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
Knowing When Not to Answer: Cross-Domain and Multi-Turn Generalization of Latent Underspecification Signals. 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.
Learning to Stop without Learning to Stop: Self-Supervised Confidence Training Improves Reasoning Efficiency. It centres on Reasoning Models, and also names Inference and 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.
Improving Visual Sensitivity of LLMs on Multimodal Machine Translation with Metric-based Loss Weighting. It centres on Multimodal, and also names Fine-tuning. 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.
Adaptive Uncertainty-Aware Modeling and Stochastic Radial Basis Function Predictive Control for Personalized Fluid Resuscitation. The story centres on Fine-tuning. 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.
Not All Dimensions Are Equal: Outlier-Aware Attention Linearization with Grouped Quadratic Kernels. It centres on Inference, and also names Benchmarks and 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.
DeepEdu-v1: Efficient and Scalable Agentic LLMs for Vietnamese Education. It centres on GPT, and also names Inference and Fine-tuning. Reported by arXiv. Bharat Hunt files it under AI Agents and AI Hardware, and tracks it as India coverage — the section covering autonomous and tool-using systems, agent frameworks, MCP and orchestration.
Evaluating In-Context Learning and Retrieval Strategies for Devanagari Post-OCR Correction. It centres on Benchmarks, and also names Gemma and Fine-tuning. Reported by arXiv. Bharat Hunt files it under Computer Vision and AI Research — the section covering image and video understanding, detection, recognition, multimodal perception.
SkillGym: Internalizing Human Skills into LLMs for Real-World Problem Solving. It centres on Inference, and also names Fine-tuning and Claude Code. 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.
Automated Regulatory Compliance Question Answering in Financial Services with Domain-Adapted Retrieval-Augmented Generation. It centres on RAG, and also names Fine-tuning and Benchmarks. 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.
Contrastive Adapter Training and Consensus Knowledge Distillation for Multi-Source-Free Domain Adaptation in Skin Cancer Diagnosis. It centres on Fine-tuning, and also names Reliance Jio and GitHub. Reported by arXiv. Bharat Hunt files it under AI Regulation, and tracks it as India coverage — the section covering law, policy, courts, standards and government positions on AI.
BYOD: Build Your Own DLM - Efficient Autoregressive-to-Diffusion Language Model Conversion. It centres on Fine-tuning, and also names Gemma and Llama. 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.
Benchmarking and Domain Adaptation of Automatic Speech Recognition (ASR) for Adolescent Health Communication in Ghanaian Languages. It centres on Benchmarks, and also names Multimodal and Gemma. 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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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.