026-10-19T10:29:55ZA graph monitor transfers hallucination detection across language modelshttps://chiosa.app/papers/look-before-you-act-bridging-active-perception-and-vision-language-action-models-via-observation-aligned-data-2610-23378Chiosaen2026-10-19T10:29:55ZTraining robot policies to act after they move the camerahttps://chiosa.app/papers/look-before-you-act-bridging-active-perception-and-vision-language-action-models-via-observation-aligned-data-2610-23378?lang=itChiosait2026-10-19T10:29:55ZAddestrare le policy robotiche a proseguire dopo aver mosso la telecamerahttps://chiosa.app/papers/look-before-you-act-bridging-active-perception-and-vision-language-action-models-via-observation-aligned-data-2610-23378?lang=frChiosafr2026-10-19T10:29:55ZEntraîner les politiques robotiques à agir après un mouvement de camérahttps://chiosa.app/papers/many-biases-one-circuit-post-training-tunes-a-pretrained-cue-routing-mechanism-2610-22937Chiosaen2026-10-19T10:29:55ZPost-training strengthens an existing circuit for following cueshttps://chiosa.app/papers/monitor-validity-failures-llm-safety-evidence-paths-at-evolving-framework-boundaries-2610-23362Chiosaen2026-10-19T10:29:55ZCanary contracts reveal where safety evidence gets losthttps://chiosa.app/papers/not-all-test-time-compute-is-evidence-model-specific-bias-in-llm-reasoning-2610-23213Chiosaen2026-10-19T10:29:55ZDia calls other models only when reasoning answers disagreehttps://chiosa.app/papers/not-all-test-time-compute-is-evidence-model-specific-bias-in-llm-reasoning-2610-23213?lang=frChiosafr2026-10-19T10:29:55ZDia ne consulte d’autres modèles qu’en cas de désaccord entre réponseshttps://chiosa.app/papers/on-the-spot-enhancing-speculative-decoding-for-diffusion-language-models-via-commit-order-prediction-2610-22543Chiosaen2026-10-19T10:29:55ZA learned drafter predicts which masked position comes nexthttps://chiosa.app/papers/peak-memory-schedules-you-can-trust-exact-realization-certified-optimality-and-the-limits-of-recomputation-fo-2610-23328Chiosaen2026-10-19T10:29:55ZReordering generated programs makes memory savings verifiablehttps://chiosa.app/papers/peak-memory-schedules-you-can-trust-exact-realization-certified-optimality-and-the-limits-of-recomputation-fo-2610-23328?lang=frChiosafr2026-10-19T10:29:55ZRéordonner les programmes générés pour vérifier les économies mémoirehttps://chiosa.app/papers/prairiefire-igniting-high-throughput-updates-in-large-scale-approximate-nearest-neighbor-search-2610-23232Chiosaen2026-10-19T10:29:55ZPrairieFire handles frequent changes in large vector indexeshttps://chiosa.app/papers/sample-mix-or-verify-allocating-test-time-compute-under-imperfect-verifiers-2610-23255Chiosaen2026-10-19T10:29:55ZA pilot helps allocate compute when verifiers errhttps://chiosa.app/papers/scalable-long-range-machine-learning-interatomic-potentials-with-electrical-response-for-atomistic-material-m-2610-23643Chiosaen2026-10-19T10:29:55ZA local atom model adds long-range forces and electrical responsehttps://chiosa.app/papers/scalable-long-range-machine-learning-interatomic-potentials-with-electrical-response-for-atomistic-material-m-2610-23643?lang=esChiosaes2026-10-19T10:29:55ZUn modelo local de átomos añade fuerzas de largo alcance y respuesta eléctricahttps://chiosa.app/papers/scalable-long-range-machine-learning-interatomic-potentials-with-electrical-response-for-atomistic-material-m-2610-23643?lang=frChiosafr2026-10-19T10:29:55ZUn modèle atomique local ajoute les forces à longue portée et la réponse électriquehttps://chiosa.app/papers/shift-reliance-alignment-ranking-vision-language-models-by-the-cues-they-trust-2610-22610Chiosaen2026-10-19T10:29:55ZA model’s cue sensitivity can change which model is besthttps://chiosa.app/papers/sparsemax-dstl-sound-robustness-with-horizon-independent-gradient-allocation-for-temporal-logic-trajectory-op-2610-23837Chiosaen2026-10-19T10:29:55ZA smoother keeps violating samples in the trajectory updatehttps://chiosa.app/papers/the-ephemeral-cost-of-switching-optimizers-across-llm-training-stages-2610-22650Chiosaen2026-10-19T10:29:55ZMatching training optimizers adds little consistent benefithttps://chiosa.app/papers/think-more-or-read-more-a-token-budget-theory-of-reasoning-with-shared-context-2610-23246Chiosaen2026-10-19T10:29:55ZA token-budget method predicts when debate groups helphttps://chiosa.app/papers/tool-recommendation-for-multi-step-agents-finding-every-tool-a-task-needs-2610-23707Chiosaen2026-10-19T10:29:55ZArcade lets agents request tools from a changing cataloghttps://chiosa.app/papers/tool-recommendation-for-multi-step-agents-finding-every-tool-a-task-needs-2610-23707?lang=itChiosait2026-10-19T10:29:55ZArcade consente agli agenti di richiedere strumenti da un catalogo in evoluzionehttps://chiosa.app/papers/tool-recommendation-for-multi-step-agents-finding-every-tool-a-task-needs-2610-23707?lang=esChiosaes2026-10-19T10:29:55ZArcade permite que los agentes soliciten herramientas de un catálogo cambiantehttps://chiosa.app/papers/tool-recommendation-for-multi-step-agents-finding-every-tool-a-task-needs-2610-23707?lang=frChiosafr2026-10-19T10:29:55ZArcade permet aux agents de demander des outils dans un catalogue évolutifhttps://chiosa.app/papers/uncertainty-aware-budget-allocation-for-efficient-llm-tree-search-2610-22858Chiosaen2026-10-19T10:29:55ZLanguage model search can spend less on predictable stepshttps://chiosa.app/papers/understanding-kv-residency-in-production-scale-agentic-llm-workloads-2610-22863Chiosaen2026-10-19T10:29:55ZReusable AI session context can strain GPU memoryhttps://chiosa.app/papers/vulnsage-adjudicating-sast-reports-by-connecting-llm-reasoning-to-program-structure-2610-23035Chiosaen2026-10-19T10:29:55ZVulnSAGE checks the code evidence behind security warningshttps://chiosa.app/papers/webcoder-scaling-general-frontend-development-with-artifact-grounded-agentic-reinforcement-learning-2610-23104Chiosaen2026-10-19T10:29:55ZWebCoder trains one agent to handle changing frontend projectshttps://chiosa.app/papers/webcoder-scaling-general-frontend-development-with-artifact-grounded-agentic-reinforcement-learning-2610-23104?lang=itChiosait2026-10-19T10:29:55ZWebCoder addestra un agente a gestire progetti frontend che cambianohttps://chiosa.app/papers/webcoder-scaling-general-frontend-development-with-artifact-grounded-agentic-reinforcement-learning-2610-23104?lang=frChiosafr2026-10-19T10:29:55ZWebCoder entraîne un agent à gérer des projets frontend évolutifshttps://chiosa.app/papers/what-it-is-not-what-it-says-securing-agentic-browsing-interactions-with-web-semantics-2610-22697Chiosaen2026-10-19T10:29:55ZIntact ties browser actions to the webpage elements behind themhttps://chiosa.app/papers/who-pays-for-efficient-inference-confidence-based-small-to-large-llm-routing-across-tasks-and-languages-2610-23249Chiosaen2026-10-19T10:29:55ZSmall-model confidence helps routing on some multilingual tasks