Symbolic Reasoning Frameworks Modulate LLM Risk Aversion in Multi-Agent Strategic Settings

arXiv:2606.07552v1 Announce Type: cross
Abstract: Large language models exhibit innate behavioral tendencies when deployed as strategic agents — notably a risk-averse “turtle” bias toward defensive play. We show that symbolic reasoning frameworks, injected as per-round reflective prompts into one agent, differentially modulate this bias and reshape the multi-agent ecosystem to produce framework-specific winner distributions. In a 7-player Warring States Diplomacy variant (41 games, 4 conditions, single-campaign memory accumulation), each framework produces a distinct ecosystem signature: under control, Yan dominates (7/11, 64%); under I-Ching yarrow divination, Yan and Chu co-dominate while Qin is completely suppressed (0/10); under Tarot, Qin dominates (5/10, Fisher vs. pooled p = 0.006); under scrambled-text ablation (incoherent oracle text preserving prompt structure), Qi dominates (5/10, Fisher vs. pooled p = 0.006). The framework-receiving agent (Han) never wins and shows no survival difference across conditions (Fisher p = 1.0), but Tarot consistently elevates Han’s peak territory (mean 3.0 SCs vs. 2.1-2.5 others, Kruskal-Wallis p = 0.010). Neither framework’s content predicts subsequent actions — hexagram themes (chi-squared p = 0.95) and Tarot card postures (chi-squared p = 0.69) are both independent of action choice — suggesting the modulation operates through the reflective process, not content-following. We present this as an observation paper establishing that alignment-framework choice at the agent level produces distinctive system-level consequences in multi-agent settings.