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: unde
The rapid advancement of large language models necessitates deeper understanding and control over their strategic behaviors, especially as they move into more autonomous roles.
This research provides a mechanism to modulate AI agent behavior, potentially allowing for fine-tuned control over risk profiles in complex multi-agent systems and preventing undesirable 'turtle' biases.
The ability to inject symbolic reasoning frameworks 'on the fly' means that AI agents' strategic predispositions are no longer fixed, but can be dynamically reshaped, offering greater steerability.
- · AI developers
- · Organizations deploying AI agents in strategic roles
- · AI safety researchers
- · Uncontrolled or poorly designed AI systems
- · Traditional fixed-strategy AI agents
AI agents can be made more (or less) risk-averse depending on policy, leading to specific strategic outcomes in competitive environments.
This control could enable the creation of diverse AI agent 'personalities' or roles within larger AI ecosystems, optimizing for various objectives beyond simple win-loss conditions.
The development of symbolic reasoning frameworks becomes a critical differentiator, shaping the strategic capabilities and ethical alignment of advanced AI systems.
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Read at arXiv cs.LG