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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64 stories
Reduce LLM latency with prefix-aware routing on Amazon SageMaker Inference. It centres on Amazon, and also names Inference and Benchmarks. Reported by AWS. Bharat Hunt files it under AI Funding and AI Models — the section covering rounds, valuations, acquisitions and investor moves in AI.
Strength Controls Change How Interventions Compare Across Sparse Autoencoder Codes. It centres on Gemma, and also names Llama. Reported by arXiv. Bharat Hunt files it under AI Models — the section covering a new or updated model, its capabilities, benchmarks or availability.
GrammarRL: Effective Grammar-Constrained Decoding via Reinforcement Learning. It centres on Llama, 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.
Rare, Relevant, and Still Wrong: How Memory Fails LLM Group Decisions. It centres on Benchmarks, and also names Llama and DeepSeek. 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.
Risk-Controlled KV-Cache Eviction: From Memory Budgets to Risk Targets. It centres on Inference, and also names Llama and Mistral AI. 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.
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.
Do "True" and "False" Add Up to One? Complement Consistency in LLM-Agent Opinion Simulations. It centres on Llama, and also names Gemma and Qwen. Reported by arXiv. Bharat Hunt files it under AI Models — the section covering a new or updated model, its capabilities, benchmarks or availability.
DualQuant: Dual-scale Calibration-free Weight-and-Activation Quantization for LLMs. It centres on Inference, and also names Llama and Qwen. Reported by arXiv. Bharat Hunt files it under AI Models — the section covering a new or updated model, its capabilities, benchmarks or availability.
Document-Level Text Simplification in Estonian Using Large Language Models. It centres on Benchmarks, and also names Gemini and Llama. 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.
Training Small Language Models for Transferable Tool Selection. It centres on Claude, and also names GPT and Llama. Reported by arXiv. Bharat Hunt files it under AI Models and AI Tools — the section covering a new or updated model, its capabilities, benchmarks or availability.
Forty Questions: Identifying a Language Model from One-Word Answers. It centres on Gemma, and also names Llama and Grok. Reported by arXiv. Bharat Hunt files it under AI Models — the section covering a new or updated model, its capabilities, benchmarks or availability.
From Parameters to Answers: How LLMs Retrieve and Use Their Internal Knowledge. It centres on Qwen, and also names Llama and Gemma. 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.
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.