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 34 minutes ago123 stories in the last 24 hours
503 stories
Agensh: Scaling Organizational Intelligence to 1,024 Agents. The story centres on GPT. Reported by arXiv. Bharat Hunt files it under AI Agents and AI Funding — the section covering autonomous and tool-using systems, agent frameworks, MCP and orchestration.
A2M: Trace-Optimized Agent Hijacking in the MCP Ecosystem. It centres on Model Context Protocol, and also names GitHub. Reported by arXiv. Bharat Hunt files it under AI Agents and AI Coding — the section covering autonomous and tool-using systems, agent frameworks, MCP and orchestration.
ProAS: A Progress-Aware Advantage Shaping Framework for Multi-Turn Agentic Reinforcement Learning. The story centres on GitHub. Reported by arXiv. Bharat Hunt files it under AI Agents — the section covering autonomous and tool-using systems, agent frameworks, MCP and orchestration.
OpenAI expands partnership with Oracle to bring its apps to ChatGPT. It centres on OpenAI, and also names GPT. Reported by OpenAI. Bharat Hunt files it under AI Agents — the section covering autonomous and tool-using systems, agent frameworks, MCP and orchestration.
Proof-Carrying Contracts: Commitments AI Agents Cannot Break. Reported by arXiv. Bharat Hunt files it under AI Agents and AI Regulation — the section covering autonomous and tool-using systems, agent frameworks, MCP and orchestration. Open the original report for the full detail.
Claims of emergence in AI should be grounded in information decomposition. Reported by arXiv. Bharat Hunt files it under AI Agents and AI Research — the section covering autonomous and tool-using systems, agent frameworks, MCP and orchestration. Open the original report for the full detail.
KV-streams for Efficient Compaction in Agentic Reinforcement Learning. Reported by arXiv. Bharat Hunt files it under AI Agents and AI Hardware — the section covering autonomous and tool-using systems, agent frameworks, MCP and orchestration. Open the original report for the full detail.
Shockingly Simple Self-retrospection Improves Agentic Models Without RL. The story centres on Fine-tuning. 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.
StebBench: Diagnosing Real-World Procedural Understanding in AI Agents. The story centres on 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.
Harness Learning Enables Generalizable Test-Time Adaptation. The story centres on Meta. 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. Open the original report for the full detail.
Which Model for Which Role? Cost-Efficient Pareto Recommendation for LLM Agentic Workflows. The story centres on 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.
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.
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.