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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842 stories
Density Ratio Estimation with Stein Displacement Fields. The story centres on Inference. Reported by arXiv. Bharat Hunt files it under AI Research — the section covering papers, benchmarks, evaluations, interpretability and safety results. Open the original report for the full detail.
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.
Learning to Read the Contextual Tokens in Diffusion Transformers. It centres on Multimodal, and also names AI Safety. 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.
AdaGEPA: Adaptive Feedback Allocation for Reflective Prompt Optimization. The story centres on Benchmarks. Reported by arXiv. Bharat Hunt files it under AI Research — the section covering papers, benchmarks, evaluations, interpretability and safety results. Open the original report for the full detail.
The Download: OpenAI’s chief research officer explains its hacking response. It centres on OpenAI, and also names Hugging Face. Reported by MIT Technology Review. Bharat Hunt files it under AI Research and Open Source AI — the section covering papers, benchmarks, evaluations, interpretability and safety results.
Jaxolotl: A Unified High-Performance Benchmark Suite for LTL-Based Multi-Task RL. The story centres on Benchmarks. Reported by arXiv. Bharat Hunt files it under AI Research and AI Models — the section covering papers, benchmarks, evaluations, interpretability and safety results.
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.
Brenier Meets Adversarial Training: Optimal Transport Geometry for Robust Learning. Reported by arXiv. Bharat Hunt files it under AI Research — the section covering papers, benchmarks, evaluations, interpretability and safety results. Open the original report for the full detail.
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.
UniAR: A Unified Framework for Autism Recognition Enhanced by Multi-View Prompt Learning. It centres on Multimodal, 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.
How Reproducible Are Evaluation Conclusions? A Self-Audit of LLM-Inferred Prompt Structure. It centres on Benchmarks, and also names Inference. Reported by arXiv. Bharat Hunt files it under AI Research and AI Models — the section covering papers, benchmarks, evaluations, interpretability and safety results.
LEAP-CBF: A Safety Filter for Uncertain Systems with Least-Effort Adversarial Potentials. 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.
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.