Adversarial Tool Trust Calibration via Collaborative Self-Play

Eisenstein et al. teach agents when to trust tools via group rewards, but typical tools are assumed to be honest-if-noisy. Inspired by Audit-LLM’s evidence-based multi-agent debate (Song et al., 2024) and VipAct’s expert-tool orchestration (Zhang et al., 2024), this project turns tool reliability into an adversary: some tools are intermittently corrupted, biased, or lagging. Self-play is extended with a reward for (i) identifying inconsistent tool outputs, (ii) seeking corroboration from redundant tools or teammates, and (iii) abstaining when evidence conflicts. This differs from supervised fine-tuning because the failures are endogenous to the interaction and adapt to the policy. The novelty is treating tool trust as an online hypothesis test under budget and correctness constraints. If successful, single agents distilled from these societies would show markedly improved calibration, robust tool auditing, and selective prediction under distribution shift.

References:

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