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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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.
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Yeneen jaar-jaar
9 jaar-jaarYeesal
FinRCA-Bench gisna ni saytu IA ci wàllu xaalis mingi aju ci seet firnde
Benn benchmark bu bees bu arXiv dafa wax ni soppi anam wi ñuy jëlee rek moo yokk njubte gi gëna jub ci xeetu njubte ci wàllu xaalis, joge ci 2.05% ba 72.44%. Gëstu bi dafa wane ni etiketu root-cause bu jaar yoon mënul tekki ni sistem bi delloo na firnde yu doy ngir saytu feebar bi.arxiv.orgYeesal
Abra këyit kàrtu xayma ak done kompromis ci diffusion nataal tàggat yaram
Benn jàngat bu bees bu arXiv dafay wane jàngat ci wàllu yoon ngir xeetu diffusion bind-ci-nataal ci budget ordinatër yu tàmbalee ci 10^19 ba 10^22 FLOPs. Auteur yi dañu wax ni modelu nataal yi dañu soxla done yu bari ci parametre bu nekk moo gën modelu làkk yi ngir mëna tàggat bu baax.arxiv.orgYeesal
Delta2Gamma xamlena 92.4% njubte ci jëfandikoo EEG ngir gis feebaru Alzheimer
Benn këyit bu bees bu arXiv dafay fësal benn xeetu EEG bu ñuy saytu boppam, buy wax ni 92.4% njub di wuutale feebaru Alzheimer ak njiitu cognitif ci ADFTD cohort.arxiv.orgYeesal
Agent ESOpt dafay digle ab ajustement bu gëna ndaw ci mémoire ngir agent IA yu am horizon bu gudd
Benn këyit bu arXiv dafay wane Agent ESOpt, di benn kaadaru pexe jëm kanam buñ nara defar ngir gëna suqali ab agent model làkk bu gudd ak mémoire GPU bu tollu ci niveau inférence. Auteur yi dañu wax ni Qwen-3.5-27B amna njariñ ci WebArena-Lite ak yokkute ci 28 ci 36 jekkal yu gaaw yi.arxiv.orgYeesal
Këyit dafa digle CSE ngir jàngat jamonoy jamono yu jaarul yoon ginaaw MSE
Benn këyit bu arXiv dafa wax ni njuumte kaare moyenne mën na juum ci xayma yi ci diir bu jaarul yoon, ba noppi digle ab metric buy wéy di natt ci synthetik, semi-synthetik ak juróom ñatti done yu àdduna dëgg.arxiv.orgYeesal
Jàngat bi dafay laaj misaalu prograamu njàngale buy xam toxal ngir tàggat ci misaalu làkk
Benn téere arXiv dafay wane Toxal Relatif, di nattukaay biy wane ni misaali tàggat yi ci niveau jafe-jafe yu wuute di amee njeexital ci seen biir, ba noppi digle Toxal-xam Dynamic Curriculum Sampling ngir méngale oraaru tàggat yi ci diiru tàggat-yaram.arxiv.orgYeesal
Co-RL paper reports label-free reasoning gains from diverse model cohorts
An arXiv preprint describes Co-RL, a multi-agent reinforcement-learning framework in which separate models reward one another. The authors report gains across text-only and multimodal benchmarks without ground-truth labels, while acknowledging the risks of self-reinforcing errors and training collapse.arxiv.orgYeesal
A proposed fine-tuning method allocates training effort across related tasks
A new arXiv paper proposes Task Specialization Fine-Tuning to allocate limited fine-tuning across related task regions. It reports improved task coverage in optimization, control, and LLM experiments, but gives no numerical results or independent validation.arxiv.orgYeesal
SURE-Ridge proposes a faster way to infer causal graphs from limited data
Accepted to the 2026 Asilomar Conference, a paper proposes SURE-Ridge, a closed-form method for causal-graph estimation in equal-variance linear Gaussian models. Authors report lower small-sample structural error and fastest tested-baseline runtime; the source does not establish performance beyond these assumptions.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.
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