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
LoGRA: Scaling LLM Reinforcement Learning with Low-Rank Gradient Sketches. The story centres on GitHub. Reported by arXiv. Bharat Hunt files it under AI Coding and AI Hardware — the section covering code generation, developer agents, IDEs and software engineering.
Pivot-SD: Efficient Self-Distillation for Masked Diffusion Language Models. The story centres on Benchmarks. Reported by arXiv. Bharat Hunt files it under Generative AI and AI Startups — the section covering text, image, audio and video generation — products and their output.
HARISSA: Inference-Time Self-Checks for Efficient and Safe Local Language Model Deployment. It centres on Inference, and also names Reasoning Models. Reported by arXiv. Bharat Hunt files it under Enterprise AI and AI Models — the section covering deployment inside businesses — platforms, adoption, cost, integration.
UniAR: A Unified Framework for Autism Recognition Enhanced by Multi-View Prompt Learning. It centres on Multimodal, and also names AI Safety and Benchmarks. 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.
Controlled Attribute-Specific Summarization of Interrogative Dialogues. It centres on Reasoning Models, and also names Benchmarks and AI Safety. Reported by arXiv. Bharat Hunt files it under AI Research — the section covering papers, benchmarks, evaluations, interpretability and safety results.
Answer-Distribution Trajectories: A Stochastic-Dynamics View of LLM Reasoning. It centres on Reasoning Models, and also names Benchmarks and Inference. Reported by arXiv. Bharat Hunt files it under Open Source AI and AI Models — the section covering open-weight models, permissive licences and community releases.
DR Pixel: Page-Native Deep Research with Vision-Language Models. It centres on Multimodal, and also names Benchmarks and 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.
Linear Probes Transfer from Base to Distilled Language Models despite Rotation. It centres on Llama, and also names DeepSeek and Reasoning Models. 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.
The Long Road to the Same Answer: Cognitive Bias Under Escalating Reasoning Budgets in Large Language Models. It centres on Reasoning Models, and also names Inference and Benchmarks. 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.
Can LLMs Reason About Runtime Behavior? A Repository-Level Dynamic Benchmark. It centres on Benchmarks, and also names Flux. Reported by arXiv. Bharat Hunt files it under AI Coding and AI Research — the section covering code generation, developer agents, IDEs and software engineering.
Measuring LLM Sycophancy under Sustained Multi-Turn Pressure. 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. Open the original report for the full detail.
J-Comm: Jacobian-Guided Latent Communication between LLM Agents. 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. 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.