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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710 stories
ActKV: Efficient LLM Agents through Action-Guided KV Cache Management. The story centres on Inference. Reported by arXiv. Bharat Hunt files it under AI Agents — the section covering autonomous and tool-using systems, agent frameworks, MCP and orchestration. Open the original report for the full detail.
RAMP: Robust Adaptive Mixed-Precision Quantization for Edge CPU Vision 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.
On the Lexical Superstition of Large Language Models for Code Comprehension: Re-evaluation on Code of Low Lexical Quality. It centres on Benchmarks, and also names Fine-tuning and Inference. Reported by arXiv. Bharat Hunt files it under AI Coding and AI Models — the section covering code generation, developer agents, IDEs and software engineering.
Transformers as In-Context Samplers: From Closed-Form Diffusion to Estimation-Free Sampling. The story centres on Inference. 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.
StepBack: Step-Level Error-Localized Resampling for Efficient Test-Time Reasoning. It centres on Reasoning Models, and also names Benchmarks and Inference. Reported by arXiv. Bharat Hunt files it under AI Research — the section covering papers, benchmarks, evaluations, interpretability and safety results.
ACCT: Asymmetric Cross-LLM Context Transfer. It centres on Inference, 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.
When Does Test-Time Search Help World Action Models? An Empirical Audit of Candidate Selection and Simulator Integrity. It centres on Inference, and also names 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.
Write your own training loop — without managing GPUs — with Amazon SageMaker training session APIs. The story centres on Amazon. Reported by AWS. Bharat Hunt files it under AI Hardware and AI Coding — the section covering chips, accelerators, data centres, on-device inference and supply.
Tractable Inference with Kolmogorov-Arnold Networks. It centres on Inference, 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.
Adaptive Replay Refinement: Allocating Test-Time Compute Between Revision and Resampling. It centres on Inference, and also names Benchmarks and GitHub. Reported by arXiv. Bharat Hunt files it under AI Funding — the section covering rounds, valuations, acquisitions and investor moves in AI.
Efficient Reasoning Training Does Not Always Harm CoT Faithfulness and Monitorability. It centres on Reasoning Models, and also names Inference. Reported by arXiv. Bharat Hunt files it under AI Models — the section covering a new or updated model, its capabilities, benchmarks or availability.
Metropolis-Hastings Dominates Importance Resampling for Policy Composition. The story centres on Inference. Reported by arXiv. Bharat Hunt files it under AI Regulation — the section covering law, policy, courts, standards and government positions on AI. 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
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.