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

Oct 5

Overcoming Challenges of Interpretive Structural Modeling with Large Language Models

arXiv:2610.02254v1 Announce Type: cross Abstract: Interpretive Structural Modeling (ISM) is a well-known process for multi-criteria decision making. The success of ISM over other methodologies is its...

By Everett Rush, David J. Icove, Ari Kim, Byung H. Park, Michael A. Langston

llms

arXiv AI

Oct 5

Frequency Is Not Sensitivity Identifying Safety-Sensitive Experts in Sparse MoE LLM

arXiv:2610.02910v1 Announce Type: new Abstract: Suppressing a small set of routed experts can weaken the safety behavior of a sparse Mixture-of-Experts (MoE) language model without retraining. Which...

By Md Nurul Absar Siddiky, Liuwan Zhu, Yingfei Dong

llms safety

arXiv AI

Oct 5

PsyEvo: A Personalized Counseling Agent That Self-Evolves at Test Time

arXiv:2610.02885v1 Announce Type: new Abstract: Mental health disorders affect a substantial proportion of the global population, yet a persistent shortage of trained practitioners leaves the majorit...

By Yuting Yan, Shihao Xu, Junhao Yu, Mingcong Zuo, Lu Chen, Nan Xiang, Haiyang Geng, Dongjie Tao, Minghao Wang

llms agents

arXiv AI

Oct 5

Labels Override Definitions in Jev-Style Typed Decision Models

arXiv:2610.02586v1 Announce Type: new Abstract: A typed decision model answers a fixed question about an input by returning a probability for each of several caller-defined options. Each option carri...

By Seyedarmin Azizi, Erfan Baghaei Potraghloo, Massoud Pedram

llms safety

arXiv AI

Oct 5

MIRROR: Multipath Quorum Integrity for LLM Multi-Agent Communication

arXiv:2610.02349v1 Announce Type: cross Abstract: Inter-agent communication is central to Large Language Model Multi-Agent Systems (LLM-MAS), but it introduces an underexplored vulnerability: Agent-i...

By Ryuichi Yamafuji Lun, Jingzhen Wang, Shreyas Kolte, Ruiteng Li

llms agents multimodal benchmarks

arXiv AI

Oct 5

Hypothesis-guided discovery of cognitive algorithms via program refinement

arXiv:2610.02523v1 Announce Type: new Abstract: Developing cognitive models of algorithmic reasoning from behavioral data is a central problem in cognitive science that challenges current methods. Tr...

By Huiwen Alex Yang, Mark K. Ho, Bill D. Thompson

llms agents

arXiv AI

Oct 5

Revisiting Visual Representation Enhancement of VLMs via Kernel Canonical Correlation Analysis

arXiv:2610.02718v1 Announce Type: cross Abstract: Vision-language models such as CLIP exhibit strong semantic generalization, but remain limited in fine-grained visual perception. A recent work named...

By Peilin Yang, Xiaoyu Liu, Jian Sun, Qinghua Tao

llms rag fine-tuning multimodal safety

arXiv AI

Oct 5

Improving the Energy-Efficiency of the Code Generated by LLMs through Effective Prompting

arXiv:2610.02571v1 Announce Type: cross Abstract: As AI-assisted programming becomes increasingly mainstream, the environmental impact of AI-generated software has emerged as an important considerati...

By Ritika Rekhi, Bing Zhang, Md Arman Islam, Jaya Krishna Pasham, Yeswanth Chitturi, Akshay Paramesha, Isha Valiveti, Asif Imran, Bekir Turkkan, Tevfik Kosar

llms

arXiv AI

Oct 5

EchoDistill: Robust Large Audio Language Models via Noisy-to-Clean Self-Distillation

arXiv:2605.23954v2 Announce Type: replace-cross Abstract: Large Audio Language Models (LALMs) remain vulnerable to acoustic noise, which can obscure task-relevant evidence and produce unreliable resp...

By Kaiwen Luo, Chunxi Luo, Liang Lin, Yuxuan Li, Zhenhong Zhou, Junhao Dong, Yingjie Zhou, Zhendong Chu

llms efficiency benchmarks

arXiv AI

Oct 5

LEAF: A Living Benchmark for Event-Augmented Forecasting

arXiv:2605.16358v2 Announce Type: replace-cross Abstract: Large Language Models (LLMs) are increasingly applied to real-world forecasting tasks, yet evaluating their true predictive capability remain...

By Mingtian Tan, Mihir Parmar, Palash Goyal, Chun-Liang Li, Nanyun Peng, Thomas Hartvigsen, Jinsung Yoon, Tomas Pfister

llms agents benchmarks

arXiv AI

Oct 5

Verify Before You Fix: Agentic Execution Grounding for Trustworthy Cross-Language Code Analysis

arXiv:2604.10800v2 Announce Type: replace-cross Abstract: Learned classifiers deployed in agentic pipelines face a fundamental reliability problem: predictions are probabilistic inferences, not verif...

By Jugal Gajjar

llms rag agents

arXiv AI

Oct 5

Evaluation format, not model capability, drives measured triage failure in the assessment of consumer health AI

arXiv:2603.11413v4 Announce Type: replace-cross Abstract: A recent Nature Medicine study reported that ChatGPT Health under-triages 51.6% of emergencies and concluded that consumer-facing AI triage p...

By David Fraile Navarro, Jialei Sheng, Farah Magrabi, Enrico Coiera

llms benchmarks safety

arXiv AI

Oct 5

$T^5$: Twin-Critic Training for Token-Level Thoughts in Reinforcement Mid-Training

arXiv:2609.32791v2 Announce Type: replace Abstract: Reinforcement mid-training lets language models learn internal thoughts from unlabeled text, but efficient token-level credit assignment remains ch...

By Nan Qiao, Yebin Yang, Weinong Wang, Shuning Wang, Shangpin Peng, Fengyuan Lu, Xinming Wang, Zhehan Kan, Ruixu Zhang, Songyang Zhang, Sheng Yue, Yonglong Tian, Ju Ren

llms reinforcement-learning benchmarks

arXiv AI

Oct 5

ANCRe: Adaptive Neural Connection Reassignment for Efficient Depth Scaling

arXiv:2602.09009v2 Announce Type: replace-cross Abstract: Scaling network depth has been a central driver behind the success of modern foundation models, yet recent investigations suggest that deep l...

By Yilang Zhang, Bingcong Li, Niao He, Georgios B. Giannakis

llms diffusion

arXiv Machine Learning

Oct 5

SlimKV: Joint Token-Feature KV Cache Compression with Reconstruction-Free Beacon Attention

arXiv:2610.02953v1 Announce Type: new Abstract: Long-context LLM serving is increasingly bottlenecked by KV-cache memory, especially in resource-constrained scenarios. Among existing KV-cache compres...

By Zihan Teng, Jiayu Zhao, Wentao Ren, Minhao Fan, Tianrui Ma, Song Chen, Weichen Liu

llms rag efficiency

arXiv Machine Learning

Oct 5

Muon Learns Facts Better: Understanding the Role of Spectral Orthogonalization

arXiv:2610.02798v1 Announce Type: new Abstract: The Muon optimizer applies spectral orthogonalization to matrix-valued updates and has shown strong performance in large-scale neural network training,...

By Xuheng Li, Qiwei Di, Yuan Cao, Quanquan Gu

llms rag

arXiv Machine Learning

Oct 5

All Work And No Play Makes Jack a Dull Boy: Understanding and Preventing Catastrophic Strategy Collapse in RLVR

arXiv:2610.02835v1 Announce Type: new Abstract: During post-training of large language models (LLMs) with Reinforcement Learning with Verifiable Rewards (RLVR), GRPO-style algorithms can exhibit seve...

By Qiyuan Huang, Tianshi Xu, Meng Li

llms reinforcement-learning efficiency

arXiv Machine Learning

Oct 5

Hesitation Has a Geometry: Entropy-Trained Hyperbolic Probes for Sparse Activation Steering

arXiv:2610.02391v1 Announce Type: new Abstract: When a large language model solves a mathematical problem, its reasoning is largely hierarchical, and the solution often branches at a few tokens where...

By Zeyong Zhang, Tung Sum Thomas Kwok, Tengfei Ma, Mengjia Xu

llms

arXiv Machine Learning

Oct 5

Teaching LLMs to See Graphs: Unifying Text and Structural Reasoning

arXiv:2605.10247v2 Announce Type: replace Abstract: Applying Large Language Models (LLMs) to graph-structured data usually involves multi-step pipelines in which textual node attributes are compresse...

By Dario Vajda

llms fine-tuning benchmarks

arXiv Machine Learning

Oct 5

From Patching to Pruning Visual Computation in Vision Language Models

arXiv:2610.03389v1 Announce Type: cross Abstract: Vision language models (VLMs) incur substantial inference cost because every visual token is processed by the attention and MLP projections of every...

By Rahul Chowdhury, Timothy A Rupprecht, Xuan Shen, Shaoyi Huang, Pu Zhao, Yanzhi Wang

llms efficiency multimodal benchmarks safety

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