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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265 stories
AdaRSI: On-Demand Recursive Self-Improvement for Embodied Agents. The story centres on Fine-tuning. Reported by arXiv. Bharat Hunt files it under Robotics and AI Funding — the section covering embodied AI, humanoids, autonomous vehicles and drones. Open the original report for the full detail.
When Should Agents Think? Adaptive Reasoning via Cross-Turn Estimation. 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.
A Systematic Study of Small Language Models on Abstract Reasoning Tasks. It centres on Benchmarks, and also names Fine-tuning. Reported by arXiv. Bharat Hunt files it under AI Research and AI Models — the section covering papers, benchmarks, evaluations, interpretability and safety results.
LLM2Jev: LLMs Are Already Jev-Style Decision Models -- When and How to Fine-Tune Them. It centres on Fine-tuning, and also names Inference and Multimodal. Reported by arXiv. Bharat Hunt files it under AI Models — the section covering a new or updated model, its capabilities, benchmarks or availability.
When Context Misleads: In-context Learning with Jurisdiction in Large Language Models. It centres on Benchmarks, and also names Fine-tuning and GitHub. 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.
Pseudo-Labels as Rewards, Not Targets: Group-Relative Policy Optimization for Semi-Supervised ASR. It centres on Fine-tuning, and also names Whisper. Reported by arXiv. Bharat Hunt files it under AI Regulation — the section covering law, policy, courts, standards and government positions on AI.
Loopholing All the Way: Language Modeling with pure Self-Conditioning. It centres on Fine-tuning, and also names GitHub. Reported by arXiv. Bharat Hunt files it under AI Coding and AI Funding — the section covering code generation, developer agents, IDEs and software engineering.
Ambient Discrete Diffusion: Using the Wrong Data at the Right Time for Data Efficient Learning. The story centres on Fine-tuning. 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.
Yalda-Embedding: Open Development of a Competitive Persian Retrieval Model. It centres on Fine-tuning, and also names Inference and Benchmarks. 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.
SyRA-CS: Synthetic-to-Real Adaptation for Code-Switched ASR with Meta-Learning. It centres on Meta, and also names Fine-tuning. Reported by arXiv. Bharat Hunt files it under AI Coding and AI Models — the section covering code generation, developer agents, IDEs and software engineering.
Learning Kilometer-Scale Weather Prediction with Global-Regional Alignment. It centres on AI Safety, and also names Fine-tuning. Reported by arXiv. Bharat Hunt files it under AI Research and AI Models — the section covering papers, benchmarks, evaluations, interpretability and safety results.
T-Search: An Open Agentic Retriever and Playground for Hard Multi-Step Search. It centres on Fine-tuning, and also names Benchmarks. Reported by arXiv. Bharat Hunt files it under AI Agents and AI Models — the section covering autonomous and tool-using systems, agent frameworks, MCP and orchestration.
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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.