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Large language models

Model releases, architecture work and prompting research on large language models — from frontier-lab announcements to the arXiv papers behind them.

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arXiv Machine Learning
Sep 17

Robust Ultra Low-Bit Post-Training Quantization via Stable Diagonal Curvature Estimate

The paper introduces DASH-Q, a post‑training quantization method that uses a diagonal Hessian approximation and iterative weighted least squares to reduce noise in curvature estimates. By discarding noisy cross‑channel dependencies, DASH‑Q preserves salient feature power and achieves superior performance in ultra low‑bit quantization. Across five large language models, it improves zero‑shot accuracy by an average of 7.01% and up to 14.01% over the strongest baselines, even with very small calibration datasets.

By Jaemin Kim, Sungkyun Kim, Junyeol Lee, Jiwon Seo
llmsefficiency
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arXiv Computation and Language
Sep 17

Correlation-Guided Encoder Selection for Multi-Encoder Large Audio-Language Models

The paper introduces CUES, a lightweight heuristic for selecting encoder combinations in large audio‑language models by estimating complementarity through Pearson correlations of single‑encoder performance profiles. Using a frozen SmolLM2‑135M backbone, CUES consistently identifies optimal encoder sets for each track on the XARES‑LLM benchmark without requiring fusion training or test data. On broad audio tasks, CUES selects a diverse trio of encoders, improving performance by 4.3% over Whisper‑medium, while on text generation it opts for a focused speech‑only pair, outperforming mHuBERT‑147 by 6.3%. The results illustrate how correlation signals guide a diversity–interference trade‑off across different task families.

By Pei-Jun Liao, Hung-Shin Lee, Wenze Ren, Kuo-Hsuan Hung, Hung-yi Lee, Hsin-Min Wang
llmsfine-tuningmultimodalbenchmarks
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arXiv Computer Vision
Sep 17

PhysVGGT: Feed-Forward Dense Physical Property Estimation from A Single Image

PhysVGGT is a feed‑forward model that predicts dense maps of friction coefficient, Shore hardness, Young's modulus, and density, along with object‑level mass, from a single RGB image in one forward pass. It treats physical property estimation as a dense per‑pixel prediction problem, using a visual geometry transformer to extract geometry‑aware tokens and separate dense and global prediction branches. A scalable pseudo‑label generation pipeline enables large‑scale weakly supervised training, and the model achieves state‑of‑the‑art performance on the ABO‑500 dataset while running 27× faster than previous methods.

By Sneha Paul, Guile Wu, Bingbing Liu, Dongfeng Bai
llmsroboticsefficiencymultimodalbenchmarks
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arXiv Computer Vision
Sep 17

Think Before You Move: Latent Motion Reasoning for Text-to-Motion Generation

The paper introduces Latent Motion Reasoning (LMR), a two‑stage approach that separates text‑to‑motion generation into a planning phase and an execution phase. LMR uses a Dual‑Granularity Tokenizer to create a compressed, semantically rich reasoning latent for global trajectory planning and a high‑frequency execution latent for detailed motion fidelity. Experiments on T2M‑GPT and MotionStreamer show that this architecture improves both semantic alignment and physical plausibility compared to direct translation methods.

By Yijie Qian, Juncheng Wang, Yuxiang Feng, Chao Xu, Wang Lu, Yang Liu, Baigui Sun, Yiqiang Chen, Yong Liu, Shujun Wang
llmsnlpbenchmarkssafety
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arXiv Computation and Language
Sep 17

Large Language Models Versus Physicians in Traditional Chinese Medicine: A Real-World Clinical Case Evaluation

arXiv:2609.17544v1 Announce Type: new Abstract: Large language models (LLMs) are increasingly being explored for clinical applications, yet their assessment for real-world traditional Chinese medicin...

By Jiacheng Xie, Xiaoting Tang, Yang Yu, Jinpu Li, Shouli Li, Congcong Jing, Yantao Yang, Zhiyong Zhao, Ziyang Zhang, Qilin Song, Guanghui An, Dong Xu
llmssafety
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arXiv Computation and Language
Sep 17

"If I Had to Buy Just ONE: Galaxy S26 Ultra": Auditing AI-Generated Product Recommendations

arXiv:2609.18729v1 Announce Type: cross Abstract: Consumers increasingly use AI chatbots for advice on what to buy. With companies like OpenAI and Google monetising their AI through advertising, this...

By Lucas G. Uberti-Bona Marin, Thales Bertaglia, Giovanni Astante, Bram Rijsbosch, Gijs van Dijck, Anik\'o Hann\'ak, Gerasimos Spanakis, Konrad Kollnig
llmssafety
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arXiv Computer Vision
Sep 17

AgenTeeth: A Model-Agnostic Framework for Suppressing Hallucination in Frozen Vision-Language Models on Dental X-Rays via Tool Evidence Injection

arXiv:2609.17800v1 Announce Type: new Abstract: Vision-language models (VLMs) remain largely unreliable on panoramic dental radiographs and can rely on learned anatomical priors rather than evidence...

By Ahmed Rafid, Fariya Ahmed, Rumman Adib, Mehedi Ahamed, Ajwad Abrar, Tareque Mohmud Chowdhury
llmsagentsreinforcement-learningfine-tuningmultimodalsafety
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