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

BadRAG: Identifying Vulnerabilities in Retrieval Augmented Generation of Large Language Models

arXiv:2406.00083v3 Announce Type: replace-cross Abstract: Retrieval-Augmented Generation (RAG) enhances Large Language Models (LLMs) by retrieving relevant information from external knowledge bases t...

By Jiaqi Xue, Mengxin Zheng, Yebowen Hu, Fei Liu, Xun Chen, Qian Lou

llms rag robotics safety

arXiv AI

Sep 30

Asymptotic Universal Alignment: A New Alignment Framework via Test-Time Scaling

arXiv:2601.08777v2 Announce Type: replace-cross Abstract: Aligning large language models (LLMs) to serve users with heterogeneous and potentially conflicting preferences is a central challenge for pe...

By Yang Cai, Weiqiang Zheng

llms reinforcement-learning safety

arXiv AI

Sep 30

COMiT: Learning Structured Visual Tokens through Sequential Communication

arXiv:2602.20731v2 Announce Type: replace-cross Abstract: Discrete image tokenizers provide a sequential interface for vision and multimodal models, but are typically optimized for reconstruction or...

By Aram Davtyan, Yusuf Sahin, Yasaman Haghighi, Sebastian Stapf, Pablo Acuaviva, Alexandre Alahi, Paolo Favaro

llms nlp multimodal safety

arXiv AI

Sep 30

Meta-SecAlign: Training LLMs against Prompt Injection for Robust Agents

arXiv:2507.02735v4 Announce Type: replace-cross Abstract: Prompt injection attacks, where untrusted data contains an injected prompt to manipulate the system, have been listed as the top security thr...

By Sizhe Chen, Arman Zharmagambetov, David Wagner, Chuan Guo

llms agents fine-tuning benchmarks

arXiv AI

Sep 30

Do Proactive Agents Need an LLM to Decide When to Act?

arXiv:2605.30152v2 Announce Type: replace-cross Abstract: Proactive assistants continuously decide when to intervene and what context should support the intervention. Large language model (LLM) pipel...

By Xiaoze Liu, Ruowang Zhang, Amir H. Abdi, Michel Galley, Zhikai Chen, Siheng Xiong, Xiaoqian Wang, Jing Gao

llms agents

arXiv AI

Sep 30

Scalable Heterogeneous Graph Foundation Models for Data-Driven Optimal Power Flow in Smart Grids

arXiv:2605.23194v2 Announce Type: replace-cross Abstract: Fast and reliable optimal power flow (OPF) approximation is important for power system operation, yet heterogeneous OPF graph models are ofte...

By Massimiliano Lupo Pasini, Yijiang Li, Kibaek Kim, Teja Kuruganti

llms fine-tuning benchmarks

arXiv AI

Sep 30

Guava: Distilling Frontier VLMs into a Compact Agent through a Robotic Manipulation Harness

arXiv:2606.18363v3 Announce Type: replace-cross Abstract: Language models trained on large-scale vision-language data have demonstrated strong potential for embodied agents. Harnessing models through...

By Haowen Liu, Xirui Li, Shaoxiong Yao, Peng Shi, Tianyi Zhou, Jia-Bin Huang, Furong Huang, Jiayuan Mao

llms agents robotics multimodal

arXiv AI

Sep 30

OneLatent: Latent Reasoning for Efficient Foundation Recommendation Models

arXiv:2607.26621v3 Announce Type: replace-cross Abstract: Large language models (LLMs) have demonstrated strong reasoning capabilities, motivating their use as the backbone of foundation recommendati...

By Hao Jiang, Peiru Du, Pengfei Yao, Mengting Li, Siyuan Lou, Kuo Cai, Sheng Yu, Qiang Luo, Jian Liang, Ruiming Tang, Fei Pan, Peng Jiang, Wenwu Ou

llms efficiency benchmarks safety

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

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