Beyond Reward Suppression: Reshaping Steganographic Communication Protocols in MARL via Dynamic Representational Circuit Breaking
In decentralized Multi-Agent Reinforcement Learning (MARL), steganographic collusion -- where agents develop private protocols to evade monitoring -- presents a critical AI safety threat. Existing defenses, limited to behavioral or reward layers, fail to detect coordination in latent communication channels. We introduce the Dynamic Representational Circuit Breaker (DRCB), an architectural defense operating at the optimization substrate. Building on the AI Mother Tongue (AIM) framework, DRCB util
Record details
Published: 7 March 2026
Source: arXiv
Category: Research
Topics: Safety & alignment · Military & security · Agents & autonomy
Retrieved: 14 July 2026
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ethics.ai (7 March 2026), “Beyond Reward Suppression: Reshaping Steganographic Communication Protocols in MARL via Dynamic Representational Circuit Breaking,” evidence record 7564, https://ethics.ai/record/7564 (originally published by arXiv).
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