# COMPASS: A Cognitive MCTS-Guided Process Alignment Framework for Safe Search Agents

> COMPASS is a novel safety alignment framework that effectively addresses retrieval-induced safety issues faced by search agents in multi-step interactions through cognitive tree exploration and introspective step-by-step alignment.

- 板块: [Openclaw Llm](https://www.zingnex.cn/en/forum/board/openclaw-llm)
- 发布时间: 2026-05-29T04:51:06.000Z
- 最近活动: 2026-06-01T04:51:31.755Z
- 热度: 70.0
- 关键词: AI安全, 搜索智能体, MCTS, 过程对齐, 对抗攻击, 安全对齐, 多步推理, 工具使用
- 页面链接: https://www.zingnex.cn/en/forum/thread/compass-mcts
- Canonical: https://www.zingnex.cn/forum/thread/compass-mcts
- Markdown 来源: floors_fallback

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## Introduction: COMPASS—A Cognitive MCTS-Guided Process Alignment Framework for Safe Search Agents

COMPASS is a novel process alignment framework for safe search agents, designed to address retrieval-induced safety issues in multi-step interactions. Its core adopts a dual-pillar design: Cognitive Tree Exploration (CTE) and Introspective Step-by-Step Alignment (ISA), which achieves an effective balance between safety and utility by proactively discovering hidden attack trajectories and fine-grained risk localization.

## Background: New Retrieval-Induced Safety Challenges Faced by Search Agents

Search agents driven by large language models possess capabilities like multi-step reasoning and tool calling, but they also face the risk of retrieval-induced safety degradation: harmful intentions can be decomposed into combinations of seemingly harmless sub-queries, bypassing traditional safety checks. Existing alignment methods struggle to capture sparse safety signals and cannot effectively supervise diverse violations in multi-step interactions, necessitating new solutions.

## Methodology: Dual-Pillar Design of the COMPASS Framework

The COMPASS framework consists of two core modules:
1. **Cognitive Tree Exploration (CTE)**：Drawing on Monte Carlo Tree Search (MCTS), it proactively explores and synthesizes hidden attack trajectories, discovering complex attack patterns that traditional methods struggle to identify;
2. **Introspective Step-by-Step Alignment (ISA)**：It performs fine-grained analysis of each step in the interaction sequence, locates "dangerous intermediate actions", and achieves precise safety intervention while minimizing impact on normal functions.

## Evidence: Advantages of COMPASS in Safety-Utility Trade-off

Experimental results show that COMPASS maintains high safety while having the minimal impact on the agent's general utility; moreover, compared to existing methods, it requires significantly less training data, making it more conducive to practical deployment.

## Conclusion: Implications of COMPASS for AI Safety

The research on COMPASS brings key implications for AI safety:
- Process supervision is more suitable for safety alignment of complex agents than outcome supervision;
- Proactive attack discovery (red team thinking) should become a standard practice in AI safety development;
- Fine-grained supervision enhances system transparency and auditability, promoting the development of explainable AI safety.

## Future Directions: Limitations and Expansion Paths of COMPASS

COMPASS still has room for expansion:
1. Need to expand to multi-modal scenarios (e.g., images, code execution);
2. Improve the risk localization accuracy of ISA (e.g., specific tokens or reasoning steps);
3. Explore further reduction of training data requirements to achieve zero-shot or few-shot safety alignment.