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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Does Local Video Understanding Transfer Across Encounters? The EgoGears Benchmark. It centres on Benchmarks, and also names AI Safety and GitHub. Reported by arXiv. Bharat Hunt files it under AI Research and AI Models — the section covering papers, benchmarks, evaluations, interpretability and safety results.
Source-preserving alignment for robust evidence localization in scientific PDFS. 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.
QC-Stark: A Multi-Task Benchmark Revealing Capability Dissociations in LLMs Evaluated on Quantum Computing Tasks. It centres on Benchmarks, and also names Hugging Face. Reported by arXiv. Bharat Hunt files it under AI Research and AI Models — the section covering papers, benchmarks, evaluations, interpretability and safety results.
The Unequal Influence of Bad Advice: Using Training Data Attribution to Modulate Emergent Misalignment. It centres on Fine-tuning, and also names AI Safety and Benchmarks. Reported by arXiv. Bharat Hunt files it under AI Research and AI Models — the section covering papers, benchmarks, evaluations, interpretability and safety results.
Representation Alignment as a Bottleneck in LLM-Based Retrosynthesis Planning. 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.
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.
EquivSVA: A Formally Verified Dataset of Behavioral Assertions Across Equivalent RTL Implementations. It centres on Benchmarks, and also names GitHub. Reported by arXiv. Bharat Hunt files it under AI Research — the section covering papers, benchmarks, evaluations, interpretability and safety results.
JEV versus LLMs: Accuracy, Cost and Calibration on Seven Political Science Replications. It centres on GPT, and also names OpenAI. Reported by arXiv. Bharat Hunt files it under AI Research and AI Models — the section covering papers, benchmarks, evaluations, interpretability and safety results.
FREA: A Multi-Source Expert Benchmark for Reaction Feasibility Verification. 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.
Polar Switch: Fast Muon Orthogonalization with Polynomial Circuits. The story centres on Gemma. Reported by arXiv. Bharat Hunt files it under AI Research and AI Hardware — the section covering papers, benchmarks, evaluations, interpretability and safety results. Open the original report for the full detail.
JEV-as-a-Judge: Accept When Confident, Escalate When Unsure. 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. Open the original report for the full detail.
DAMI: Dual-Axis Memory Initialization for Enhanced Long-Context Retrieval in Gated Linear Attention. Reported by arXiv. Bharat Hunt files it under AI Research and AI Coding — 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.