The latest AI news, launches, models, companies and research — collected from official sources, research feeds and reporting, grouped into stories and ranked by the BharatHunt Trend Score.
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842 stories
CausalXRL: Explainable Reinforcement Learning through Causal Graph Reasoning. The story centres on Benchmarks. Reported by arXiv. Bharat Hunt files it under AI Research — the section covering papers, benchmarks, evaluations, interpretability and safety results. Open the original report for the full detail.
How I Aligned Your Model: Scaling Up Multilingual Safety Training with Templates. It centres on AI Safety, and also names Benchmarks. Reported by arXiv. Bharat Hunt files it under AI Research and AI Models — the section covering papers, benchmarks, evaluations, interpretability and safety results.
Patient-specific rubric-based evaluation of large language models across longitudinal cancer care. The story centres on Benchmarks. Reported by arXiv. Bharat Hunt files it under AI Research and AI Models — the section covering papers, benchmarks, evaluations, interpretability and safety results.
An Empirical Study of Recent Large Language Models for Knowledge Graph to Text Generation on Standard Benchmarks. The story centres on Benchmarks. Reported by arXiv. Bharat Hunt files it under AI Research and AI Models — the section covering papers, benchmarks, evaluations, interpretability and safety results.
Towards User Simulators That Stay On Task And Still Sound Natural. Reported by arXiv. Bharat Hunt files it under AI Research — the section covering papers, benchmarks, evaluations, interpretability and safety results. Open the original report for the full detail.
CAT-alyzing Honesty: Continuous Adversarial Training for Transferable LLM Honesty. It centres on AI Safety, and also names Llama and Benchmarks. Reported by arXiv. Bharat Hunt files it under AI Models and AI Agents — the section covering a new or updated model, its capabilities, benchmarks or availability.
What Matters in Quality-Aware VLA Pretraining: A Benchmark and Empirical Study. It centres on Benchmarks, and also names Fine-tuning and Inference. Reported by arXiv. Bharat Hunt files it under AI Research and AI Models — the section covering papers, benchmarks, evaluations, interpretability and safety results.
MC-EvoBench: Auditing Experience-Driven Self-Improvement in Minecraft Agents. It centres on Benchmarks, and also names GitHub. Reported by arXiv. Bharat Hunt files it under AI Research and Robotics — the section covering papers, benchmarks, evaluations, interpretability and safety results.
Not Just an Evaluation: A Case Study of Discourse Particle Sensitivity in Language Models. It centres on Benchmarks, and also names GitHub. Reported by arXiv. Bharat Hunt files it under AI Research — the section covering papers, benchmarks, evaluations, interpretability and safety results.
FAITH: Feasibility-Aware Safety-Filtered RL for High-Dimensional Systems. Reported by arXiv. Bharat Hunt files it under Robotics and AI Regulation — the section covering embodied AI, humanoids, autonomous vehicles and drones. Open the original report for the full detail.
A persistent accuracy ceiling in automated verbal deception detection. It centres on Meta, and also names Benchmarks. Reported by arXiv. Bharat Hunt files it under AI Research and AI Models — the section covering papers, benchmarks, evaluations, interpretability and safety results.
Google invests millions in Mark Zuckerberg’s efforts to create a ‘virtual cell’. It centres on Google, and also names Mark Zuckerberg and Google DeepMind. Reported by The Verge. Bharat Hunt files it under AI Research and AI Startups — the section covering papers, benchmarks, evaluations, interpretability and safety results.
Bharat Hunt links to original reporting and does not republish it. Headlines and links belong to their publishers; summaries and the trend ranking are ours. All AI stories
Stories in the last 24 hours, against the 24 before it. A change is shown only where the earlier window had enough coverage to compare against.
Our own 0-100 ranking, not an industry metric and not anyone else’s “trending” number. It combines how recently a story was covered (32%), how many independent publications covered it (28%), how fast that coverage is arriving right now (20%), how reliable those sources are (12%) and how many people opened the story (8%).
A story we cannot date carries no score at all rather than a made-up one, so some cards show no badge. Summaries are written by Bharat Hunt from the headline and the coverage — the original article is always the source of truth, and every link goes to the publisher.