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.
Updated 1 hour ago118 stories in the last 24 hours
68 stories
Cola2: Co-Adaptive Hierarchical Language Modeling with Continuous Semantic Compression and Balanced Discrete Generation. It centres on Inference, and also names Llama. Reported by arXiv. Bharat Hunt files it under Generative AI — the section covering text, image, audio and video generation — products and their output.
FuTransformer: FP8 prefill and INT4 decode for Llama-style inference on Hopper. It centres on Llama, and also names Inference and NVIDIA. Reported by arXiv. Bharat Hunt files it under AI Models and AI Hardware — the section covering a new or updated model, its capabilities, benchmarks or availability.
Beyond Linear Concepts: Discovering and Aligning Non-Linear Concept Manifolds in Large Language Models. It centres on AI Safety, and also names Qwen and Llama. Reported by arXiv. Bharat Hunt files it under AI Research and AI Models — the section covering papers, benchmarks, evaluations, interpretability and safety results.
When Do LLMs Help with Feature Selection? A Systematic Empirical Study. It centres on Benchmarks, and also names Llama and Qwen. Reported by arXiv. Bharat Hunt files it under AI Research and AI Models — the section covering papers, benchmarks, evaluations, interpretability and safety results.
Input-Blind Controls Produce Substantial Oracle Headroom for Layer Programs in Multiple-Choice Evaluation. It centres on Benchmarks, and also names Inference and Llama. Reported by arXiv. Bharat Hunt files it under AI Research and AI Models — the section covering papers, benchmarks, evaluations, interpretability and safety results.
Enhancing Accessibility of Medical Texts through Large Language Model-Driven Plain Language Adaptation. It centres on GPT, 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.
Nobody Truly Agrees on Sentiment: Humans, Bespoke Tools, and LLMs Struggle with Social Media Texts. It centres on GPT, and also names Llama and AI Safety. 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.
Follow the Gradient: Tracing Safety Degradation Back to Training Data. It centres on Fine-tuning, and also names Qwen and Llama. 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.
Aligning Language Models with General Preferences: An Empirical Study of Direct Nash Optimization. It centres on Llama, and also names Mistral AI 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.
Evaluating LLMs as Judges of Counterspeech Quality Against Online Hate Speech. It centres on Reasoning Models, and also names Qwen and Benchmarks. Reported by arXiv. Bharat Hunt files it under AI Research and AI Models — the section covering papers, benchmarks, evaluations, interpretability and safety results.
Measuring the Serving Stack Instead of the Model: Hidden Confounds in Local Tool-Use Evaluation. It centres on Benchmarks, and also names Ollama and Phi. Reported by arXiv. Bharat Hunt files it under AI Tools and AI Models — the section covering applications people use — assistants, editors, search, productivity.
An Experimental Study of Hybrid Retrieval, NLI and Open-source LLMs for Scientific Claim Verification. It centres on Inference, and also names Benchmarks 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.
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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%).
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