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
Predictable performance is suboptimal, and what it means for model routing. 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.
Game of Phones: Harnessing Game Theory and LLMs in 'Bot Wars' to Counteract Phone Scams. It centres on Fine-tuning, and also names Benchmarks and AI Safety. Reported by arXiv. Bharat Hunt files it under AI Research and AI Coding — the section covering papers, benchmarks, evaluations, interpretability and safety results.
AnchorReasoning: A Visual Grounding and Causal Reasoning Dataset in Long-Tail Autonomous Driving Scenarios. It centres on Reasoning Models, and also names Fine-tuning and Inference. Reported by arXiv. Bharat Hunt files it under Robotics and Computer Vision — the section covering embodied AI, humanoids, autonomous vehicles and drones.
RetroThinker: Enabling Retrospective Thinking in Speech LLMs. It centres on Reasoning Models, and also names Inference and 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.
Co-Evolving Harnesses and Models: On-Policy Correction Helps Weaker Models Catch Up Where Imitation Fails. It centres on Fine-tuning, and also names Gemma and Meta. 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.
Stepwise-Querying: Distilling Reasoning without Access to Teacher Chain-of-Thought. It centres on Reasoning Models, and also names Fine-tuning and Benchmarks. Reported by arXiv. Bharat Hunt files it under AI Coding — the section covering code generation, developer agents, IDEs and software engineering.
Precision--Recall Trade-off on the Inference Level for LLMs in Grammatical Error Correction. It centres on Inference, and also names Fine-tuning 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.
Preserve to Expand: Source-Aware Adaptation and Model Merging for Multilingual LLM-Based ASR. The story centres on 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.
CompOrca: Corpus-Scale Compliance Labelling of Instruction-Tuning Data. It centres on Fine-tuning, and also names Benchmarks and Hugging Face. Reported by arXiv. Bharat Hunt files it under AI Regulation and Open Source AI — the section covering law, policy, courts, standards and government positions on AI.
Training-Free Task Vectors for LLM Behavioral Control. It centres on Reliance Jio, and also names Fine-tuning and GitHub. Reported by arXiv. Bharat Hunt files it under AI Models, and tracks it as India coverage — the section covering a new or updated model, its capabilities, benchmarks or availability.
RATED: Reliability-Aware AI Agent Tool Retrieval via Execution Dynamics. It centres on Fine-tuning, and also names Benchmarks and Inference. 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.
A Zeroth-Order Paradigm for LLM Preference Alignment. It centres on AI Safety, and also names Fine-tuning and Mistral AI. Reported by arXiv. Bharat Hunt files it under AI Research and AI Models — the section covering papers, benchmarks, evaluations, interpretability and safety results.
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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.