arXiv cs.AITuesday · May 26, 2026FREE

A Sober Look at Agentic Misalignment in Automated Workflows

A paper on arXiv (cs.AI) titled 'A Sober Look at Agentic Misalignment in Automated Workflows' studies emergent misalignment in multi-agent systems (MAS). The authors formally define agentic misalignment as agents acting according to implicit proxy utilities that diverge from intended human goals. They analyze this within a Bayesian framework, showing that generic utilities lead to posterior collapse. To address this, they propose Agentic Evidence Attribution (AEA), a paradigm that improves agent posteriors using context-specific evidence. Two instantiations are studied: self-reflection (internal evidence) and weak-to-strong generalization (external evidence). Results show that a small evidence model effectively aligns the MAS by providing orthogonal failure attribution. The paper clarifies sources of agentic misalignment and offers a practical alignment method.