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 1 hour ago121 stories in the last 24 hours
502 stories
A Matryoshka Hierarchical RAG for Efficient Multi-Hop Question Answering. It centres on RAG, 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.
UNREAL: Unifying Retrieval and Long-Context with a Single Model. It centres on Inference, and also names RAG. 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.
Building an AI-powered contract intelligence platform with Amazon Quick and Amazon Bedrock AgentCore. It centres on Amazon, and also names RAG. Reported by AWS. Bharat Hunt files it under AI Agents and AI Tools — the section covering autonomous and tool-using systems, agent frameworks, MCP and orchestration.
EviGen: Predictive Evidence Scaffolding for Verifiable Clinical Rationale Generation. It centres on RAG, 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.
Mem++: Non-Destructive Memory for Long-Term Organizational LLM Agents. It centres on Benchmarks, and also names GPT and RAG. Reported by arXiv. Bharat Hunt files it under AI Models — the section covering a new or updated model, its capabilities, benchmarks or availability.
Reimagining Enterprise RAG for Multi-Agent Systems: A Knowledge Layer That Organizes Before It Retrieves. The story centres on RAG. Reported by arXiv. Bharat Hunt files it under Enterprise AI and AI Agents — the section covering deployment inside businesses — platforms, adoption, cost, integration.
Q2D-Web: A Large-Scale Benchmark for Retrieval in Agentic RAG Systems. It centres on Benchmarks, and also names RAG and Hugging Face. 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.
Computation Over Geometry: Meaning Identity Is Computed, Not Shipped in the Embeddings. It centres on RAG, and also names Mistral AI and Llama. Reported by arXiv. Bharat Hunt files it under AI Models — the section covering a new or updated model, its capabilities, benchmarks or availability.
SANTA++: Sampling Attention through Representative Keys. It centres on RAG, and also names GitHub. Reported by arXiv. Bharat Hunt files it under AI Hardware — the section covering chips, accelerators, data centres, on-device inference and supply. Open the original report for the full detail.
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.
RAG-PIBench: A Leakage-Aware Benchmark for Prompt-Injection Detection in Trustworthy RAG Systems. It centres on RAG, 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.
Stale-Document Poisoning: When Outdated Retrieval Overrides Correct Model Answers. It centres on RAG, and also names AI Safety 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.
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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.