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
AfriGuard: Safety and Robust Refusal For African Languages. 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.
Towards Continual Learning: Mitigating Forgetting With Subspace Tuning. The story centres on Fine-tuning. 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.
Not X, but When: Diagnosing a Mandarin LLM Writing Tic through Contextual Appropriateness. The story centres on Fine-tuning. Reported by arXiv. Bharat Hunt files it under Open Source AI and AI Models — the section covering open-weight models, permissive licences and community releases.
CoTrace: Data Recipes for Training Terminal Agents with Harness-Model Co-Evolution. It centres on Fine-tuning, and also names Benchmarks. 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.
MoLE: Mixture of Latent Experts for Complementary Visual Reasoning. It centres on Benchmarks, and also names Fine-tuning. Reported by arXiv. Bharat Hunt files it under Computer Vision — the section covering image and video understanding, detection, recognition, multimodal perception.
EviRover: Reinforcing Agentic Perception Beyond a Glance. It centres on Benchmarks, and also names Fine-tuning and Multimodal. 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.
Learning Native Reflection in Unified Models with Interleaved Reinforcement Learning. It centres on Multimodal, and also names Fine-tuning and Inference. Reported by arXiv. Bharat Hunt files it under AI Models and Generative AI — the section covering a new or updated model, its capabilities, benchmarks or availability.
Where Should a Document Live: Context, Representations, or Parameters? It centres on Fine-tuning, and also names Benchmarks. Reported by arXiv. Bharat Hunt files it under AI Models — the section covering a new or updated model, its capabilities, benchmarks or availability.
TEMPO: Time Series Understanding via Discrete Tokenization. The story centres on Fine-tuning. Reported by arXiv. Bharat Hunt files it under AI Funding and AI Coding — the section covering rounds, valuations, acquisitions and investor moves in AI. Open the original report for the full detail.
Preprocessing Choices in Fine-Tuning Data Matter: Utility and Demographic Sensitivity in LLM-Based Recommendation. The story centres on Fine-tuning. Reported by arXiv. Bharat Hunt files it under AI Funding and AI Research — the section covering rounds, valuations, acquisitions and investor moves in AI.
Aligning Large Language Models for Safety in African Languages: A Case Study of Hausa. It centres on AI Safety, 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.
When Copying Is Hard: Copy-Constrained Decoding for Exact Span Reproduction. It centres on Benchmarks, and also names Fine-tuning and GitHub. 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.
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.