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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CardioVascBench:Prioritized Diagnosis Generation from Real-World Chinese Cardiovascular Records. It centres on Benchmarks, 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.
DR Pixel: Page-Native Deep Research with Vision-Language Models. It centres on Multimodal, and also names Benchmarks 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.
Capable Teacher, Myopic Scorer: Reward-Free Teacher Repair for Agentic On-Policy Distillation. It centres on Fine-tuning, and also names GitHub. 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.
Topology-Inspired Fine-Tuning for Wind-Field Super-Resolution. It centres on Fine-tuning, and also names Benchmarks. 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.
Beyond Compression: Training Latent Representations for Stable Long-Horizon Rollout in Neural Surrogate Solvers. 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.
ActReview: Rebuttal-Guided Training Data and Rubric Rewards for Actionable Peer Review Generation. It centres on Fine-tuning, and also names Benchmarks. Reported by arXiv. Bharat Hunt files it under AI Research — the section covering papers, benchmarks, evaluations, interpretability and safety results.
Hierarchical Residual Policy Learning for Real-World Mobile Manipulation with Sparse Human Guidance. It centres on Fine-tuning, and also names AI Safety and GitHub. Reported by arXiv. Bharat Hunt files it under AI Regulation and AI Research — the section covering law, policy, courts, standards and government positions on AI.
SOTA: Stock Options Trading Agents Guided by Option-Implied Return Distributions. 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.
A foundation model for energy and radiation systems built on heterogeneous scientific interfaces. It centres on Inference, and also names Fine-tuning. 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.
On Trajectory-Aware Training for Masked Diffusion Language Models. It centres on Inference, and also names AI Safety and Fine-tuning. Reported by arXiv. Bharat Hunt files it under Generative AI and AI Research — the section covering text, image, audio and video generation — products and their output.
TransBERT: A Framework for Synthetic Translation in Domain-Specific Language Modeling. The story centres on Fine-tuning. 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.
Suppressing Misaligned Behaviors in Language Models, Durably. It centres on Fine-tuning, and also names AI Safety and GPT. Reported by arXiv. Bharat Hunt files it under AI Research and AI Models — the section covering papers, benchmarks, evaluations, interpretability and safety results.
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
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.