Shanyong Wang

📣 Our paper is accepted to Findings of EMNLP 2025! Many thanks to all the co-authors! 🌍 Math modeling is the perfect lens for agents to approach the real world challenges. Come and check how we do it: arxiv.org/pdf/2505.15068

Such a memorable experience working alongside amazing collaborators! As my first publication, I'm delighted to see our hard work come to fruition! Thanks again for @xiusi_chen @qiancheng1231 @HongruWang007!

📣 Our paper is accepted to Findings of EMNLP 2025! 📷 Decision Modeling is the process of formulating an abstract representation of a decision scenario by identifying key variables, their attributes, relevant constraints, and possible courses of action, in order to evaluate trade-offs and arrive at the most rational and explainable outcome. Many thanks to all the co-authors! @Swimmingwang04 @qiancheng1231 @HongruWang007 @peixuanhakhan @hengjinlp Come and check how we do it: arxiv.org/pdf/2505.21397

Can LLMs make rational decisions like human experts? 📖Introducing DecisionFlow: Advancing Large Language Model as Principled Decision Maker We introduce a novel framework that constructs a semantically grounded decision space to evaluate trade-offs in hard decision-making scenarios transparently. 📑Paper: arxiv.org/abs/2505.21397 💻Code: github.com/xiusic/Decisio… 🧵👇

What’s is the agent? What is the optimal behavior to achieve the predefined goal? And how to learn that behavior policy? We formally introduce a systematic Theory of Agent (ToA), analogous to the cognitive framework of Theory of Mind (ToM). Where ToM refers to the ability to attribute mental states (e.g., beliefs, intentions, knowledge) to oneself and others, enabling the prediction and interpretation of behavior, ToA characterizes an agent’s capacity to model not only external environments but also its own internal knowledge state to make decisions and complete the goal. We provide new definition, three specific principles to guide the optimal behavior of agent where the keys lies in the alignment between knowledge boundary with the decision boundary. More importantly, we provide an actionable roadmap to agent foundation model. Almost everything you need for the agent can be found in the paper: arxiv.org/pdf/2506.00886

🚀 Can we cast reward modeling as a reasoning task? 📖 Introducing our new paper: RM-R1: Reward Modeling as Reasoning 📑 Paper: arxiv.org/pdf/2505.02387 💻 Code: github.com/RM-R1-UIUC/RM-… Inspired by recent advances of long chain-of-thought (CoT) on reasoning-intensive tasks, we hypothesize and validate that integrating reasoning capabilities into reward modeling significantly enhances RM's interpretability and performance. RM-R1 achieves state-of-the-art or near state-of-the-art performance of generative RMs on RewardBench, RM-Bench and RMB. 🧵👇