Evaluating AI Agent Skill Performance with NVIDIA SkillEvaluator
NVIDIA SkillEvaluator measures the impact of verified skills on AI agent performance through a three-tier evaluation process.Liir jaar-jaar bi
Source buñ firndeel: developer.nvidia.comDañu koy yeesal bis bu nekk2273 jaar-jaar yuñ firnde
Xibaar AI. Bu amul xumbaay.
IA buñu saytu bu baax ci lu jëm ci genne ay fasoŋu porodiwi, coppite ci politik, gestu ci kaaraange, ak toxu usine yi, ap ekipu njang buy def te Yàlla tax moo ko leeral ci Àngle bu leer.
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Angale bu leer
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Amul filler
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Yeneen jaar-jaar
9 jaar-jaarYeesal
Benchmark Says Top Multimodal Models Score Under 10% at Reading Words From Pen Sounds and Hand Motion
A new arXiv paper introduces a test in which models must infer a written word from pen-scratch audio and hand-movement video, with no ink visible. The authors report humans above 80% ordered letter accuracy and leading models below 10% — and that giving models both modalities often made results worse.arxiv.orgYeesal
Këyit neena Agent-Aware Cache Management dafa dagg Token bu njëkk bi yeexal ba 45% ci sarwiisu Agent yu bari
Benn preprint bu bees bu arXiv dafay leeral CacheScout, di layer buñ tabax ci kaw serwëru vLLM bu ubbeeku biy tànn li ñuy denc ci cache bu model bi, lépp di aju ci ban agent moo wara daw ci kanam. Auteur yi dañu wax ni latency ak produit yi dañu am ñaari araf; Liggéeyukaay yi, model yi, ak aparey yi waxu ñu ci abstract bi.arxiv.orgYeesal
Audit bu benn OpenAI AI-Generated Proof gisna anam wu jaarul yoon, ba noppi siiwal ab defar
Ñaari gëstukat neena ñu firnde lemma bu nekk ci Chapitre 6 ci këyitu math bu OpenAI amna njuumte ci polarite: test ci wàllu réussite moyenne fu jéego bi ci topp soxla njuumte bu mag. Dañuy joxe misaal bu wuute ak firnde buñ jubbanti, ba noppi ñu artu ni loolu du firnde theorem bu mag bi ci chapitre bi.arxiv.orgYeesal
Replication Study Says FLOPs Still Mispredict AI Runtime, and the Proposed Fix Fails on Newer Hardware
A preprint by two researchers reproduces an earlier study on why equal FLOP counts do not mean equal execution time. It confirms the underlying claim but reports that the α-FLOPs correction formula generally underestimates runtime on newer hardware, which shows jumps and oscillations the formula does not capture.arxiv.orgLiggéeyukaay
Benchmark Paper Finds Four Ways to Query Enterprise Data With LLMs All Score Under 26%
A new arXiv preprint pits four architectures for natural-language querying of enterprise databases against each other on a synthetic bilingual benchmark. None answered more than about a quarter of cases correctly, and the design that scored highest was not the safest or the cheapest.arxiv.orgYeesal
Paper Proposes Grading AI Security Agents Without Labels by Measuring Convergence to a Stronger Model
A new arXiv preprint argues security teams can judge whether a memory- or retrieval-equipped AI agent is learning by measuring how far it closes the gap to a stronger "teacher" model, rather than on labeled benchmarks that are often scarce or stale. Judging by a similarly powered model gave no usable signal.arxiv.orgYeesal
New Benchmark Tests Whether AI Assistants Can Remember a Year of Phone Use
A 17-author technical report posted to arXiv introduces MobileMem, a benchmark and framework for on-device long-term memory built from a year-scale collection of mobile experiences. The abstract describes the design but reports no scores, and key details about the underlying data remain undisclosed.arxiv.orgYeesal
Paper Reports Brain-Like Modular Organization Emerging Inside Large Language Models
A new arXiv preprint says large language models develop functionally specialized internal structure that lines up with distinct human brain networks, based on circuit analyses across 46 tasks in four cognitive domains. The abstract page leaves key methodological details unstated.arxiv.orgYeesal
Këyit dafa gis ay layers yu mujj ci benn modelu njaxasu-ekspert muñ maskewu eksper bu diis
Benn preprint dafa wax ni dindi eksper yu am magnitude bu woyof ci juróomi couche yu mujj yi ci benn model Mixture-of-Expert bu am 35 milyaar ci parametre, moo gëna am njariñ ci tekki kode, moo gën ñu tasaare benn dagg ci couche yépp. Dafay wax ci benn model ak benn référence, te abstract bi amul benn baseline buñu maskewul.arxiv.org
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Wutal xibaar IA buñ firndeel ci ayu-bis bi, done yu baax, jumtukaay yu am njariñ, tànneefi jàng, ak liggéey IA yu bees.
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