LoopBench: Discovering Emergent Symmetry Breaking Strategies with LLM Swarms
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arXiv
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| Main Authors: | , , , , |
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| Format: | Preprint |
| Published: |
2025
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| _version_ | 1866918249549004800 |
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| author | Parsaee, Ali Talebirad, Yashar Szepesvári, Csongor Ohal, Vishwajeet Redman, Eden |
| author_facet | Parsaee, Ali Talebirad, Yashar Szepesvári, Csongor Ohal, Vishwajeet Redman, Eden |
| contents | Large Language Models (LLMs) are increasingly being utilized as autonomous agents, yet their ability to coordinate in distributed systems remains poorly understood. We introduce \textbf{LoopBench}, a benchmark to evaluate LLM reasoning in distributed symmetry breaking and meta-cognitive thinking. The benchmark focuses on coloring odd cycle graphs ($C_3, C_5, C_{11}$) with limited colors, where deterministic, non-communicating agents fail in infinite loops. A strategy passing mechanism is implemented as a form of consistent memory. We show that while standard LLMs and classical heuristics struggle, advanced reasoning models (e.g., O3) devise strategies to escape deadlocks. LoopBench allows the study of emergent distributed algorithms based on language-based reasoning, offering a testbed for collective intelligence. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2512_13713 |
| institution | arXiv |
| publishDate | 2025 |
| record_format | arxiv |
| spellingShingle | LoopBench: Discovering Emergent Symmetry Breaking Strategies with LLM Swarms Parsaee, Ali Talebirad, Yashar Szepesvári, Csongor Ohal, Vishwajeet Redman, Eden Artificial Intelligence Machine Learning Multiagent Systems Large Language Models (LLMs) are increasingly being utilized as autonomous agents, yet their ability to coordinate in distributed systems remains poorly understood. We introduce \textbf{LoopBench}, a benchmark to evaluate LLM reasoning in distributed symmetry breaking and meta-cognitive thinking. The benchmark focuses on coloring odd cycle graphs ($C_3, C_5, C_{11}$) with limited colors, where deterministic, non-communicating agents fail in infinite loops. A strategy passing mechanism is implemented as a form of consistent memory. We show that while standard LLMs and classical heuristics struggle, advanced reasoning models (e.g., O3) devise strategies to escape deadlocks. LoopBench allows the study of emergent distributed algorithms based on language-based reasoning, offering a testbed for collective intelligence. |
| title | LoopBench: Discovering Emergent Symmetry Breaking Strategies with LLM Swarms |
| topic | Artificial Intelligence Machine Learning Multiagent Systems |
| url | https://arxiv.org/abs/2512.13713 |