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 16

The Evolution of Coordination in a Collective Intelligence System: 25 Years of English Wikipedia and the Emergence of Generative AI

arXiv:2609.16856v1 Announce Type: cross Abstract: English Wikipedia is one of the largest examples of collective intelligence on the Web, sustained not only by article production but also by voluntee...

By Neal Reeves, Maja \'Swieczkowska, Amy Rechkemmer, Elena Simperl

llms

arXiv AI

Sep 16

Finding Common Mistakes In Modelling With Mathematical Formalisms Using LLMs

arXiv:2609.17111v1 Announce Type: cross Abstract: Modelling with mathematical formalisms like logical formulas, mathematical equations, or regular expressions is an important yet challenging task for...

By Lilian Killich, Marko Schmellenkamp, Fabian Vehlken, Thomas Zeume

llms

arXiv AI

Sep 16

Where Should a Document Live: Context, Representations, or Parameters?

arXiv:2609.17346v1 Announce Type: cross Abstract: To answer questions outside of their pre-training data, large language models (LLMs) need access to new information, which can be presented in the co...

By Nathana\"el Carraz Rakotonirina, Momchil Hardalov, Gonzalo Iglesias, Adri\`a de Gispert

llms fine-tuning benchmarks

arXiv Machine Learning

Sep 16

LoopSpec: Pipelined Self-Speculative Decoding for Looped Transformers

arXiv:2609.17184v1 Announce Type: new Abstract: Looped Transformers achieve strong performance with compact parameter sizes by repeatedly applying a shared stack of Transformer blocks across recurren...

By SangLyul Cho, Langqing Cui, Sehoon Kim, Dongsu Han, Insu Han

llms benchmarks

arXiv AI

Sep 16

FactorEngine: A Program-level Knowledge-Infused Factor Mining Framework for Quantitative Investment

arXiv:2603.16365v3 Announce Type: replace Abstract: We study alpha factor mining, the automated discovery of predictive signals from noisy, non-stationary market data-under a practical requirement th...

By Qinhong Lin, Ruitao Feng, Yinglun Feng, Zhenxin Huang, Yukun Chen, Zhongliang Yang, Linna Zhou, Binjie Fei, Jiaqi Liu, Yu Li

llms rag agents benchmarks safety

arXiv AI

Sep 16

The Troy Moment of AI: Why Some Will Cheat and Some Will Follow?

arXiv:2609.15494v2 Announce Type: replace Abstract: Recent investigations of the July 2026 OpenAI-Hugging Face incident motivate two questions: when an assigned task becomes impossible, does an agent...

By Ivy Zhang

llms agents benchmarks

arXiv AI

Sep 16

MASCOT: Multi-Agent Socio-Collaborative Companion Systems

arXiv:2601.14230v3 Announce Type: replace-cross Abstract: Multi-agent systems (MAS) are emerging as promising socio-collaborative companions for emotional and cognitive support. However, existing sys...

By Yiyang Wang, Yiqiao Jin, Alex Cabral, Josiah Hester

llms agents benchmarks safety

arXiv AI

Sep 16

Comparative Characterization of KV Cache Management Strategies for LLM Inference

arXiv:2604.05012v2 Announce Type: replace-cross Abstract: Efficient inference with Large Language Models (LLMs) increasingly relies on Key-Value (KV) caches to store previously computed key and value...

By Oteo Mamo, Olga Kogiou, Hyunjin Yi, Weikuan Yu

llms efficiency benchmarks

arXiv AI

Sep 16

LLM-Guided Dynamic Action Spaces for Synthesizable Molecular Optimization

arXiv:2604.07669v3 Announce Type: replace-cross Abstract: Synthesizable molecular optimization seeks to improve target properties while ensuring that molecular modifications follow feasible synthetic...

By Tao Li, Kaiyuan Hou, Tuan Vinh, Fanglei Xue, Monika Raj, Zhichun Guo, Carl Yang

llms

arXiv AI

Sep 16

Ave: Guiding Agentic GPU Optimization Using Data-Flow Invariants

arXiv:2604.18616v2 Announce Type: replace-cross Abstract: LLM coding agents can generate correct GPU kernels, but their performance still trails expert libraries. Reaching peak throughput requires co...

By Haohui Mai, Xiaoyan Guo, Xiangyun Ding, Daifeng Li, Qiuchu Yu, Chenzhun Guo, Cong Wang, Jiacheng Zhao, Christos Kozyrakis, Binhang Yuan

llms rag agents reinforcement-learning efficiency benchmarks safety

arXiv AI

Sep 16

ASTRIL-MPC: Autonomous Traversal Framework of Articulated Tracked Robots with Language-Guided Neural-Kinematic MPC

arXiv:2609.13083v2 Announce Type: replace-cross Abstract: In urban search and rescue, articulated tracked robots (ATRs) must traverse structured but contact-rich environments such as stairwells and c...

By Zhenfeng Gan, Yanbo Chen, Lirong Che, Junbo Tan, Xueqian Wang

llms agents reinforcement-learning robotics safety

arXiv Machine Learning

Sep 16

Few-Shot Degradation Is Not What It Seems: Behavioral Evidence, Representation Analysis, and a Random-Text Control Across 12 Models, 2 Tasks, and 2 Architectures

arXiv:2609.15990v1 Announce Type: cross Abstract: Few-shot prompting sometimes degrades language models instead of helping them, but why this happens is unknown. We evaluate 12 open-weight models on...

By Volodymyr Ovcharov

llms

arXiv Machine Learning

Sep 16

Tables Decoded: DELTA for Structure, TARQA for Understanding

arXiv:2609.17458v1 Announce Type: cross Abstract: Table understanding is a core task in document intelligence, encompassing two key subtasks: table reconstruction and table visual question answering...

By Jahanvi Rajput, Dhruv Kudale, Saikiran Kasturi, Utkarsh Verma, Ganesh Ramakrishnan

llms computer-vision nlp fine-tuning benchmarks

arXiv Machine Learning

Sep 16

CodecSight: Leveraging Video Codec Signals for Efficient Streaming VLM Inference

arXiv:2604.06036v4 Announce Type: replace-cross Abstract: Continuous inference over concurrent video streams imposes substantial compute and memory demands on vision-language model (VLM) serving. Str...

By Yulin Zou, Wenyan Chen, Yan Chen, Anya Rajan, JooYoung Park, Shivaraman Nitin, Luo Tao, Francisco Romero, Dmitrii Ustiugov

llms efficiency multimodal benchmarks