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Source-checked AI coverage of product launches, policy shifts, safety research, and industry moves, explained in plain English by a nonprofit education team.
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Modal auxiliaries such as must, should, and have to mark necessity and obligation within the contexts of speaker authority and interpersonal stance.
An arXiv preprint introduces a weight-editing method intended to make safety refusals harder to extract and remove. The paper reports stronger post-abliteration refusal scores on two open models, with different tradeoffs in general-purpose performance.
An arXiv preprint describes a hallucination detector that combines text statistics, entailment signals and language-model surprisal across sequences instead of judging tokens independently. Its BiGRU model reached an AUC of 0.840 on RAGTruth, according to the paper.
Understanding language requires tracking entities across discourse - i.e., knowing where things are and how they change, even when not explicitly stated.
An arXiv preprint proposes an adaptive proof-search method for context-dependent Lean 4 projects. Its authors report a 12.8-percentage-point average pass-rate improvement within a pass@32 budget while using 21.9% fewer LLM calls than pass@k baselines.
A new open-source benchmark tests whether foundation models can construct faithful geometry diagrams, not merely solve the underlying problems. Its authors report that evaluated models achieved an average compile success rate of 36.14%, exposing a gap between mathematical answers and usable visual constructions.
A new approach to training multimodal large language models (MLLMs) eliminates the need for extensive task-specific supervision.
This work examines emoji-augmented prompts as a test case for gaps in safety evaluation of large language models (LLMs).
Modern e-commerce platforms often operate search, recommendation, personalization, and CRM systems independently, limiting opportunities for proactive customer re-engagement.
A paper introduces THPT-Ladder, a 632-item benchmark that applies Vietnam’s 2025 national exam grading scheme to language models and reports materially different scores from standard proportional-accuracy measures.
An arXiv paper describes how Netflix built, deployed and continuously monitored an LLM judge for recommendation explanations, reporting viewing and engagement gains in a five-week A/B test involving tens of millions of members.
A new arXiv survey proposes viewing an AI agent’s memories, tools, skills, workflows and relationships as a graph that changes over time, and calls for graph-aware evaluation and governance.
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