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
Legal-R1: Agentic Retrieval for Evidence-Based Legal Reasoning. It centres on RAG, and also names Fine-tuning and Benchmarks. Reported by arXiv. Bharat Hunt files it under AI Agents and AI Tools — the section covering autonomous and tool-using systems, agent frameworks, MCP and orchestration.
VioLA: Learning Generalist Humanoid Control Policies from Human Data. 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.
HarnessSQL: Harness-Native Training for SQL Agents in Realistic Database Environments. It centres on Inference, and also names Fine-tuning and Benchmarks. Reported by arXiv. Bharat Hunt files it under AI Models — the section covering a new or updated model, its capabilities, benchmarks or availability.
MemFLoRA: Memory-Floor LoRA for CNN Adaptation at the Edge. The story centres on Fine-tuning. 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.
TACO: Ternary Absolute-max Column-wise One-sparse Optimizer for LLM Fine-Tuning. The story centres on Fine-tuning. Reported by arXiv. Bharat Hunt files it under AI Hardware and AI Models — the section covering chips, accelerators, data centres, on-device inference and supply.
Comparison of techniques for fine-tuning open-weight models for entity extraction from radiology reports. It centres on Fine-tuning, and also names Gemma and GPT. Reported by arXiv. Bharat Hunt files it under Open Source AI and AI Models — the section covering open-weight models, permissive licences and community releases.
Less Sycophancy, Stronger Refusal? Lessons for AI Safety from Mechanistic Interpretability. It centres on AI Safety, and also names Inference and Fine-tuning. Reported by arXiv. Bharat Hunt files it under AI Research and AI Funding — the section covering papers, benchmarks, evaluations, interpretability and safety results.
FAVoR: Measuring and Mitigating Author-Style Homogenization in Federated Personalized Generation. 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.
Search-Aware Reinforcement Learning for Multi-Component Query Understanding in Roblox Game Search. The story centres on Fine-tuning. Reported by arXiv. Bharat Hunt files it under AI Regulation and AI Tools — the section covering law, policy, courts, standards and government positions on AI.
Your Transformer Can Hold Two Thoughts at Once: Evidence of Linear Superposition in LLMs. 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.
Time series generation with spectrally aligned latent flow matching. It centres on Fine-tuning, 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.
PlannerForge: LLM Agents for Scenario-Based Testing of Motion Planners in Autonomous Driving. The story centres on Fine-tuning. Reported by arXiv. Bharat Hunt files it under Open Source AI and AI Agents — the section covering open-weight models, permissive licences and community releases.
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.