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 AI

Sep 30

Trustworthiness Costs of Domain Adaptation in Small Language Models:A Cross-Architecture Empirical Study

arXiv:2608.00042v2 Announce Type: replace-cross Abstract: Domain adaptation of small language models (SLMs) has emerged as a practical strategy for deploying capable NLP systems in resource-constrain...

By Ramesh B. Paramkusham

llms nlp reinforcement-learning fine-tuning multimodal benchmarks safety

arXiv AI

Sep 30

Temporal-Attention Head Specialization During Video Diffusion Training

arXiv:2609.31654v2 Announce Type: replace-cross Abstract: Video diffusion transformers depend on temporal attention to coordinate information across frames, yet nearly everything known about this mec...

By Taewoo Ha, Shafayat Mowla Anik, Dae Yeol Lee, Byeong Kil Lee, Jeeho Ryoo

llms diffusion benchmarks

arXiv AI

Sep 30

Video-to-Music Generation for Gameplay Videos

arXiv:2609.31810v2 Announce Type: replace-cross Abstract: Video-to-music models have advanced considerably in the last few years, particularly in film and music video applications. In this paper, we...

By Felipe Marra, Lucas N. Ferreira

llms fine-tuning benchmarks

arXiv AI

Sep 30

Harnessing Coupled Stream Completion For Human-Object Interaction Modeling

arXiv:2609.32551v2 Announce Type: replace-cross Abstract: Text-conditioned human-object interaction (HOI) generation requires body motion, object trajectories & rotations, and hand articulation to re...

By Dawei Guan, Di Yang, Jiangtao Wang

llms

arXiv AI

Sep 30

ASCEND: Personal AI Agents for Autonomous Scientific Computing Across HPC Clusters and GPU Workstations

arXiv:2609.32868v2 Announce Type: replace-cross Abstract: Traditional scientific computing requires researchers to translate computational intent into environment configuration, resource requests, an...

By J. Paul Liu, Uthpala Herath, Andrew Petersen

llms agents benchmarks

arXiv AI

Sep 30

Coherence-Aware Distributional Evaluation of Open-Ended Text Generation

arXiv:2609.34240v2 Announce Type: replace-cross Abstract: Existing open-ended generation metrics measure likelihood, lexical diversity, or distributional similarity in generic representation space, y...

By Jinnuo Liu, Junhao Zhu, Weifeng Jiang, Haoming Liu, Hongyi Wen

llms

arXiv AI

Sep 30

Diffusion Reward Models

arXiv:2609.33803v2 Announce Type: replace-cross Abstract: Reward models underpin the alignment of large language models, yet the dominant designs reduce each prompt--response pair to a point estimate...

By Xiangyang Wang, Bingxiang He, Zeyuan Liu, Jiaze Wang, Ziqing Qiao, Yuxin Zuo, Huan-ang Gao, Cheng Qian, Wenbin Zhang, Ran Li, Youbang Sun, Ning Ding, Yuanchun Shi, Zhiyuan Liu, Chaojun Xiao, Chun Yu

llms diffusion reinforcement-learning multimodal benchmarks safety

arXiv Machine Learning

Sep 30

FluxLite: Inference-Time Proposal Control for Discrete Diffusion Models

arXiv:2609.35947v1 Announce Type: new Abstract: Many inference-time tasks for pretrained discrete diffusion models and diffusion language models reduce to drawing samples from a tilted version of the...

By Yinuo Ren, Haoxuan Chen, Grant M. Rotskoff, Jiequn Han, Lexing Ying

llms diffusion benchmarks

arXiv Machine Learning

Sep 30

Understanding LLM Parameter Update Sparsity through the Lens of Fisher

arXiv:2609.36262v1 Announce Type: new Abstract: Recent studies have observed that parameter changes during language-model post-training can be concentrated in a small subset of coordinates. This phen...

By Yufan Zhang, Sagnik Mukherjee, Hao Peng

llms reinforcement-learning fine-tuning efficiency

arXiv Machine Learning

Sep 30

When Trees Are Not Enough: Learning Mixed-Topology Feature Graphs with Adaptive Graph Sparse Autoencoders

arXiv:2609.36294v1 Announce Type: new Abstract: Sparse autoencoders (SAEs) expose interpretable features in large language model activations, yet existing structured SAEs impose single-parent trees o...

By Xiaozuo Shen, Yifei Cai, Tian Tan, Rui Ning, Chunsheng Xin, Hongyi Wu

llms

arXiv AI

Sep 30

Trajectory Soup: Pushing the Compute-Scaling Frontier of LLM Mid-training via Diverse Trajectories

arXiv:2609.37169v1 Announce Type: cross Abstract: Mid-training equips pretrained large language models with specialized and reasoning capabilities, but the returns of this stage are bounded since add...

By Zhehao Huang, Changxin Tian, Qingyuan Yang, Kunlong Chen, Ziqi Liu, Zhiqiang Zhang, Xiaolin Huang, Jun Zhou

llms safety

arXiv Machine Learning

Sep 30

Replay the Curvature: Accurate and Scalable NVFP4 Quantization for Large Language Model Inference

arXiv:2609.36654v1 Announce Type: new Abstract: Large language models make weight storage and memory traffic major inference costs, motivating low-precision formats that represent each weight with on...

By Ruiyi Ding, Jie Li, Kang He, Ziyan Liu, Chengru Song, Yuedong Xu, Yuan Cheng

llms efficiency benchmarks

arXiv Machine Learning

Sep 30

CHAIN: Calibrated LLM Forecasting via Causal-Temporal Hypergraph Inference

arXiv:2609.36689v1 Announce Type: new Abstract: Large language models have achieved significant progress in event forecasting, yet their probability outputs exhibit systematic calibration bias that v...

By Wenjin Liu, Chenxi Wang, Yue Lu, Zhe Cui, Haoran Luo

llms benchmarks safety

arXiv Machine Learning

Sep 30

Learned Queries and Keys Are All You Need: Replacing the Value Projection with Structured Transforms

arXiv:2609.36698v1 Announce Type: new Abstract: To reduce the number of parameters and cache memory requirements of transformers we introduce dual-headed transformers instead of three heads. We studi...

By Ene Meco, Emadeldeen Hamdan, A. Enis Cetin

llms

arXiv Machine Learning

Sep 30

EasyPPO: Stabilizing the Critic Is Key

arXiv:2609.36802v1 Announce Type: new Abstract: A key strength of Proximal Policy Optimization (PPO) is its learned critic, which uses historical trajectories collected during reinforcement learning...

By Xuanyi Zhou, Qiuyang Mang, Huanzhi Mao, Dacheng Li, Wenhao Chai, Mayank Mishra, Yichuan Wang, Karthik Narasimhan, Alvin Cheung, Joseph E. Gonzalez

llms reinforcement-learning

arXiv Machine Learning

Sep 30

Architecture Alignment With Sparse Priors in Tabular Foundation Models

arXiv:2609.36883v1 Announce Type: new Abstract: Tabular foundation models (TFMs) are increasingly popular because they deliver strong predictions on new datasets through in-context learning, without...

By Tianqi Zhao, Tianyi Zhuang, Shuo Duan, Guanyang Wang, Yan Shuo Tan, Qiong Zhang

llms safety

arXiv Machine Learning

Sep 30

Message Passing Does More with Less for In-Context Learning on Graphs

arXiv:2609.37057v1 Announce Type: new Abstract: Achieving strong performance with graph neural networks (GNNs) typically requires training and hyperparameter tuning for each dataset, incurring repeat...

By Dooho Lee, Jinmo Lee, Minho Jeong, Kijung Shin, Jaemin Yoo

llms

arXiv Machine Learning

Sep 30

Trident: Unifying Guarded Dispatch and Host Execution for PyTorch Triton Workloads

arXiv:2609.37241v1 Announce Type: new Abstract: User-written Triton kernels enable high-performance GPU computation within PyTorch, but their end-to-end latency can remain dominated by host-side orch...

By Jinjie Liu, Xiaoyan Liu, Shuhan Zhang, Wenjia Sun, Ruilin Yang, Chunlei Men, Yonghua Lin, Shaohua Li

llms agents

arXiv Machine Learning

Sep 30

Scaling Full Conformal Image Classifiers

arXiv:2609.37298v1 Announce Type: new Abstract: Conformal prediction provides set-valued predictions with distribution-free coverage guarantees, making it attractive for high-stakes image classificat...

By Julio Silva-Rodr\'iguez, Ender Konukoglu

llms computer-vision multimodal benchmarks

arXiv Machine Learning

Sep 30

Scale Sensitivity in Low-Bit Post-Training Quantization: Curvature of the Quantization Error Landscape

arXiv:2609.37416v1 Announce Type: new Abstract: Post-training quantization (PTQ) methods in the GPTQ family minimize a layer-wise reconstruction error on a uniform grid whose scale must be chosen; th...

By Jonas von Berg, Massimiliano Datres, Carlo Knei{\ss}l, Gitta Kutyniok

llms efficiency

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