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
Show-Harness: Just a VLM Agent Can Play Robots. The story centres on Fine-tuning. Reported by arXiv. Bharat Hunt files it under Robotics and Open Source AI — the section covering embodied AI, humanoids, autonomous vehicles and drones. Open the original report for the full detail.
GPT-6-Astra Understands Touch and Outperform Vision--Tactile--Language--Action Models. It centres on GPT, and also names Multimodal and Fine-tuning. Reported by arXiv. Bharat Hunt files it under AI Models and Robotics — the section covering a new or updated model, its capabilities, benchmarks or availability.
TokenGate: Adapting LLMs for Fast and Memory-Efficient Prefill with Learned Token Pruning. It centres on Fine-tuning, and also names GitHub. 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.
Revision-Based Learning from Interactive Experience and Feedback. The story centres on 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. Open the original report for the full detail.
BetaMem: Learning to Remember a Changing World for Embodied Agents. It centres on Fine-tuning, and also names Benchmarks. Reported by arXiv. Bharat Hunt files it under Robotics and Open Source AI — the section covering embodied AI, humanoids, autonomous vehicles and drones.
Multi-SFT: Permissively-Licensed Multilingual Instruction-Following. It centres on Fine-tuning, and also names Benchmarks. Reported by arXiv. Bharat Hunt files it under AI Models — the section covering a new or updated model, its capabilities, benchmarks or availability. Open the original report for the full detail.
Dream to Remember: Continual Steering of Generalist Robot Policies with World Models. The story centres on Fine-tuning. Reported by arXiv. Bharat Hunt files it under Robotics and AI Models — the section covering embodied AI, humanoids, autonomous vehicles and drones.
How uniopen customized Amazon Nova to their retail moderation policies for production deployment. It centres on Amazon, and also names Nova and Fine-tuning. Reported by AWS. Bharat Hunt files it under Enterprise AI — the section covering deployment inside businesses — platforms, adoption, cost, integration.
Transcribe, Translate, and Optimize: Joint Reward Learning for Speech Translation. It centres on Reasoning Models, and also names Fine-tuning and Inference. Reported by arXiv. Bharat Hunt files it under AI Regulation — the section covering law, policy, courts, standards and government positions on AI.
General-purpose foundation models surpass specialized ones on chemical property prediction tasks. It centres on Fine-tuning, and also names GPT and Claude. Reported by arXiv. Bharat Hunt files it under AI Models — the section covering a new or updated model, its capabilities, benchmarks or availability.
Heads of Security: Learned Attention Gates Improve Prompt Injection Robustness. It centres on Llama, and also names GPT and Fine-tuning. 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.
The Effectiveness and Durability of Pretraining Data Filtering in Protein Language Models. The story centres on Fine-tuning. 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
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.