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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265 stories
Knowingly Wrong: Training LLMs Against Their Own Beliefs can Induce Misalignment. The story centres on Fine-tuning. 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.
From Reasoning to Action: Benchmarking Frontier AI Models on LIBERO-10 Robot Manipulation Tasks. It centres on Benchmarks, and also names Fine-tuning and GPT. Reported by arXiv. Bharat Hunt files it under AI Models and Robotics — the section covering a new or updated model, its capabilities, benchmarks or availability.
Brain2Vec: A Self-Supervised 3D Foundation Model for Structural Brain MRI. It centres on Fine-tuning, and also names GitHub. Reported by arXiv. Bharat Hunt files it under AI Models and AI Tools — the section covering a new or updated model, its capabilities, benchmarks or availability.
Training with ML-labeled Data via Stochastic First- and Zeroth-Order Optimization. The story centres on Fine-tuning. Reported by arXiv. Bharat Hunt files it under AI Models — the section covering a new or updated model, its capabilities, benchmarks or availability.
CAT-alyzing Honesty: Continuous Adversarial Training for Transferable LLM Honesty. It centres on AI Safety, and also names Llama and Benchmarks. Reported by arXiv. Bharat Hunt files it under AI Models and AI Agents — the section covering a new or updated model, its capabilities, benchmarks or availability.
What Matters in Quality-Aware VLA Pretraining: A Benchmark and Empirical Study. It centres on Benchmarks, and also names Fine-tuning and Inference. Reported by arXiv. Bharat Hunt files it under AI Research and AI Models — the section covering papers, benchmarks, evaluations, interpretability and safety results.
Overcoming Prior Barriers: Supervised Fine-Tuning under Long-Tail Distribution. The story centres on Fine-tuning. Reported by arXiv. Bharat Hunt files it under AI Models — the section covering a new or updated model, its capabilities, benchmarks or availability. Open the original report for the full detail.
Detect and Suppress: A Mechanistic Defense against Adversarial Patches in VLA Models. It centres on Inference, and also names Fine-tuning. Reported by arXiv. Bharat Hunt files it under AI Models and Robotics — the section covering a new or updated model, its capabilities, benchmarks or availability.
Is Weight Tying Still Beneficial for Decoder-Only LLMs in Private Settings Under DP-SGD? The story centres on Fine-tuning. Reported by arXiv. Bharat Hunt files it under AI Regulation — the section covering law, policy, courts, standards and government positions on AI.
Estimating and Orthogonalizing Unknown Pre-training Gradients for Continual Fine-tuning of Large Language Models. The story centres on Fine-tuning. Reported by arXiv. Bharat Hunt files it under AI Models — the section covering a new or updated model, its capabilities, benchmarks or availability.
Domain-Specific Hallucination Detection in Large Language Models. It centres on Benchmarks, and also names Inference and Fine-tuning. 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.
Post-Hoc Feature Selection Layer for Neural Networks Interpretability. The story centres on Fine-tuning. 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.
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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.