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 1 hour ago121 stories in the last 24 hours
44 stories
SolBehave: Evaluating the Behavioural Correctness of LLM-Generated Smart Contracts. It centres on Benchmarks, and also names Gemma. Reported by arXiv. Bharat Hunt files it under AI Models and Open Source AI — the section covering a new or updated model, its capabilities, benchmarks or availability.
Interpretable sparse fine-tuning with TopKLoRA enables faithful circuit discovery. It centres on Fine-tuning, and also names Gemma and GitHub. Reported by arXiv. Bharat Hunt files it under AI Research — the section covering papers, benchmarks, evaluations, interpretability and safety results.
Co-Evolving Harnesses and Models: On-Policy Correction Helps Weaker Models Catch Up Where Imitation Fails. It centres on Fine-tuning, and also names Gemma and Meta. Reported by arXiv. Bharat Hunt files it under AI Models and AI Regulation — the section covering a new or updated model, its capabilities, benchmarks or availability.
A Zeroth-Order Paradigm for LLM Preference Alignment. It centres on AI Safety, and also names Fine-tuning and Mistral AI. Reported by arXiv. Bharat Hunt files it under AI Research and AI Models — the section covering papers, benchmarks, evaluations, interpretability and safety results.
Navigating the digital spectrum: Assessing political bias, stability, and downstream fairness in Large Language Models. It centres on Benchmarks, and also names Gemma and Qwen. Reported by arXiv. Bharat Hunt files it under AI Models — the section covering a new or updated model, its capabilities, benchmarks or availability.
EmbeddingGemma 2: an open, lightweight multimodal embedding model. The story centres on Multimodal. Google DeepMind and blog.google have both covered it. Bharat Hunt files it under AI Models — the section covering a new or updated model, its capabilities, benchmarks or availability.
Measure Before You Train: Typed Decision Models vs. a Classical Baseline vs. a Generative LLM for Routing a Personal Multilingual Inbox. The story centres on Gemma. Reported by arXiv. Bharat Hunt files it under AI Models — the section covering a new or updated model, its capabilities, benchmarks or availability.
Decodable but Misrouted: Sparse Features Uncover a Readout Gap in Vision-Language Models for Harmful Meme Detection. It centres on Gemma, and also names Benchmarks and Qwen. Reported by arXiv. Bharat Hunt files it under AI Models and AI Coding — the section covering a new or updated model, its capabilities, benchmarks or availability.
MultiInpaintCOCO: Fine-Grained Visual Understanding in Multiple Languages. It centres on Benchmarks, and also names Multimodal and Gemma. 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.
ALIAS: Evaluating Performance and Memorization in LLM-Written TLA+ Specifications. It centres on Benchmarks, and also names Claude and GPT. 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.
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.
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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.