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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387 stories
Beyond Token Scale: Chunk-Level Sparse Autoencoders for Reliable Semantic Feature Discovery. Reported by arXiv. Bharat Hunt files it under AI Tools and AI Research — the section covering applications people use — assistants, editors, search, productivity. Open the original report for the full detail.
Evolve on Graph: Self-Evolving Knowledge Graph Reasoning without Question Annotations. It centres on Benchmarks, and also names GitHub. Reported by arXiv. Bharat Hunt files it under AI Models — the section covering a new or updated model, its capabilities, benchmarks or availability.
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.
MedPrune: Topology-Efficient Multimodal Multi-Agent Communication Evolution for Medical VQA Tasks. The story centres on Multimodal. Reported by arXiv. Bharat Hunt files it under AI Agents and AI Models — the section covering autonomous and tool-using systems, agent frameworks, MCP and orchestration.
Combining Hierarchical Cognitive Process with Process Supervision for Interpretable Scene Safety Understanding. The story centres on Benchmarks. Reported by arXiv. Bharat Hunt files it under AI Research and AI Models — the section covering papers, benchmarks, evaluations, interpretability and safety results.
From Symbolic Perception to Logical Deduction: A Framework for Guiding Language Models in Geometric Reasoning. It centres on Multimodal, and also names Benchmarks and Gemini. 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.
MILO: Efficient Many-shot In-Context Learning with Block-wise Low-rank Compression. It centres on Inference, and also names Benchmarks. Reported by arXiv. Bharat Hunt files it under AI Hardware and Enterprise AI — the section covering chips, accelerators, data centres, on-device inference and supply.
TaoD2C-Bench: Benchmarking MLLMs for Industrial UI Code Generation Beyond Visual Fidelity. It centres on Multimodal, and also names Benchmarks and AI Safety. Reported by arXiv. Bharat Hunt files it under AI Coding and AI Research — the section covering code generation, developer agents, IDEs and software engineering.
When Should LLMs Abstain? Chain-of-Self-Questioning for Selective Risk Control. It centres on Reasoning Models, and also names Benchmarks. Reported by arXiv. Bharat Hunt files it under AI Research and Open Source AI — the section covering papers, benchmarks, evaluations, interpretability and safety results.
Hindsight-Guided Rationale Distillation for Rare Disease Diagnosis. It centres on Reasoning Models, and also names Inference. 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.
Evaluating Open-Weight LLMs for Turkish Domain Documents Under Retrieval and Hardware Constraints. It centres on Benchmarks, and also names NVIDIA. Reported by arXiv. Bharat Hunt files it under Open Source AI and AI Research — the section covering open-weight models, permissive licences and community releases.
Semantic Abstraction for Natural Language Inference: a Methodological Framework for Discovering and Compensating Semantic Knowledge and Reasoning Gaps in Large Language Models. The story centres on Inference. 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.
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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.