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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.
A new evaluation framework for watermarking schemes in large language models is proposed, focusing on cross-lingual fairness.
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.
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.
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.
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).
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.
A new arXiv preprint tests financial named-entity recognition across filings, news and social media, finding that some confidence measures remain useful under domain shift while others deteriorate sharply.
A study explores whether conversational AI can shift how majority-group members categorize and relate to Latine immigrants.
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.
A preliminary arXiv study finds that responses from language models have become less diverse across open-ended creativity tasks, raising questions about homogenization in human-AI creative work.
An arXiv preprint describes a method that trains transformer language models to work with sparse attention and reports frequent gains over models trained with exact attention.
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