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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An Interpretable Memory Decision Controller for LLM Agents Based on Three-Signal Complementarity: Decoupling Confidence and Consistency. It centres on RAG, and also names Meta and Benchmarks. Reported by arXiv. Bharat Hunt files it under Open Source AI — the section covering open-weight models, permissive licences and community releases.
Route First, See Later: Enabling Vision Agents to Reason Scientifically by Separating Routing Policy from Action Spaces. It centres on Benchmarks, and also names Qwen. Reported by arXiv. Bharat Hunt files it under AI Research and AI Regulation — the section covering papers, benchmarks, evaluations, interpretability and safety results.
From Self-Confidence to Statistical Accuracy: Scaling Human-in-the-Loop Evaluation with Direct and LLM-Assisted Auditing. It centres on Benchmarks, and also names Multimodal and Inference. Reported by arXiv. Bharat Hunt files it under AI Research and AI Models — the section covering papers, benchmarks, evaluations, interpretability and safety results.
MiRL: Error-Targeted Reinforcement Learning for Correcting Terminal Agent Misbehaviors. Reported by arXiv. Bharat Hunt files it under AI Research and AI Regulation — the section covering papers, benchmarks, evaluations, interpretability and safety results. Open the original report for the full detail.
Towards a Unified Framework of Optimal Input-Side Dimensional Configuration for Deep CTR Prediction. The story centres on Benchmarks. Reported by arXiv. Bharat Hunt files it under AI Models — the section covering a new or updated model, its capabilities, benchmarks or availability.
Not One Belief: Answer Payloads, Persistence, and Dissociated Confidence Signals in Conversational Question Answering. The story centres on Llama. Reported by arXiv. Bharat Hunt files it under AI Models — the section covering a new or updated model, its capabilities, benchmarks or availability.
EvoRouter: Cost-Efficient Multi-Agent Routing via Insight-Guided On-Policy Distillation. It centres on Benchmarks, and also names Inference and GitHub. 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.
TrySee: Modular Perception-Enhanced VLM Grounding for Closed-Loop Robot Task Planning in Specialized Domains. The story centres on Fine-tuning. Reported by arXiv. Bharat Hunt files it under Robotics — the section covering embodied AI, humanoids, autonomous vehicles and drones.
Adapting LLM-SC to Living Sacred Heritage: Domain-Specific Extraction, Semantic Networks and Human Evaluation on Pashupatinath Temple Reviews. It centres on Benchmarks, and also names GPT. 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.
OmniShowcase: Omnidirectional Subject-Consistent Video Generation from Multi-View Images. It centres on AI Safety, and also names Benchmarks and GitHub. Reported by arXiv. Bharat Hunt files it under Generative AI and AI Research — the section covering text, image, audio and video generation — products and their output.
CounterFlow: Target-Conditioned Flow Matching for Visual Counterfactuals. The story centres on Benchmarks. Reported by arXiv. Bharat Hunt files it under Computer Vision and Generative AI — the section covering image and video understanding, detection, recognition, multimodal perception. Open the original report for the full detail.
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.
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.