Holistically Guided Monte Carlo Tree Search for Intricate Information Seeking

2025-02-07

AI Summary

To address the challenge of jointly optimizing local precision and global semantic coherence in complex, multi-step, heterogeneous information retrieval tasks, this paper proposes HG-MCTS, a large language model–based search assistant that formalizes retrieval as a knowledge-augmented, progressive information gathering process. We introduce an adaptive checklist-guided Monte Carlo Tree Search (MCTS) framework, integrating a multi-faceted sparse reward mechanism that jointly accounts for exploration, retrieval efficacy, and progress awareness. Furthermore, we incorporate dynamic subgoal modeling and cross-step knowledge caching to enhance long-horizon reasoning and reduce redundancy. Evaluated on realistic, complex retrieval benchmarks, HG-MCTS achieves significant improvements in knowledge coverage completeness and answer accuracy, while substantially reducing search path redundancy. It consistently outperforms state-of-the-art baselines across all key metrics, demonstrating superior capability in balancing fine-grained relevance with holistic semantic understanding.

Technology Category

• Search and Optimization: Metareasoning and Metaheuristics
• Data Mining & Knowledge Management: Conversational Systems for Recommendation & Retrieval
• Constraint Satisfaction and Optimization: Search

Application Category

• Search and Retrieval-Augmented AI: Agentic search
• User Modeling, Personalization and Recommendation: Fairness-aware retrieval and ranking
• Semantics and Knowledge: Methods to enhance, augment, integrate or synergize semantic models such as knowledge graphs and LLMs

Abstract

In the era of vast digital information, the sheer volume and heterogeneity of available information present significant challenges for intricate information seeking. Users frequently face multistep web search tasks that involve navigating vast and varied data sources. This complexity demands every step remains comprehensive, accurate, and relevant. However, traditional search methods often struggle to balance the need for localized precision with the broader context required for holistic understanding, leaving critical facets of intricate queries underexplored. In this paper, we introduce an LLM-based search assistant that adopts a new information seeking paradigm with holistically guided Monte Carlo tree search (HG-MCTS). We reformulate the task as a progressive information collection process with a knowledge memory and unite an adaptive checklist with multi-perspective reward modeling in MCTS. The adaptive checklist provides explicit sub-goals to guide the MCTS process toward comprehensive coverage of complex user queries. Simultaneously, our multi-perspective reward modeling offers both exploration and retrieval rewards, along with progress feedback that tracks completed and remaining sub-goals, refining the checklist as the tree search progresses. By striking a balance between localized tree expansion and global guidance, HG-MCTS reduces redundancy in search paths and ensures that all crucial aspects of an intricate query are properly addressed. Extensive experiments on real-world intricate information seeking tasks demonstrate that HG-MCTS acquires thorough knowledge collections and delivers more accurate final responses compared with existing baselines.

Problem

• Optimize complex information retrieval
• Balance localized precision with holistic context
• Enhance accuracy in multi-step web searches

Innovation

• Holistically Guided Monte Carlo Tree Search
• Adaptive Checklist for Sub-Goals
• Multi-Perspective Reward Modeling