Symbolic Reasoning Frameworks Trigger Memory-Mediated Ecosystem Dynamics in Multi-Agent LLM Systems
Large language models exhibit a risk-averse "turtle" bias as strategic agents. We show that injecting a symbolic reasoning framework as a per-round reflective prompt into one agent acts as a small perturbation whose consequences are not per-decision but emergent: the agent's risk posture is unchanged in isolation, yet over a campaign of accumulating memory and multi-agent interaction the conditions settle into distinct, condition-associated winner ecosystems. In a 7-player Warring States Diploma
Record details
Published: 22 May 2026
Source: arXiv
Category: Research
Topics: Bias & fairness · Agents & autonomy
Retrieved: 14 July 2026
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ethics.ai (22 May 2026), “Symbolic Reasoning Frameworks Trigger Memory-Mediated Ecosystem Dynamics in Multi-Agent LLM Systems,” evidence record 3875, https://ethics.ai/record/3875 (originally published by arXiv).
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