Trust-Calibrated Reinforcement Learning for Adversarially Robust Multimodal Agents

Research question

Should agent security be learned as part of decision-making rather than bolted on as an external guardrail? Concretely: can an agent learn a calibrated estimate of how much to trust its own observations, and pay a real cost to VERIFY before it ACTs?

Methodology

Gate-first experimental sequence on MiniGrid-LavaGapS7 with seeded, reproducible PPO (CleanRL-style, 64x64 MLP actor-critic) under symbolic observation corruption (mask / noise / shuffle at controlled probabilities). Stage 1 establishes degradation curves and seen-vs-held-out transfer for clean- and corruption-trained policies. Only if the gate justifies it do later stages add an Oracle-Trust ceiling, a calibrated trust head (Brier / ECE), and priced protective actions (ACT / VERIFY / DEFER / ABSTAIN). Every run records a full research.v1 step trace and a provenance manifest; instrumentation is proven observationally inert by automated tests.

Published experiments

• Gate: corruption robustness (LavaGapS7) (MiniGrid-LavaGapS7-v0)

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