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
SEMA-Auto: Sighted and Efficient Multi-Turn Attack with Autonomy. It centres on Fine-tuning, and also names DeepSeek and GPT. Reported by arXiv. Bharat Hunt files it under AI Models — the section covering a new or updated model, its capabilities, benchmarks or availability.
TrySee: Modular Perception-Enhanced VLM Grounding for Closed-Loop Robot Task Planning in Specialized Domains. The story centres on Fine-tuning. Reported by arXiv. Bharat Hunt files it under Robotics — the section covering embodied AI, humanoids, autonomous vehicles and drones.
Natural Language to First-Order Logic LLM-based Autoformalization. It centres on Benchmarks, and also names Fine-tuning. Reported by arXiv. Bharat Hunt files it under AI Research — the section covering papers, benchmarks, evaluations, interpretability and safety results. Open the original report for the full detail.
Generative modeling of intrinsically disordered protein regions by reinforcing sparse autoencoder features. It centres on Fine-tuning, and also names GitHub. Reported by arXiv. Bharat Hunt files it under AI Regulation and AI Models — the section covering law, policy, courts, standards and government positions on AI.
Learning to Ideate for Scientific Impact. 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.
Benchmarking the Explanatory Quality of Open-Weight Vision-Language Models in Face Recognition. It centres on Benchmarks, and also names Reliance Jio and AI Safety. Reported by arXiv. Bharat Hunt files it under Open Source AI and AI Research, and tracks it as India coverage — the section covering open-weight models, permissive licences and community releases.
Decoupling Patient Personas and Clinical Workflows: A Dual-Agent Approach to Realistic Medical Dialogue Generation. It centres on AI Safety, and also names Fine-tuning. Reported by arXiv. Bharat Hunt files it under AI Regulation and AI Agents — the section covering law, policy, courts, standards and government positions on AI.
Cycle-of-Thought: Self-Evaluative Iterative Reasoning for Table Visual Question Answering in Document Images. It centres on Fine-tuning, and also names Inference and Benchmarks. Reported by arXiv. Bharat Hunt files it under AI Models and Computer Vision — the section covering a new or updated model, its capabilities, benchmarks or availability.
SLDR: Defending Against Malicious Fine-tuning via Selective Layers Recovery and Dynamic Routing. It centres on Fine-tuning, and also names Inference and Benchmarks. Reported by arXiv. Bharat Hunt files it under AI Coding and AI Models — the section covering code generation, developer agents, IDEs and software engineering.
RECAST: Learning to Compute the Right Context through Adaptive Evidence Routing. It centres on RAG, and also names Fine-tuning and Benchmarks. Reported by arXiv. Bharat Hunt files it under AI Agents and AI Tools — the section covering autonomous and tool-using systems, agent frameworks, MCP and orchestration.
Cross-Scale Transfer Learning for Depression Severity Prediction: From PHQ-8 to HAMD-17 Across Languages and Clinical Paradigms. 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.
From Pretraining to Proficiency: Real-World Subtask RL for Long-Horizon Manipulation with Minimal Human Intervention. The story centres on Fine-tuning. Reported by arXiv. Bharat Hunt files it under Robotics and AI Regulation — the section covering embodied AI, humanoids, autonomous vehicles and drones.
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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.