LoopBench: Discovering Emergent Symmetry Breaking Strategies with LLM Swarms
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.
Code (0)
등록된 구현이 없습니다.
Similar Papers 제목 키워드 기반
Discovering Symmetry Breaking in Physical Systems with Relaxed Group Convolution
Modeling symmetry breaking is essential for understanding the fundamental changes in the behaviors and properties of physical systems, from microscopic particle interactions to macroscopic phenomena like fluid dynamics a…
Super-ResolutionLoopGuard: Breaking Self-Reinforcing Attention Loops via Dynamic KV Cache Intervention
Through systematic experiments on long-context generation, we observe a damaging failure mode in which decoding can collapse into persistent repetition loops. We find that this degeneration is driven by collapsed attenti…
Optimizing over trained GNNs via symmetry breaking
Optimization over trained machine learning models has applications including: verification, minimizing neural acquisition functions, and integrating a trained surrogate into a larger decision-making problem. This paper f…
Cell motility modes are selected by the interplay of mechanosensitive adhesion and membrane tension
The initiation of directional cell motion requires symmetry breaking that can happen both with or without external stimuli. During cell crawling, forces generated by the cytoskeleton and their transmission through mechan…
A Unified Framework to Enforce, Discover, and Promote Symmetry in Machine Learning
Symmetry is present throughout nature and continues to play an increasingly central role in physics and machine learning. Fundamental symmetries, such as Poincar\'{e} invariance, allow physical laws discovered in laborat…
image-classificationImage Classification