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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3337 stories
The Semantic Bottleneck: Leveraging Semantic Representations for Non-Invasive Speech Decoding. 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.
Smartphone-Enabled Depth-Aware Transillumination Imaging for Detection of Chicken Breast Fillet Myopathies in Chicken Fillets. The story centres on Multimodal. Reported by arXiv. Bharat Hunt files it under AI Research — the section covering papers, benchmarks, evaluations, interpretability and safety results.
A Geometric Invariant of Attention Predicts Head Specialization from Weights Alone. It centres on Perplexity, and also names GitHub. Reported by arXiv. Bharat Hunt files it under AI Research and AI Coding — the section covering papers, benchmarks, evaluations, interpretability and safety results.
Adversarial Training with Large Step Sizes: Implicit Bias, Optimization, and Sharpness. 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.
Dialect Matters: A Dialect-Aware Baseline for Igala-English Machine Translation. 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.
DAPS: Dependency-Aware Premise Selection for LLM Theorem Proving. 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. Open the original report for the full detail.
TRACE: Memory-Efficient Time Series Class-Incremental Learning via Temporal-Spectral Reconstruction. The story centres on Benchmarks. Reported by arXiv. Bharat Hunt files it under AI Regulation — the section covering law, policy, courts, standards and government positions on AI. Open the original report for the full detail.
Self-normalized Bernstein Inequalities for Regenerative Processes and Applications to Learning Without Independence. 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.
When Do LLMs Help with Feature Selection? A Systematic Empirical Study. It centres on Benchmarks, and also names Llama and Qwen. Reported by arXiv. Bharat Hunt files it under AI Research and AI Models — the section covering papers, benchmarks, evaluations, interpretability and safety results.
Contextual Candour: Role-Aware Graduated Disclosure for LLMs over Mixed-Sensitivity Contexts. The story centres on Benchmarks. Reported by arXiv. Bharat Hunt files it under Enterprise AI — the section covering deployment inside businesses — platforms, adoption, cost, integration. Open the original report for the full detail.
On the Tip of the Tongue: First-Token Cueing as a Behavioral Probe of Recall Failures in LLMs. 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.
Input-Blind Controls Produce Substantial Oracle Headroom for Layer Programs in Multiple-Choice Evaluation. It centres on Benchmarks, and also names Inference and Llama. Reported by arXiv. Bharat Hunt files it under AI Research and AI Models — the section covering papers, benchmarks, evaluations, interpretability and safety results.
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.