Dañuy jàngat xarañteg ndawu IA ak NVIDIA
NVIDIA SkillEvaluator dafay natt njeextalu xam-xam biñ firndeel ci liggéeyu ndawu IA ci anamu jàngat bu am ñatti dayo.Liir jaar-jaar bi
Source buñ firndeel: developer.nvidia.comDañu koy yeesal bis bu nekk3929 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.
Sourcing buñ firndeel
Bépp jaar-jaar dafay lëkkale ak firnde yi gëna am doole: balluwaay yu njëkk yi suñu ko amee, luko moy rapoor yuñ joxe ci anam wu leer.
Angale bu leer
Li xewoon, lu tax mu am solo, ak li ñu wara seetaan - te amul jargon.
Amul filler
Su siñaal bi sew, dunu siiwal dara ludul padding feed bi.
Yeneen jaar-jaar
9 jaar-jaarYeesal
Benchmark neena xeetu model yu bari yi gëna am poñ yu néew 10% ci jàng kàddu yi ci sonu bik ak yëngu loxo
Benn këyit bu bees bu arXiv dugal na test boo xamni model yi dañu wara jël baat buñ bind ci audio bi ak wideo buy yëngu loxo, te kenn duko gis. Auteur yi dañu wax ni nit ñi dañu ëpp 80% ci njubteg arafu komànd yi ak xeetu njiit yi ci suufu 10% - te ni joxe xeetu ñaari anam yi dafay faral di gëna yokk njariñ yi.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
Jàngat ci Replication dafa wax ni FLOPs ba leegi ñu ngi juum ci diir bi IA di doxee, te saafara bi ñu xalaat defa lajj ci aparey yu bees yi
Benn preprint bu ñaari gëstukat def, dafay génne jàngat bu njëkk ci li waral lim FLOP bu tolloo tekkiwul diiru jëfandikoo bu tolloo. Dafay firndeel wax ji am ci suufu mbir mi waaye dafay wax ni formula jubbanti α-FLOPs dafay suufeel runtime ci aparey yu bees yi, loolu dafay wane tëb ak oscillations yi formul bi jàppul.arxiv.orgLiggéeyukaay
Benchmark Paper gisna ñeenti anam yu ñuy laajte ay done liggéeyukaay ak LLMs ñépp am poñ yu néew 26%
arXiv bu bees bi ñu njëkka imprime dafay tëral ñeenti architecture ngir laaj làkk wiñ wax ci base de done liggéeyukaay yi, seen biir ci benn benchmark bu am ñaari làkk. Kenn ci ñoom tontuwul lu ëpp ñeent ci mbir yi ci anam wu jaar yoon, te jëmmal bi gëna am poñ nekkul bi gëna wóor wala bi gëna xéewale.arxiv.orgYeesal
Këyit dafay digle grade IA Security Agents te amul etiket ci natt convergence ci model bu gëna am doole
Benn preprint bu bees bu arXiv neena ekipu kaaraange yi munnañu atte ndax ap ndawu juntuwaay yu bees yu am memory mbaa retrieval ñungi jang ci natt ba fu mu tëj bërëb bi ci xeetu "jàngalekat" bu gina am doole, du ci ay jeego yu ñu joxoñ mbaa yu yagg. Buñu sukkandikoo ci model bu am doole bu noonu mel, joxewul siñaal buñ mëna jëfandikoo.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
Benn nettali bu am njariñ ayu-bis bu nekk
Weyal IA te doo dundu ci feed bi.
Wutal xibaar IA buñ firndeel ci ayu-bis bi, done yu baax, jumtukaay yu am njariñ, tànneefi jàng, ak liggéey IA yu bees.
Yegg ci nit ñiy jàng IA
Nga jël ab liggéeykat IA wala nga genne ab produit IA bu am njariñ? Tegal ko ci kanamu nit ñi ñëw fi ngir jàng ak jëf.
Publie ab liggey IAYonnee ab jumtukaayu IA