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 23

TTE-Flash: Accelerating Reasoning-based Multimodal Representations via Think-Then-Embed Tokens

arXiv:2605.16638v2 Announce Type: replace Abstract: Recent research has demonstrated that Universal Multimodal Embedding (UME) benefits significantly from Chain-of-Thought (CoT) reasoning. In this pa...

By Jianpeng Cheng, Xian Wu, Jiangfan Zhang, Wentao Bao, Chaitanya Ahuja, Shlok Kumar Mishra, Xuanming Cui, Hanchao Yu, Yang Gao, Fan Xia, Haixing Dai, Frankie Yuan, Zihao Wang, Xiaobing Chen, Qi Guo, Shaodan Zhai, Aashu Singh, Xiangjun Fan, Jun Xiao

llms rag multimodal benchmarks

arXiv AI

Sep 23

Toward an Unbiased Collective Memory for Efficient LLM-Based Agentic 6G Cross-Domain Management

arXiv:2509.26200v2 Announce Type: replace-cross Abstract: Agentic artificial intelligence is a candidate enabler of Level-4 autonomy in sixth-generation (6G) networks, but agents reasoning over a sha...

By Hatim Chergui, Farhad Rezazadeh, Miguel Catalan Cid, Pouria Sayyad Khodashenas, Daniel Camps Mur, Christos Verikoukis

llms agents safety

arXiv AI

Sep 23

Heterogeneous Robot Collaboration in Unstructured Environments with Grounded Generative Intelligence

arXiv:2510.26915v2 Announce Type: replace-cross Abstract: While heterogeneous teams have typically been designed for well-specified missions with known semantics, generative intelligence, i.e., large...

By Zachary Ravichandran, Fernando Cladera, Ankit Prabhu, Jason Hughes, Carlos Nieto-Granda, Varun Murali, Camillo Taylor, George J. Pappas, Vijay Kumar

llms robotics multimodal

arXiv AI

Sep 23

A Human-AI Collaborative Workflow for Mathematical Discovery: A Case Study in Grover-Compatible Riemannian Optimization

arXiv:2512.09443v3 Announce Type: replace-cross Abstract: We investigate how large language models can be used as research tools in scientific computing while preserving mathematical rigor. We propos...

By Chenyi Li, Zhijian Lai, Dong An, Jiang Hu, Zaiwen Wen

llms

arXiv AI

Sep 23

When Scaling Fails: Mitigating Audio Perception Decay of LALMs via Multi-Step Perception-Aware Reasoning

arXiv:2603.02266v2 Announce Type: replace-cross Abstract: Test-Time Scaling has shown notable efficacy in addressing complex problems through scaling inference compute. However, within Large Audio-La...

By Ruixiang Mao, Xiangnan Ma, Dan Chen, Ziming Zhu, Yuan Ge, Aokai Hao, Haishu Zhao, Yifu Huo, Qing Yang, Kaiyan Chang, Xiaoqian Liu, Chenglong Wang, Qiaozhi He, Tong Xiao, Jingbo Zhu

llms reinforcement-learning benchmarks

arXiv AI

Sep 23

Cognition to Control - Multi-Agent Learning for Human-Humanoid Collaborative Transport

arXiv:2603.03768v2 Announce Type: replace-cross Abstract: Full-stack human-robot collaboration (HRC) can become brittle when replacing a planner, partner model, coordination policy, or controller cha...

By Hao Zhang, Yisen Li, Ruize Geng, Yves Tseng, Yaru Niu, Ding Zhao, H. Eric Tseng

llms agents reinforcement-learning robotics multimodal

arXiv AI

Sep 23

Using LLMs in Software Design: An Empirical Study of GitHub and A Practitioner Survey

arXiv:2605.01392v2 Announce Type: replace-cross Abstract: Recent advancements in Large Language Models (LLMs) have demonstrated significant potential across software engineering tasks, including soft...

By Yifei Wang, Ruiyin Li, Peng Liang, Yangxiao Cai, Zengyang Li, Mojtaba Shahin, Arif Ali Khan, Qiong Feng

llms

arXiv AI

Sep 23

APIOT: Autonomous Vulnerability Management Across Bare-Metal Industrial OT Networks

arXiv:2605.02346v2 Announce Type: replace-cross Abstract: Operational technology (OT) devices run safety-critical physical processes, yet their security testing remains manual and expert-intensive. A...

By Adel ElZemity, Budi Arief, Shujun Li, George Oikonomou, James Pope

llms agents safety

arXiv AI

Sep 23

gwBenchmarks: Stress-Testing LLM Agents on High-Precision Gravitational Wave Astronomy

arXiv:2605.11269v2 Announce Type: replace-cross Abstract: Modern gravitational wave astronomy relies on modeling tasks that often require months of graduate-level effort, including building fast wave...

By Tousif Islam, Digvijay Wadekar, Zihan Zhou

llms agents benchmarks

arXiv AI

Sep 23

Compared to What? A Human-Anchored Security Benchmark for LLM-Generated Infrastructure-as-Code

arXiv:2608.28021v2 Announce Type: replace-cross Abstract: Large language models increasingly author Infrastructure-as-Code (IaC), where one insecure default is provisioned straight into production. P...

By Animesh Shaw

llms benchmarks

arXiv AI

Sep 23

RoofLang: Enabling AI-Driven Architecting of LLM Inference Systems

arXiv:2609.12551v2 Announce Type: replace-cross Abstract: AI is beginning to make substantive contributions to LLM inference optimization. Existing AI optimizations are predominantly profiling-based....

By Ziyue Yang, Yuting Jiang, Lei Qu, Peng Cheng

llms agents efficiency

arXiv Machine Learning

Sep 23

"As a Language Model...": Chat Template Switches LLM Self-Referential Voice and Activation Steering Reproduces It

arXiv:2609.25021v1 Announce Type: new Abstract: Large Language Models (LLMs) tend to add disclaimers like "I'm just an AI" when asked about something related to themselves. The self-reports from such...

By J\k{e}drzej Maczan

llms safety

arXiv Machine Learning

Sep 23

Trains but Doesn't Learn: A Post-Training Delivery Benchmark for LLM Agents as Forward-Deployed Engineers

arXiv:2609.25237v1 Announce Type: new Abstract: Post-training is becoming a service (PTaaS): a customer hands an operator data and a goal, and a forward-deployed engineer (FDE) returns a fine-tuned,...

By Weihang Ding, Junfei Zhan

llms agents fine-tuning benchmarks

arXiv Machine Learning

Sep 23

Mean Velocity Matching: Rethinking Generative Dynamics in Diffusion Models

arXiv:2609.25444v1 Announce Type: new Abstract: This work studies prediction parameterization for stochastic generative dynamics in diffusion models. Existing velocity-based generative models provide...

By Yunhong Zhang, Changjie Cao, Zhihua Zhang, Bingli Liu, Zongjie Cao, Zongyong Cui, Ying Yang

llms diffusion

arXiv Machine Learning

Sep 23

Efficient Cost-Aware LLM Evaluation via Bayesian Bandit Gittins Indices

arXiv:2609.25645v1 Announce Type: new Abstract: Exhaustively evaluating every candidate LLM configuration on every benchmark item to identify a high-performing one is costly. We formulate configurati...

By Qian Xie, Yueli He, Nairen Cao

llms reinforcement-learning benchmarks

arXiv Machine Learning

Sep 23

From Experts to Sub-experts: Fine-grained Parameter-Efficient Fine-Tuning for MoE LLMs

arXiv:2609.25655v1 Announce Type: new Abstract: As large language models (LLMs) scale rapidly, dense full-parameter adaptation becomes increasingly expensive, motivating sparse and modular architectu...

By Zhentao Tan, Chang Liu, Yao Liu, Yue Wu, Jieping Ye

llms fine-tuning benchmarks

arXiv Machine Learning

Sep 23

Evaluating Accuracy and Probabilistic Reliability of Zero-Shot Time Series Foundation Models

arXiv:2609.25788v1 Announce Type: new Abstract: Time Series Foundation Models (TSFMs) promise a paradigm shift toward zero-shot forecasting by eliminating task-specific training. However, existing wo...

By Panagiotis Michael, Moysis Symeonides, Demetris Trihinas

llms benchmarks

arXiv Machine Learning

Sep 23

You Only Need 2/3 of the Chosen Experts: An Empirical Study of Dynamic Expert Pruning in Fine-Grained MoE LLMs

arXiv:2609.25809v1 Announce Type: new Abstract: Fine-grained mixture-of-experts (MoE) architectures have become a mainstream design for open-weight LLMs, with hundreds of experts and increasingly man...

By Yuanteng Chen, Qiwei Lai, Chen Tianqi, Peisong Wang, Yuantian Shao, Nanxin Zeng, Zhilei Liu, Chuangyi Li, Jing Liu, Jian Cheng

llms efficiency multimodal benchmarks

arXiv Machine Learning

Sep 23

In-Context Guidance: Learning Inter-Task Synergies via Numerical Foundational Models for Few-Shot Multitask Optimization

arXiv:2609.25836v1 Announce Type: new Abstract: Multi-task optimization (MTO) addresses a set of optimization tasks simultaneously, often suffering from inaccurate inter-task relationship estimation...

By Tingyang Wei, Haofeng Wu, Jiao Liu, Zhao Wei, Puay Siew Tan, Yew-Soon Ong

llms robotics benchmarks

arXiv Machine Learning

Sep 23

GeoPair: Geometry-Preserving Cross-Layer Factorization for Training-Free Transformer Compression

arXiv:2609.25963v1 Announce Type: new Abstract: Transformer architectures exhibit cross-layer redundancies, yet post-training compression pipelines typically optimize layers in isolation or rely on h...

By Baher Mohammad, Ammar Ali, Stamatios Lefkimmiatis

llms efficiency benchmarks

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