The paper proposes a framework to evaluate the reliability of large language model (LLM) agents when they generate stakeholder preferences for multi‑criteria decision‑making in healthcare operations. It examines both explicit preferences—agents rating the importance of patient waiting time, provider workload, and operating cost in an outpatient scheduling scenario—and implicit preferences—agents’ pairwise choices between emergency department configurations. Experiments show that explicit preferences vary by role and operating conditions, but deliberation can homogenize them, while implicit preferences are heavily influenced by presentation order and only modestly improved by contextual models. Robust weighted‑sum optimization models that incorporate preference uncertainty often yield different schedules than those based on average weights, underscoring the need for auditing LLM‑generated preferences before using them in healthcare decision support.
By Marco Boresta, Tommaso Giovannelli, Sepehr Namirad, Giulia Riccardi
The paper introduces a model‑agnostic framework that lets frozen large language models (LLMs) and vision‑language models (VLMs) learn from deployment experience without retraining. It does so by adding three external expertise components—a Skill for guiding reasoning and tool use, a Knowledge Memory for storing reliable facts, and a Multimodal Knowledge Base for visual examples—and by using a validation strategy that updates only when new cases improve performance without harming past performance. Experiments on six benchmarks with both open‑weight and closed‑source models show up to a 34.2% performance gain on medical tasks, generalization to unseen cases, and transferability to other models and non‑medical domains.
By Yexiao He, Yucheng Tang, Pengfei Guo, Yufan He, Andriy Myronenko, Can Zhao, Ang Li, Daguang Xu, Dong Yang
The paper questions whether Theory of Mind (ToM) benchmarks truly assess theory of mind or merely test parsing and algorithmic state tracking. It introduces PDDL-Mind, a neuro‑symbolic method that parses narratives into the Planning Domain Definition Language (PDDL) and executes candidate actions using predefined update algorithms, yielding program‑verified state traces. Experiments on MMToM, MuMA, and FanToM show that delegating parsing to programs and state tracking to PDDL significantly improves performance, suggesting that current benchmarks mainly penalize unreliable implicit state tracking rather than genuine belief or counterfactual reasoning.
By Qiutong Tony Yi, Wang Bill Zhu, Robin Jia, Jesse Thomason
arXiv:2610.23707v1 Announce Type: new
Abstract: A multi-step agent task fails when a single tool it needs is missing, yet the tools an agent may need are too many to list: the roughly 8,200 tools on...
By Sam Partee, River Stevens, Thierry Damiba, Valerie Fanelle
arXiv:2610.23246v1 Announce Type: cross
Abstract: Reasoning systems increasingly improve answers by incorporating additional context into the model's input, from multi-agent debate, where agents read...
By Manh Nguyen, Sunil Gupta, Hung Le
arXiv:2610.22499v1 Announce Type: new
Abstract: Human communication is inherently multimodal, conveying intent not only through linguistic and vocal cues but also through visual signals. However, exi...
By Junseok Ahn, Jaehyun Park, Jeongsoo Choi, Joon Son Chung, Ji-Hoon Kim
arXiv:2511.11266v5 Announce Type: replace
Abstract: Vision-language models have recently emerged as promising planners for autonomous driving, where success hinges on reasoning over spatial structure...
By Fabian Schmidt, Markus Enzweiler, Abhinav Valada
arXiv:2610.22825v1 Announce Type: new
Abstract: World models have shown considerable promise in sequential decision-making by anticipating the consequences of actions and providing predictive foresig...
By Xiangteng Zhang, Haoyuan Liu, Yang Guan, Jingliang Duan, Yao Lyu, Tao Zhang, Shuai Zhang, Kehua Sheng, Bo Zhang, Shengbo Eben Li
arXiv:2610.22869v1 Announce Type: new
Abstract: LLM agents are increasingly integrated into real-world works, yet they must handle diverse domain-specific tools and complex multi-sub-task problems th...
By Bowen Ding, Qixin Wang, Lexing Ruan, Weiyi Lu, Qiwei Ge, Ruishan Fang, Tao Lin
arXiv:2610.23599v1 Announce Type: new
Abstract: Large language model (LLM) agents tackle complex tasks through reasoning and interaction with external environments. Agent skills provide persistent, t...
By Yewei Zhao, Derong Xu, Wei Chen, Yin Xu, Peilun Zhou, Xiaohan Wang, Guojun Yin, Wei Lin, Mingjun Xiao
arXiv:2610.23791v1 Announce Type: new
Abstract: Computational drug discovery requires agents to compose scientific operations and adapt to changing evidence and constraints. Here we introduce DrugHar...
By Yuxing Chen, Zhisheng Zhang, Cheng Tang, Wei Li, Minghui Zhang, Ziyan Huang, Shaocong Wei, Jiawei Chen, Junru Hu, Hongze Zhu, Jianghan Shen, Min Cen, Xinyuan Zhu, Yaxue Zhao, Jubo Wang, Yuqiang Li, Chun-Mei Feng, Zongyuan Ge, Rui Su, Jie Xu, Yirong Chen, Xi Xu, Ming Hu
arXiv:2605.28360v3 Announce Type: replace
Abstract: Automatic prompt optimization (APO) has driven significant gains in LLM-based agentic workflows. However, most existing methods treat each task's p...
By Jyotirmoy Nath, Neeraj Kumar, Brejesh Lall
arXiv:2307.10631v2 Announce Type: replace-cross
Abstract: The practice of code reuse is crucial in software development for a faster and more efficient development lifecycle. In reality, however, cod...
By Zhiwei Fu, Steven H. H. Ding, Furkan Alaca, Benjamin C. M. Fung, Philippe Charland
arXiv:2601.19747v2 Announce Type: replace-cross
Abstract: In the rapidly evolving field of Electronic Design Automation (EDA), the deployment of Large Language Models (LLMs) for Register-Transfer Lev...
By Jiale Liu, Taiyu Zhou, Tianqi Jiang
arXiv:2511.16080v2 Announce Type: replace-cross
Abstract: Modern data processing workflows frequently encounter ragged data: collections with variable-length elements that arise naturally in domains...
By Sungbin Moon, Jiho Park, Suyoung Hwang, Donghyun Koh, Seunghyun Moon, Minhyeong Lee
arXiv:2610.22980v1 Announce Type: new
Abstract: Large language model (LLM) agents are increasingly evaluated on long-horizon tasks, including autonomously running a business. Existing benchmarks for...
By Bhuvan Arora, Ankita Karmakar, Chaitanya Jain, Dilesh Churiwal, Harshit Jain, Shivansh Manish Dubey, Manish Prasad, Raja Piyush, Kshitij Tayal, Dhruv Kumar, Jagat Sesh Challa, Raunak Manekar, Yash Sinha
arXiv:2610.23062v1 Announce Type: new
Abstract: Multi-objective optimization models are often deployed in settings where decision makers (DMs) must balance some conflicting criteria. In practice, how...
By Gokhan Ceyhan
arXiv:2610.23205v1 Announce Type: new
Abstract: Large Language Models (LLMs) remain unreliable for extremely low-resource languages whose writing systems conflict with high-resource pretraining prior...
By Jingting Zheng (TJUNLP Lab, School of Computer Science and Technology, Tianjin University, China), Hong Ren (TJUNLP Lab, School of Computer Science and Technology, Tianjin University, China), Yuehan Cui (TJUNLP Lab, School of Computer Science and Technology, Tianjin University, China), Shaolin Zhu (TJUNLP Lab, School of Computer Science and Technology, Tianjin University, China), Deyi Xiong (TJUNLP Lab, School of Computer Science and Technology, Tianjin University, China)
arXiv:2610.23254v1 Announce Type: new
Abstract: Tool-using agents read untrusted external content, which exposes them to indirect prompt injection (IPI), where hidden instructions hijack the agent's...
By Farhan Zarif, Kazi Shahid Ahmed Galib, Synthia Akter Promi, Shabnam Sharmin Tarin, Ariyan Hossain, Sadif Ahmed, Farig Yousuf Sadeque
The article discusses a shift in code review practices, moving from a final gate to continuous review that functions like spellcheck during coding. It highlights how the Claude Code team uses the Claude AI to catch routine mistakes before human reviewers intervene, allowing developers to focus on higher-level issues. The piece references insights from Addy Osmani, Morgan Westlee Lunt, and Qing Wang on this evolving approach.