arXiv:2605.30838v1 Announce Type: new Abstract: LLM-powered search agents enable multi-step reasoning and tool use. However, these capabilities introduce retrieval-induced safety degradation, as harmful intents may decompose into seemingly innocuous sub-queries that lead to unsafe outcomes. Existing alignment methods struggle to capture sparse safety signals and fail to supervise diverse violations across multi-step interactions. We propose COMPASS, a Cognitive MCTS-Guided Process Alignment framework designed to achieve robust safety alignment throughout the agent workflow while preserving gen
As LLM-powered search agents become more sophisticated, the challenge of 'retrieval-induced safety degradation' becomes a pressing concern, necessitating advanced alignment methods.
This work directly addresses a critical safety and reliability bottleneck for autonomous AI agents, determining their trustworthiness and broad deployability.
The proposed COMPASS framework introduces a cognitive, MCTS-guided alignment method that could significantly improve the safety and robustness of multi-step AI agents.
- · AI development firms
- · Cloud service providers
- · AI safety researchers
- · Users of AI search agents
- · Malicious actors leveraging AI vulnerabilities
- · AI systems with poor safety alignment
- · Less robust AI alignment methodologies
Safer and more reliable AI agents could be deployed in sensitive applications.
Increased public and regulatory trust in autonomous AI systems could accelerate adoption across various industries.
The enhanced safety could lead to more sophisticated and potentially mission-critical AI agent deployments, transforming white-collar and operational workflows.
This signal links to a primary source. Continuum Brief monitors and indexes it as part of the live intelligence stream — we do not republish source content.
Read at arXiv cs.AI