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 just now147 stories in the last 24 hours
3432 stories
One Figure, Every Canvas: Editable Flowchart Relayout via Agentic Pipeline. It centres on Benchmarks, and also names Gemini and Vercel. Reported by arXiv. Bharat Hunt files it under AI Agents and AI Models — the section covering autonomous and tool-using systems, agent frameworks, MCP and orchestration.
How Many Labels Does a Language Need? Annotation Budgets and Cross-Lingual Pooling for African-Language Text Classification. It centres on Benchmarks, and also names Nothing. Reported by arXiv. Bharat Hunt files it under AI Models and AI Hardware — the section covering a new or updated model, its capabilities, benchmarks or availability.
Can Vision-Language Models Stay Helpful When Facing Implicit Risks? Intent-Privilege OPSD for Efficient Safety-Helpfulness Alignment. It centres on AI Safety, and also names Inference 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.
Two Conformal Constructions for Adaptive Within-Document AI-Text Screening. The story centres on Benchmarks. Reported by arXiv. Bharat Hunt files it under AI Research and AI Regulation — the section covering papers, benchmarks, evaluations, interpretability and safety results. Open the original report for the full detail.
CG-HAF: An Interpretable Global-Local Lesion-Burden Fusion Framework for Ordinal Acne Severity Grading in Agentic Skincare Support. It centres on Benchmarks, and also names AI Safety. Reported by arXiv. Bharat Hunt files it under AI Agents — the section covering autonomous and tool-using systems, agent frameworks, MCP and orchestration.
Diverse Geometries, Frozen Weights: Robust Heterogeneous Treatment-Effect Estimation via Causal Expert Ensembles. It centres on Benchmarks, and also names Phi. Reported by arXiv. Bharat Hunt files it under AI Research and AI Regulation — the section covering papers, benchmarks, evaluations, interpretability and safety results.
Measuring the Serving Stack Instead of the Model: Hidden Confounds in Local Tool-Use Evaluation. It centres on Benchmarks, and also names Ollama and Phi. Reported by arXiv. Bharat Hunt files it under AI Tools and AI Models — the section covering applications people use — assistants, editors, search, productivity.
When Should LLMs Abstain? Chain-of-Self-Questioning for Selective Risk Control. It centres on Reasoning Models, and also names Benchmarks. Reported by arXiv. Bharat Hunt files it under AI Research and Open Source AI — the section covering papers, benchmarks, evaluations, interpretability and safety results.
AI Isn't Getting Dumber, We're Getting Pickier: Rising Standards in Human Evaluation of Large Language Models. It centres on Benchmarks, and also names GPT. Reported by arXiv. Bharat Hunt files it under AI Research and Open Source AI — the section covering papers, benchmarks, evaluations, interpretability and safety results.
An Effective Spoken Language Understanding Approach Leveraging Query-Adaptive Intent Detection. It centres on Fine-tuning, and also names Perplexity and Gemma. Reported by arXiv. Bharat Hunt files it under AI Research — the section covering papers, benchmarks, evaluations, interpretability and safety results.
Synthesis of Ovarian Cancer Multi-Omics Datasets: A Biology-Informed Evaluation Framework. 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.
Loop Before You Leap: What Recurrent Depth Can and Cannot Buy in Data-Limited Pretraining. It centres on Multimodal, and also names Inference. 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.
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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.