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.
Ingestion is behind — last completed run was 11 hours ago.
News ingestion is temporarily unavailable.
The pipeline has not completed a run recently, so what is below may be behind. Nothing here is ever filled in with placeholder news.
387 stories
Learning When to Commit: Schedule Distillation for Fast Diffusion Language Models. The story centres on GitHub. Reported by arXiv. Bharat Hunt files it under Generative AI and AI Models — the section covering text, image, audio and video generation — products and their output.
Prompt Sensitivity in Tool-Augmented Model Comparisons. The story centres on Benchmarks. Reported by arXiv. Bharat Hunt files it under AI Tools and AI Models — the section covering applications people use — assistants, editors, search, productivity. Open the original report for the full detail.
WOVEN: Weaving Visual World Modeling into Multimodal LLMs. It centres on Multimodal, and also names Benchmarks and GPT. Reported by arXiv. Bharat Hunt files it under AI Models and Robotics — the section covering a new or updated model, its capabilities, benchmarks or availability.
Scaling Laws for Looped Mixture of Experts. 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. Open the original report for the full detail.
Imagine3D-LLM: Teaching MLLMs to Imagine 3D Scenes Before Answering. It centres on Multimodal, and also names Benchmarks. Reported by arXiv. Bharat Hunt files it under AI Models — the section covering a new or updated model, its capabilities, benchmarks or availability.
SEABench: Benchmarking Endogenous Misalignment In Self-Evolving Agents. It centres on Benchmarks, and also names Reasoning Models. Reported by arXiv. Bharat Hunt files it under AI Research and AI Tools — the section covering papers, benchmarks, evaluations, interpretability and safety results.
Frontier Learning: Training LLM Reasoners at the Edge of Capability. Reported by arXiv. Bharat Hunt files it under AI Models and AI Regulation — the section covering a new or updated model, its capabilities, benchmarks or availability. Open the original report for the full detail.
Transcribe, Translate, and Optimize: Joint Reward Learning for Speech Translation. It centres on Reasoning Models, and also names Fine-tuning and Inference. Reported by arXiv. Bharat Hunt files it under AI Regulation — the section covering law, policy, courts, standards and government positions on AI.
Monitoring and Discovering Reward Hacking with Internal Representations during LLM Evaluations. It centres on Qwen, and also names Benchmarks and Reasoning Models. Reported by arXiv. Bharat Hunt files it under AI Models and Open Source AI — the section covering a new or updated model, its capabilities, benchmarks or availability.
Collective Dynamics of Self-Reinforcement: How Cross-Task Coupling Drives Policy Collapse in Unsupervised RLVR. Reported by arXiv. Bharat Hunt files it under AI Regulation and AI Models — the section covering law, policy, courts, standards and government positions on AI.
From Trajectories to Differentiated Skills: Cognitive Differentiation for Self-Improving Agents. It centres on Meta, and also names 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.
EgoLAP: Learning from Egocentric Human Data through Language-Action Reasoning. The story centres on Reasoning Models. Reported by arXiv. Bharat Hunt files it under Robotics — the section covering embodied AI, humanoids, autonomous vehicles and drones. Open the original report for the full detail.
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
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.