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.
Updated 2 hours ago117 stories in the last 24 hours
257 stories
Scaling Mathematical Proof Supervision from Structured Textbooks. It centres on Fine-tuning, and also names Benchmarks. Reported by arXiv. Bharat Hunt files it under AI Models and AI Funding — the section covering a new or updated model, its capabilities, benchmarks or availability.
RM-OPD: On-Policy Distillation for Generative Reward Models. It centres on Fine-tuning, and also names Benchmarks. 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.
Do Personality-Tuned LLMs Make Better Social Agents? It centres on Fine-tuning, and also names Qwen and AI Safety. Reported by arXiv. Bharat Hunt files it under Open Source AI and Robotics — the section covering open-weight models, permissive licences and community releases.
Behavioral Couplings: From Formation to Behavioral Emergence in Language Models. It centres on Fine-tuning, and also names Benchmarks. Reported by arXiv. Bharat Hunt files it under AI Coding and AI Models — the section covering code generation, developer agents, IDEs and software engineering.
Trust the Direction, Search the Step: Zero-and-First-Order Methods for LLM Fine-Tuning. It centres on Fine-tuning, and also names GitHub. Reported by arXiv. Bharat Hunt files it under AI Models — the section covering a new or updated model, its capabilities, benchmarks or availability.
Beyond Average Safety: Chance-Constrained LLM Fine-tuning. The story centres on Fine-tuning. Reported by arXiv. Bharat Hunt files it under AI Models — the section covering a new or updated model, its capabilities, benchmarks or availability. Open the original report for the full detail.
Fine-tuning GPT-OSS-20B for multilingual reasoning on Amazon SageMaker HyperPod. It centres on Fine-tuning, and also names GPT and Amazon. Reported by AWS. Bharat Hunt files it under AI Models — the section covering a new or updated model, its capabilities, benchmarks or availability.
SpaceCast-Bench: Evaluating Predictive Spatial Reasoning in Vision-Language Models. It centres on Benchmarks, and also names Inference and Fine-tuning. Reported by arXiv. Bharat Hunt files it under AI Models — the section covering a new or updated model, its capabilities, benchmarks or availability.
Fine-tune a search agent with multi-turn RL on Amazon SageMaker AI. It centres on Amazon, and also names Fine-tuning. Reported by AWS. Bharat Hunt files it under AI Models and AI Tools — the section covering a new or updated model, its capabilities, benchmarks or availability.
Watermark Basins: Black-Box Detection of Distilled LLMs. It centres on Fine-tuning, and also names AI Safety. Reported by arXiv. Bharat Hunt files it under AI Models and AI Research — the section covering a new or updated model, its capabilities, benchmarks or availability.
Retiming Robot Demonstrations with a Learned Speed Prior. The story centres on Fine-tuning. 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.
Inject a Turn, Not a Token: Learning Where to Redirect LLM Reasoning for Self-Correction. It centres on Fine-tuning, 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.
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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.