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.
Ingestion is behind — last completed run was 23 hours ago.
News ingestion is temporarily unavailable.
The pipeline has not completed a run recently, so what is below may be behind. Nothing here is ever filled in with placeholder news.
607 stories
Systemization of Knowledge (SoK): Human-Centered AI Safety for Youth. The story centres on AI Safety. 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.
Behavior is Not Enough: A Mechanism-Based Evaluation of Social Norm Emergence in LLM Societies. The story centres on Benchmarks. Reported by arXiv. Bharat Hunt files it under AI Research and AI Agents — the section covering papers, benchmarks, evaluations, interpretability and safety results.
How High Is 0.6? Floors, Ceilings, and Headroom in Interpretability Probing. The story centres on Meta. Reported by arXiv. Bharat Hunt files it under AI Research and AI Models — the section covering papers, benchmarks, evaluations, interpretability and safety results.
How to scale your HEP ML models: A recipe for robust architecture comparisons at scale. Reported by arXiv. Bharat Hunt files it under AI Research and AI Models — the section covering papers, benchmarks, evaluations, interpretability and safety results.
ScAn-Bench: Evaluating Scaling Analysis Methodology. 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. Open the original report for the full detail.
PP-Net: A Hybrid Physical-Prior Neural Network for Scattered Light Removal in Biomedical Images on Embedded Devices. It centres on Benchmarks, and also names Inference. Reported by arXiv. Bharat Hunt files it under AI Research — the section covering papers, benchmarks, evaluations, interpretability and safety results.
How to Estimate Whether You Have Found Several Needles in a Haystack: Measuring Calibration in Multi-Label Text Classification. 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.
Toward Alignment Scaling Laws: A Framework and First Preregistered Measurements. The story centres on AI Safety. Reported by arXiv. Bharat Hunt files it under AI Research and AI Models — the section covering papers, benchmarks, evaluations, interpretability and safety results.
Reproducible AI Requires Reproducible Randomness. It centres on Reliance Jio, and also names PyTorch and TensorFlow. Reported by arXiv. Bharat Hunt files it under AI Research and AI Tools, and tracks it as India coverage — the section covering papers, benchmarks, evaluations, interpretability and safety results.
Recursive self-improvement of AI research agents. It centres on Inference, and also names Benchmarks. Reported by arXiv. Bharat Hunt files it under AI Research and AI Agents — the section covering papers, benchmarks, evaluations, interpretability and safety results.
Wiki-Talkie: Multilingual Benchmarking of Persona-Based Agents on Real-World Discussions. 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.
ReX-GG: a LLM Ensemble Pipeline for Relation-extraction and Graph Generation. Reported by arXiv. Bharat Hunt files it under AI Research and AI Models — the section covering papers, benchmarks, evaluations, interpretability and safety results. 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
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.