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
Pretraining Knows What, Simulation Teaches How: Compositional Generalization in a Sim-Trained Robot Agent. It centres on Fine-tuning, and also names GPT. Reported by arXiv. Bharat Hunt files it under Robotics and AI Models — the section covering embodied AI, humanoids, autonomous vehicles and drones.
Agent Error Dataset: Scaling 50,000 Error--Diagnosis Pairs for Failure Analysis and Error-Aware Post-Training. 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.
BrainWideBench: Benchmarking large-scale pretraining and across-animal transfer in multi-region neural recordings. It centres on Benchmarks, and also names Fine-tuning and AI Safety. Reported by arXiv. Bharat Hunt files it under AI Research — the section covering papers, benchmarks, evaluations, interpretability and safety results.
Would an LLM Match into a Residency? Uncertainty, Reasoning and Image Use of Open Language Models on the Spanish MIR Examination. 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.
Temporal Evidence-Parameter Co-Alignment for LLM Reasoning over Temporal Knowledge Graphs. It centres on AI Safety, and also names RAG and Fine-tuning. Reported by arXiv. Bharat Hunt files it under AI Research — the section covering papers, benchmarks, evaluations, interpretability and safety results.
Weight-space programming for auditing clinical decision networks. It centres on Claude, and also names Fine-tuning. Reported by arXiv. Bharat Hunt files it under AI Coding and AI Models — the section covering code generation, developer agents, IDEs and software engineering.
Pretraining Data Scale Reverses the Muon-AdamW Fine-Tuning Gap. 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.
Cross-Domain Pretraining for Steady-State Neural CFD Surrogates. The story centres on 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.
Task-Oriented Rank Adaptation for Continual Learning in Text Classification. It centres on Fine-tuning, and also names Benchmarks and Reliance Jio. 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.
Latent Reasoning in Continuous Space for Unified Multimodal Models. It centres on Multimodal, and also names Reasoning Models and Fine-tuning. 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.
EASE: Entropy-Adaptive Distribution Shaping for Evading AI-generated Text Detectors. It centres on Fine-tuning, and also names Inference. Reported by arXiv. Bharat Hunt files it under AI Models — the section covering a new or updated model, its capabilities, benchmarks or availability.
Build an AI-powered product tagging system with Amazon SageMaker serverless model customization. It centres on Amazon, and also names Fine-tuning and Inference. Reported by AWS. Bharat Hunt files it under AI Models — the section covering a new or updated model, its capabilities, benchmarks or availability.
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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.