The paper introduces a text-based, multi-agent version of the board game Clue to test multi-step deductive reasoning in large language models (LLMs). Six LLM-based agents (GPT‑4o‑mini and Gemini‑2.5‑Flash) play turn‑based games, and a tool‑augmented approach uses a structured possibility matrix to convert implicit game state into explicit remaining possibilities, thereby offloading memory and deductive constraints from the agents. The study compares this tool‑augmented method against a baseline to assess its impact on reasoning quality and task success in a strategic reasoning environment.
By Rebecca Ansell, Autumn Toney-Wails
arXiv:2609.05459v1 Announce Type: new
Abstract: Reinforcement learning and large language models often struggle to accurately capture the causal mechanics of game environments. Standard reinforcement...
By Mohit Jiwatode, Bodo Rosenhahn, Alexander Dockhorn
The paper presents a symbolic sub‑policy model for chess that incorporates domain knowledge to improve interpretability. By adapting patterns learned through inductive logic programming (PAL), the authors evaluate the model using a divergence metric against a random baseline and show that it can suggest moves comparable to a human beginner. They also propose a computational evaluation scheme that augments an existing engine with their model.
By Abhijeet Krishnan, Chris Martens
arXiv:2607. 22732v1 Announce Type: new Abstract: LLM-based game agents often perform poorly on more complex tasks.
By Mohit Jiwatode, Ronja Fuchs, Robin Schm\"ocker, Bodo Rosenhahn, Alexander Dockhorn
arXiv:2503. 19990v4 Announce Type: replace Abstract: Many real-world applications of spatial intelligence, such as robotic control, autonomous driving, and automated assembly, require spatial reasoning across multiple sequential steps.
By Kexian Tang, Junyao Gao, Yanhong Zeng, Haodong Duan, Yanan Sun, Zhening Xing, Wenran Liu, Kai Chen, Kaifeng Lyu
The paper proposes a method that mimics human cognition to train reinforcement learning agents to generate explainable strategies and policies as executable procedures derived from sequences of gameplay actions. It demonstrates automatic learning of such programs for chess and a grid‑based environment, showing that the resulting strategies produce effective actions and can be learned from gameplay data.
By Abhijeet Krishnan, Colin M. Potts, Arnav Jhala, Harshad Khadilkar, Shirish Karande, Chris Martens