Dañu 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.
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Yeneen jaar-jaar
9 jaar-jaarYeesal
BanglaMamba dafay jaay yenn njub ngir gëna gaaw, gëna néew mémoire ngir gis xibaar bu baaxul bu Bangla
Benn preprint bu bees dafa wax ni BanglaMamba, benn modelu espace réew ngir gis xibaar yu baaxul yi ci Bangla, dafay jëfandikoo memory GPU bu néew ba noppi di doxal ay done yu gëna gaaw ci sistem yiñ natt ci BERT, ci noonu mu topp BanglaBERT ci njub ak performance ci biti.arxiv.orgKaaraange
Preprint gisna ni persuasion mën na tasaaroo ci reso agent LLM yi
Benn preprint bu bees dafa wax ni mesaas yu direct yi ak peer relays yi mën nañu soppi xalaatu agent model lakk yi, ba noppi wax ni jàngat yi dañu wara topp jaar-jaari exposition ak jëf, te baña topp bind kese.arxiv.orgYeesal
Preprint gisna ni ay jagleel IA yu lakk yu bari munna xajamal ay tekkale lakk yu bari
Benn preprint bu bees dafa wane ni yenn metrics yuñ jagleel ngir méngale ay model lakk ci lakk yi mën nañu indi ay jafe-jafe yu lëkkalook tokenization, encoding ak wuute ci ortograafi. Dafay wax ni log-likelihood bu baaxul ci niveau phrase ci kaw semantik yuy méngoo dafay defar ay méngale yu gëna dëppoo.arxiv.orgYeesal
MTDiag dafay saytu ndax LLMs medikaal yi mën nañu xalaat ci waxtaan yu bari ci wàllu diagnostic
Benn done bu bees dafay jàngat xeeti làkk yu yaatu ni ay agent diagnostic ci ndaje klinik yuy weccoo xalaat, du ci laaj yu wuute. MTDiag dafay boole jafe-jafe yi bawoo ci medsin urgent, done klinik yi ak rapoor yiñ siiwal ci jafe-jafe yi, ba noppi digle xam-xam bu lalu ci metrics yu weesu njubte gi ci saytu feebar bi.arxiv.orgYeesal
HealthBench-Psych dugalna benn référence bu ubbeeku ci wàllu wérgi-yaramu xel ci IA
Gëstukat yi sos nañu HealthBench-Psych, benn subset bu 610-waxtu wérgi-yaramu xel bu OpenAI's HealthBench, ba noppi génne gasoduk jàngat, tontu model, note ak kodu jàngat.arxiv.orgYeesal
Survey maps how foundation models can learn from their own outputs after training
A survey accepted to Findings of EMNLP 2026 catalogs 80 methods for adapting foundation models without human labels, preference data, stronger teachers or executable verifiers. It warns that internal learning signals can either improve a model or recursively amplify its errors.arxiv.orgYeesal
Jàngat bi dafay toppu dialect bi ci bepp etap bu modelu lakk bi di jëm kanam
Benn preprint bu bees dafa xamle ni xeetu lakk yi deñuy kode Angale dialectal ci anam wu wuute ci tokenization, pre-training, post-training ak inference, doonte ñaari mbind yi dañuy bokk luñuy tekki.arxiv.orgYeesal
Këyit dafay digle waxtu méngoo ngir saafara jafe-jafe yi ci xalaat LLM
Benn preprint bu bees dafay digle njuumte ci njuumte rek sudee firnde stereotyping yi dañuy gëna bari ci xalaatu LLM, di xamle ay intervention yu néew ci anam yu fixe-interval waaye ay tradeoffs yu am solo ci xeetu model yi.arxiv.orgYeesal
Reférence OmniPhys dafay saytu IA yu bari ci xalaat physique ak defar diagram
Benn këyit buñu nangu ci li EMNLP 2026 gis, dafa dugal OmniPhys, muy benn référence bu am 15,246 laaj ci physique ak 19,850 nataal yuñ jëlee ci corpus njàngale yu waa Chine. Dafay jàngat sistem IA yu bari ci wàllu xalaat physique ak defar ay diagram physique yuñ yamale.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.
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