Dañu koy yeesal bis bu nekk2303 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
ArXiv paper proposes milestone-based training for long-horizon LLM agents
MileGPO uses milestone discovery and local evidence to improve credit assignment when training language-model agents on long, multi-step tasks. The authors report state-of-the-art results on ALFWorld and WebShop, but the claims remain limited to the paper’s experiments.arxiv.orgYeesal
Preprint tests whether LLM agents know when to remember, verify or ask
A new benchmark evaluates whether language-model agents correctly decide when interaction-derived information should be saved, checked, used temporarily or clarified with a user.arxiv.orgYeesal
Saytu yoon ci diggante làkk yi ci misaalu làkk Watermarking
Dañu nara amal benn kaadaru jàngat bu bees ngir watermarking ci misaali làkk yu yaatu, mu lalu ci yoon ci diggante làkk yi.arxiv.orgPolitigu
Stanford AI Index finds AI policy expanding as sovereignty and investment diverge
Stanford HAI’s 2026 AI Index says national AI strategies are spreading, while data-localization rules, state-backed computing capacity and public investment remain uneven across regions.hai.stanford.eduYeesal
Together AI benchmark: GLM-5.3 trails GPT-5.6 Sol on the first try, wins on retries at half the price
A Together AI analysis of 904 DeepSWE rollouts reports that OpenAI's GPT-5.6 Sol leads on first-attempt coding accuracy while the open-weight GLM-5.3 leads once retries are allowed, at roughly half the cost per attempt.together.aiYeesal
Preprint proposes a locally tokenized AI model for robust time-series watermarking
An arXiv preprint proposes an AI generative model and watermarking method for more reliable multivariate time-series data after editing. Authors report tests across finance, energy and neuroimaging benchmarks, but the abstract gives no numerical results or evidence of deployment.arxiv.orgYeesal
Natural Language Code Retrieval for 1C:Enterprise: An Open Benchmark and Efficient Bi-Encoder
Natural language code retrieval is a rapidly evolving task in computer science. However, the 1C:Enterprise ecosystem combines Russian syntax with highly domain-specific terminology, for which open datasets and specialized models have been virtually non-existent.arxiv.orgYeesal
Time-Series Retrieval for Grounding Multimodal Language Models in Remaining Useful Life
Large language models (LLMs) and agentic AI systems are increasingly being explored for domain-specific maintenance and prognostics tasks, raising the question of whether they can effectively support prognostics and health management (PHM).arxiv.orgYeesal
Nepali-English preprint: text-only AI matched multimodal model on out-of-context misinformation benchmark
A new arXiv preprint introduces NepOOC, a 1,090-pair Nepali-English benchmark for detecting misleading captions attached to authentic images. On this dataset, a text-only mBERT model matched the best tested multimodal system, while image-only models performed near chance.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