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

Oct 5

Can AI Understand the Language of Origami?

arXiv:2603.13856v3 Announce Type: replace Abstract: Building AI systems that can plan, act, and create in the physical world requires more than pattern recognition. Such systems must reason about the...

By Naaisha Agarwal, Yihan Wu, Xin Guan, Ayaan Garg, Yikuan Hu, Mohan Li, Vincenzo Collura, Wang-Zhou Dai, Yao-Xiang Ding, Emanuele Sansone

llms multimodal benchmarks

arXiv AI

Oct 5

MuonIO: Principled Norm-Aware Descent for Embedding Tables and Language Model Heads

arXiv:2610.02705v1 Announce Type: cross Abstract: The Muon optimizer derives its update rule for hidden linear layers by solving a local linearization of the loss penalized by the spectral norm, moti...

By Linkai Ma, Xinyu Luo, Mengbo Wang, Ananth Grama, Petros Drineas, Brian Bullins

llms rag

arXiv AI

Oct 5

ConvoDrift: A Multi-Turn Conversational Dataset for Modeling Stylistic Tone Evolution

arXiv:2610.02873v1 Announce Type: cross Abstract: The evolution of linguistic style in conversations is an underexplored issue in NLP. Most style-control datasets focus on sentences or assume a stati...

By Vihindi Kotalawala, Pamoda Dilranga, Gayani Thoradeniya, Prasan Yapa

llms nlp safety

arXiv AI

Oct 5

Fast Models, Slow Evidence: A Paired and Self-Audited Evaluation of System-1 Decision Models for LLM Agent Harnesses

arXiv:2610.02267v1 Announce Type: new Abstract: Agent harnesses make many small, typed decisions per task: which model to call, which tool to use, whether retrieved text is relevant, whether an input...

By Jiawei Li

llms rag agents

arXiv AI

Oct 5

Choosing Before Acting: Comparative Value Estimation for Long-Horizon Tool-Use Agents

arXiv:2610.02330v1 Announce Type: new Abstract: Large language models (LLMs) rely on long-horizon tool invocation sequences for complex tasks, where each invocation can alter the task state and condi...

By Yu Li, Zheng Zhang, Xin Liu, Shengtian Yang, Guangfeng Cai, Lei Feng

llms agents benchmarks

arXiv AI

Oct 5

Tailoring the Quantization Space for 1-Bit KV Cache Compression

arXiv:2610.03027v1 Announce Type: cross Abstract: The key-value (KV) cache becomes a major memory bottleneck in long-context LLM inference, placing substantial pressure on memory capacity and bandwid...

By Minsoo Cheong, Donghyun Son, Sungjoo Yoo

llms efficiency benchmarks

arXiv AI

Oct 5

WebFovea: When the Model Is Right but the Click Is Wrong -- Reliable Round Trips for Vision-Based Web Agents on Live Websites

arXiv:2610.03036v1 Announce Type: cross Abstract: We present WebFovea, a vision-based web agent that placed 2nd in the WebRetriever Challenge 2026 with a final score of 57.0 out of 100. The challenge...

By Jiangang Han

llms agents multimodal benchmarks safety

arXiv Computation and Language

Oct 5

Last Layer Logits to Logic: Empowering LLMs with Logic-Consistent Structured Knowledge Reasoning

arXiv:2511.07910v3 Announce Type: replace Abstract: Large Language Models (LLMs) achieve excellent performance in natural language reasoning tasks through pre-training on vast unstructured text, enab...

By Songze Li, Zhiqiang Liu, Zhaoyan Gong, Xiaoke Guo, Zhongpu Bo, Zhengke Gui, Lei Liang, Huajun Chen, Wen Zhang

llms nlp benchmarks

arXiv Machine Learning

Oct 5

Autoregressive Differentiable Method for Integer Programming

arXiv:2610.02528v1 Announce Type: new Abstract: We introduce an autoregressive differentiable method to solve 0-1 integer programs. We fix an arbitrary order of the binary variables and we train a tr...

By Ouns El Harzli, Yudong Cao

llms benchmarks

arXiv Computation and Language

Oct 5

Specializing Without Forgetting: Analyzing Knowledge Preservation in Multilingual Model Adaptation

arXiv:2606.00284v2 Announce Type: replace Abstract: While continual pretraining (CPT) is a practical way to extend large language models to new languages, na\"ive finetuning often erodes existing cap...

By Sanchit Ahuja, Terra Blevins

llms fine-tuning

arXiv Machine Learning

Oct 5

DISSOLVR: An Interpretable and Fast Framework for Aqueous and Organic Solubility Prediction

arXiv:2610.02574v1 Announce Type: new Abstract: High-fidelity solubility prediction is fundamental to pharmaceutical development and environmental partitioning, where accurate modeling must couple mo...

By Vansh Ramani, Har Ashish Arora, Dhairya Kuchhal, Sayan Ranu, Tarak Karmakar

llms benchmarks safety

arXiv Machine Learning

Oct 5

Activation Sparsity with Weight Approximation for Faster LLM Decoding on Offloaded Weights

arXiv:2610.02598v1 Announce Type: new Abstract: Deploying LLMs on consumer-grade GPUs with insufficient memory to hold their weights can result in prohibitively slow inference, because decoding repea...

By JuneHyung Kim, Sankeerth Durvasula, Nandita Vijaykumar

llms efficiency

arXiv AI

Oct 5

When Numbers Start Talking: Numerical Signalling and Strategic Behaviour Among LLMs

arXiv:2610.03033v1 Announce Type: new Abstract: Large language model (LLM)-based agents increasingly operate in multi-agent systems (MAS) characterised by strategic interaction. However, little is kn...

By Alessio Buscemi, Daniele Proverbio, Alessandro Di Stefano, The Anh Han, German Castignani, Pietro Li\`o

llms agents safety

arXiv Computation and Language

Oct 5

Hint-Guided Diversified Policy Optimization for LLM Reasoning

arXiv:2606.03021v4 Announce Type: replace Abstract: Recent developments in Large Language Models (LLMs) have showcased impressive reasoning capabilities, with Reinforcement Learning with Verifiable R...

By Zhiyu Cao, Kaixin Wu, Mingjie Zhong, Peifeng Li, Can Ye, Qiaoming Zhu

llms reinforcement-learning

arXiv AI

Oct 5

Gains and Collapse in On-Policy Distillation:A Reinforcement Learning Perspective

arXiv:2610.03185v1 Announce Type: new Abstract: On-policy distillation (OPD) has become an important approach to language model post-training. However, despite its performance gains, OPD can also col...

By Han Cui, Jianhao Yan, Yun Luo, Hongbo Zhang, Zhizhang Fu, Yue Zhang

llms reinforcement-learning fine-tuning efficiency

arXiv Computation and Language

Oct 5

You Only Align Once: Propagating Cooperative Behaviors in Multi-Agent Systems through Seed Agents

arXiv:2605.27586v2 Announce Type: replace-cross Abstract: Ensuring aligned agent behaviors in distributed open multi-agent systems remains challenging, especially as populations grow and unaligned ag...

By Nicole Summer Hsing, Asuka Yuxi Zheng, Yi Zhao, Haoqin Tu, Jen-tse Huang

llms agents safety

arXiv Computer Vision

Oct 5

MeshQuery: Agentic Seam Planning for UV Parametrization

arXiv:2610.02507v1 Announce Type: new Abstract: We present MeshQuery, a training-free agentic approach to automatic UV unwrapping of production-grade quad meshes. A Vision-Language Model (VLM) plans...

By Marco Schouten, Arthur Roullier, Elie Michel, Ruben Wiersma, Axel Paris, Tamy Boubekeur

llms agents multimodal

arXiv AI

Oct 5

D2K-Bench: Can LLM Agents Turn Expert Designs into Efficient GPU Kernels?

arXiv:2610.03226v1 Announce Type: cross Abstract: GPU kernels generated by large language model (LLM) agents can remain less efficient than expert implementations, but runtime alone does not reveal h...

By Daifeng Li, Huiqiang Jiang, Chengruidong Zhang, Wei Wu, Xudong Guo, Jianhong Tu, Jianwei Zhang, Binhang Yuan, Dayiheng Liu

llms agents benchmarks

arXiv AI

Oct 5

Less Decoder is More Encoder: Geometric Representation Learning from Novel View Synthesis

arXiv:2610.03717v1 Announce Type: cross Abstract: This paper examines the role of Novel View Synthesis (NVS) in geometric representation learning. In principle, NVS should reason about 3D scene struc...

By Keerthi Kaashyap, Dennis Anthony, Akshay Krishnan, Nhi Ngoc Nguyen, Jeremy Collins, James Hays, Shreyas Kousik, Animesh Garg

llms computer-vision robotics

arXiv AI

Oct 5

Heads, Tails, and AI Fails: LLMs, Randomness, and Human Judgments

arXiv:2406.00092v2 Announce Type: replace Abstract: Randomness is central to human cognition and to many applications in which large language models are deployed, yet probabilistic token generation d...

By Katherine Van Koevering, Jon Kleinberg

llms nlp safety

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