imudojuiwọn ojoojumọ2303 daju itan
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Awọn itan diẹ sii
9 awọn itanAtunse
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.orgAtunse
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.orgAtunse
Ṣiṣayẹwo Iṣe Agbekọja-Lingual ni Awoṣe Ede Watermarking
Ilana igbelewọn tuntun fun awọn ero isamisi omi ni awọn awoṣe ede nla ni a dabaa, ni idojukọ lori ododo-ede-agbelebu.arxiv.orgIlana
Atọka Stanford AI rii eto imulo AI ti o pọ si bi ọba-alaṣẹ ati iyatọ idoko-owo
Stanford HAI's 2026 AI Atọka sọ pe awọn ọgbọn AI ti orilẹ-ede n tan kaakiri, lakoko ti awọn ofin agbegbe data, agbara iširo ti ipinlẹ ati idoko-owo gbogbo eniyan jẹ aiṣedeede kọja awọn agbegbe.hai.stanford.eduAtunse
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.aiAtunse
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.orgAtunse
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.orgAtunse
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.orgAtunse
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
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