Reasoning Capabilities of Large Language Models. Lessons Learned from General Game Playing
arXiv:2602.19160v1 Announce Type: new Abstract: This paper examines the reasoning capabilities of Large Language Models (LLMs) from a novel perspective, focusing on their ability to operate within formally specified, rule-governed environments. We evaluate four LLMs (Gemini 2.5 Pro and Flash variants, Llama 3.3 70B and GPT-OSS 120B) on a suite of forward-simulation tasks-including next / multistep state formulation, and legal action generation-across a diverse set of reasoning problems illustrated through General Game Playing (GGP) game instances. Beyond reporting instance-level performance, we characterize games based on 40 structural features and analyze correlations between these features and LLM performance. Furthermore, we investigate the effects of various game obfuscations to assess the role of linguistic semantics in game definitions and the impact of potential prior exposure of LLMs to specific games during training. The main results indicate that three of the evaluated model
arXiv:2602.19160v1 Announce Type: new Abstract: This paper examines the reasoning capabilities of Large Language Models (LLMs) from a novel perspective, focusing on their ability to operate within formally specified, rule-governed environments. We evaluate four LLMs (Gemini 2.5 Pro and Flash variants, Llama 3.3 70B and GPT-OSS 120B) on a suite of forward-simulation tasks-including next / multistep state formulation, and legal action generation-across a diverse set of reasoning problems illustrated through General Game Playing (GGP) game instances. Beyond reporting instance-level performance, we characterize games based on 40 structural features and analyze correlations between these features and LLM performance. Furthermore, we investigate the effects of various game obfuscations to assess the role of linguistic semantics in game definitions and the impact of potential prior exposure of LLMs to specific games during training. The main results indicate that three of the evaluated models generally perform well across most experimental settings, with performance degradation observed as the evaluation horizon increases (i.e., with a higher number of game steps). Detailed case-based analysis of the LLM performance provides novel insights into common reasoning errors in the considered logic-based problem formulation, including hallucinated rules, redundant state facts, or syntactic errors. Overall, the paper reports clear progress in formal reasoning capabilities of contemporary models.
Executive Summary
This article investigates the reasoning capabilities of Large Language Models (LLMs) by evaluating their performance on a suite of forward-simulation tasks within formally specified, rule-governed environments, using General Game Playing (GGP) game instances. The results indicate that three LLMs perform well across most experimental settings, with performance degradation observed as the evaluation horizon increases. Detailed case-based analysis provides insights into common reasoning errors, including hallucinated rules, redundant state facts, or syntactic errors. The study reports progress in formal reasoning capabilities of contemporary models.
Key Points
- ▸ The study evaluates the reasoning capabilities of four LLMs on forward-simulation tasks within GGP game instances.
- ▸ The results indicate that three LLMs perform well across most experimental settings, with performance degradation observed as the evaluation horizon increases.
- ▸ Detailed case-based analysis provides insights into common reasoning errors, including hallucinated rules, redundant state facts, or syntactic errors.
Merits
Strength in formal reasoning capabilities
The study demonstrates progress in formal reasoning capabilities of contemporary LLMs, which is a significant advancement in the field of AI research.
Demerits
Limited generalizability
The study's results may not be generalizable to all LLMs or domains, as the evaluation was limited to a specific set of GGP game instances.
Expert Commentary
This article makes a significant contribution to the field of AI research by evaluating the reasoning capabilities of LLMs within formally specified, rule-governed environments. The study's findings on common reasoning errors in LLMs provide valuable insights for the development of more robust and trustworthy LLMs. However, the study's limited generalizability and reliance on a specific set of GGP game instances are noteworthy limitations. Nevertheless, the study's results are a crucial step forward in the development of formal reasoning capabilities of LLMs, and the authors' use of obfuscation to assess the role of linguistic semantics in game definitions is a novel and insightful approach. Overall, the study provides a solid foundation for future research on LLMs and their applications in various domains.
Recommendations
- ✓ Future studies should aim to replicate the study's findings across a broader range of LLMs and domains to improve generalizability.
- ✓ The development of more robust and trustworthy LLMs requires further research on explainability, adversarial testing, and formal reasoning capabilities.
