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
Adaptive Fisher-Whitened Cross-Covariance for Low-Resource Speech Recognition. It centres on Fine-tuning, and also names Whisper and Benchmarks. Reported by arXiv. Bharat Hunt files it under AI Models — the section covering a new or updated model, its capabilities, benchmarks or availability.
Nuha-Speech: Building General-Purpose Arabic Speech-LLMs. It centres on Benchmarks, and also names Fine-tuning 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.
UpLoop: Training Looped Transformers via Upcycling at No Extra Pretraining Compute. 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.
Did the Model Use That? Counterfactual Training Boosts Self-Attribution in Language Models. It centres on Reasoning Models, and also names 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.
BeNice! Closing Looping's Inference-Memory Gap via KV-Sharing. It centres on Inference, and also names Fine-tuning and Perplexity. Reported by arXiv. Bharat Hunt files it under AI Research and AI Models — the section covering papers, benchmarks, evaluations, interpretability and safety results.
Investigating the Impact of Learner Errors on Social Bias in Large Language Models. It centres on Benchmarks, and also names AI Safety 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.
Your Prompt Should Do More: Effects of Retrieval Instructions in Embedding Models. It centres on Benchmarks, and also names 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.
Differentially Private Mixing of Public Datasets Improves Private Learning. It centres on Fine-tuning, and also names Perplexity. Reported by arXiv. Bharat Hunt files it under AI Regulation — the section covering law, policy, courts, standards and government positions on AI.
Getting Your Guidance Weights Right in diffusion and flow-matching posterior sampling. The story centres on Fine-tuning. Reported by arXiv. Bharat Hunt files it under Generative AI — the section covering text, image, audio and video generation — products and their output.
Traversing the solution space of neural networks with Hessian Null Space Continuation. It centres on AI Safety, and also names 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.
Adaptive Anisotropic Attention for Axis-Structured Signals. 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.
Train An Imaginative Agent. The story centres on Fine-tuning. Reported by arXiv. Bharat Hunt files it under AI Agents and AI Regulation — the section covering autonomous and tool-using systems, agent frameworks, MCP and orchestration. 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
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.