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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3337 stories
Using Machine Learning to Measure Self-Efficacy Belief Through Learners' Interaction Traces in Technology-Enhanced Learning. 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.
Learning Nonlocal Interactions in Large-Scale Heterogeneous Metamaterials through Scientific Machine Learning. Reported by arXiv. Bharat Hunt files it under AI Research and AI Tools — the section covering papers, benchmarks, evaluations, interpretability and safety results. Open the original report for the full detail.
Don't Lose the Thread: Empowering Long-Horizon LLM Agents with Cognitive Resource Self-Allocation. It centres on Benchmarks, and also names Inference. Reported by arXiv. Bharat Hunt files it under AI Models and AI Agents — the section covering a new or updated model, its capabilities, benchmarks or availability.
Evaluating DeepSeek V4.1 Flash-1017 for loss of control and harmful manipulation risks. It centres on DeepSeek, and also names Benchmarks and GPT. Reported by arXiv. Bharat Hunt files it under AI Models and Open Source AI — the section covering a new or updated model, its capabilities, benchmarks or availability.
Accordion: Elastic-Depth Inference via Fixed-Prefix Layer Looping. It centres on Inference, and also names Fine-tuning. 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.
Towards English - Mizo Legal Machine Translation: Corpus Creation, Analysis, and Benchmarks. The story centres on Benchmarks. Reported by arXiv. Bharat Hunt files it under AI in India and AI Regulation, and tracks it as India coverage — the section covering india's AI ecosystem — government programmes, institutions, national models.
MAPS: Macro-Adaptive Parallel Reasoning for Regulating Search Momentum in Retrieval-Augmented Generation. It centres on RAG, and also names Benchmarks and GitHub. Reported by arXiv. Bharat Hunt files it under AI Models — the section covering a new or updated model, its capabilities, benchmarks or availability.
OpenVLHarness: Real World Visual Agentic Tool-Use with Multimodal Memory. It centres on Multimodal, 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.
Beyond Large Language Models: A Mental World Model Framework for Mental Modeling, Simulation, Evolution, and Interpretability. It centres on Inference, and also names AGI. Reported by arXiv. Bharat Hunt files it under AI Research and AI Models — the section covering papers, benchmarks, evaluations, interpretability and safety results.
Scalable long-range machine learning interatomic potentials with electrical response for atomistic material modeling. It centres on Benchmarks, and also names Inference. Reported by arXiv. Bharat Hunt files it under AI Hardware — the section covering chips, accelerators, data centres, on-device inference and supply.
Beyond Answer Voting: Fine-Grained Correction of LLM Reasoning through Graph Alignment across Sampled Traces. It centres on AI Safety, and also names Benchmarks. Reported by arXiv. Bharat Hunt files it under AI Research — the section covering papers, benchmarks, evaluations, interpretability and safety results.
Prior or Feedback? What an LLM Uses When Adapting Neural Operators. 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.
Bharat Hunt links to original reporting and does not republish it. Headlines and links belong to their publishers; summaries and the trend ranking are ours. All AI stories
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.