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
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.orgYeesal
Preprint bi feeñ na ne yaakaaru AI ci wàllu xaalis mën na wàññiiku bu tékt bi soppeekoo
Benn preprint bu bees bu arXiv dafay seetlu naka lañuy nangu turu nit walla mbir ci wàllu xaalis ci biir tékt, xibaar ak ci biir social media, te feeñ na ne yenn yaakaar yi mën nañu jariñ ci biir soppeeku gu bari, te yeneen yi di wàññiiku lool.arxiv.orgYeesal
Ndax IA biy waxtaan mënna dindi sunu diggante ak ñoom?
Ap gestu mungi doon saytu ndax juntuwaay yu bees yi munna soppi ni ñi ëppu ci mboolo mi di xaajalee ak di jaxasoo ak ay toxu Latin.arxiv.orgYeesal
Preprint bi feeñ na ne soppeeku gu yàgg-yàgg ci ay mbir dafay yokk jaaxle ci base language models
A new arXiv preprint reports that injecting a new subject every few hundred tokens can increase language-model outputs’ judged surprise and connection. The effect did not produce integrated documents or improve the best result in an online bin-packing test, exposing limits in how long generations are evaluated.arxiv.orgYeesal
Preprint bi dafay yëgle ne wàññiiku gu bariwul ci li LLM di génne ci wàllu mbind yu bees ci biir ñetti at
Benn njàngum arXiv bu jëkk feeñ na ne tontu yi jóge ci language models wàññiiku nañu ci wàllu li ñuy def ci wàllu mbind yu bees, loolu di jur laaj ci naka lañuy def ba yépp mel ni yeneen yi ci liggéeyu nit ak AI.arxiv.orgYeesal
Mbind mi dafay yëgle benn yoonu fine-tuning ngir long-context AI ak sparse attention
Benn preprint bu arXiv dafay nettali benn yoon bu di jàngal transformer language models ngir liggéey ak sparse attention te dafay yëgle ne dafay gën ci models yi ñu jàngal ak exact attention.arxiv.orgKaaraange
Google Cloud dafay nettali CodeMender AI Agent ngir Code Vulnerability Remediation
Google Cloud dafay indil CodeMender ni benn AI security agent bu di seet codebases, di seet bu fekke ne vulnerabilities mën nañu leen exploiter, te di def ay patch ngir developer yi seet ko. Xibaar bi nettaliwul ne dafa mel ni formal-verification framework walla mu joxe ay kàddu yu wér ci naka la liggéey bi di deme.
cloud.google.com
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
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