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A new arXiv preprint proposes giving large language models compact frequency-domain evidence, alongside indexed observations, to help detect anomalies in recurring time-series patterns.
A paper introduces G2I, a two-stage method that turns graph neural-network explanations into actionable intervention rules. Tests on synthetic graphs and suicide-risk networks suggest improved efficiency, though the source gives no numerical results or evidence that the proposed interventions improve real-world…
A new preprint examines whether multilingual language models use shared internal features when solving the same math problem in different languages. It finds that geometric similarity between features does not reliably translate into functional interchangeability.
An evaluation of 12 open-weight language models finds that they frequently mistake indirect or reversed relationships for direct causal connections, while expressing high confidence in many incorrect judgments.
A new arXiv preprint proposes Flexible Self-Consistency, a method that estimates how uncertain a language model is about a question and adjusts the number of sampled reasoning paths. The authors report token savings of up to 76% while maintaining accuracy comparable to standard self-consistency across various…
A new preprint describes FireRedAudio, a 9-billion-parameter audio language model that combines audio understanding, multilingual speech recognition, speech synthesis and speech editing through separate continuous representations.
A study of eight language models finds that conventional semantic metrics can make AI systems appear more reliable than they are in safety-critical air-traffic-control communication.
A new arXiv preprint reports that quantizing large language models can affect Bangla understanding unevenly: GPT-OSS lost up to 57.35% accuracy on reasoning-heavy tasks in one format, while Qwen and LLaMA were generally more stable.
A new arXiv preprint finds that sparse and correlated datasets make multilingual embedding-model rankings less reliable across Slavic languages, and proposes an evidence-strength measure for interpreting benchmark results.
A new arXiv preprint proposes FARCA, a reinforcement-learning method that assigns factual supervision to specific tokens and discounts evidence judged unreliable. The authors report improved factuality across multiple benchmarks while preserving general reasoning, but the source provides no numerical results or…
A new arXiv preprint proposes an AI-based method for measuring how strongly job titles and skills are associated with digital work. Applied to millions of Dutch job profiles, the method identifies uneven digitalization and pathway-dependent movement toward digital occupations.
An arXiv preprint reports that five large language models changed harmful-content classifications across Urdu scripts and English translations, revealing a measurable “Missed-in-Urdu” safety gap.
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