LLM-based Search Assistant with Holistically Guided MCTS for Intricate Information Seeking (SIGIR 2025)
This entry summarizes a SIGIR 2025 paper from the Search Assistance track: *LLM-based Search Assistant with Holistically Guided MCTS for Intricate Information Seeking*.
- Publication: SIGIR 2025 (ACM)
- Source: https://dl.acm.org/doi/abs/10.1145/3726302.3730025
- Resource type: Academic paper
- The paper proposes an LLM-based search assistant that applies Monte Carlo Tree Search (MCTS), guided by holistic signals, to complex information-seeking tasks.
- It responds to a long-standing challenge in information retrieval: traditional pipelines treat retrieval, ranking, and generation as separate stages, which limits support for multi-hop reasoning, iterative querying, and natural-language interaction.
- By modeling search as sequential decision-making, the system can plan actions—whether to retrieve, how many steps to take, and which tools to invoke—rather than relying on fixed single-pass retrieval.
- The work sits at the intersection of agentic search and retrieval-augmented generation (RAG), where retrieval strategy itself becomes a learnable component.
- Asking Clarification Questions to Handle Ambiguity in Open-Domain QA
- Asking Clarifying Questions in Open-Domain Information-Seeking Conversations
- Enhancing Discoverability in Enterprise Conversational Systems
- Evaluation and Continual Improvement for an Enterprise AI Assistant
- Original paper: *LLM-based Search Assistant with Holistically Guided MCTS for Intricate Information Seeking*, SIGIR 2025. https://dl.acm.org/doi/abs/10.1145/3726302.3730025
Key points
Context within the field
Neural information retrieval has evolved from BM25 through dense bi-encoder retrieval and cross-encoder reranking toward generative retrieval and LLM agents. Each generation balances the efficiency–effectiveness–maintainability triangle. Agentic approaches such as this one extend external knowledge access from one-shot retrieval to an iterative, plannable, verifiable process, shifting evaluation from static metrics (e.g., nDCG) toward task success rate, citation accuracy, and multi-hop reasoning-chain completeness.
Typical evaluation setup
Work in this area is generally evaluated on datasets such as MS MARCO, BEIR, and Natural Questions, using metrics like nDCG@10, MRR, Recall@k, task success rate, latency, and token cost, against baselines including BM25, dense retrieval, cross-encoder reranking, and retrieval-free LLMs. Exact numbers for this paper should be taken from the original PDF.
Practical takeaways
1. Architecture: Cascade retrieve–rerank–generate remains mainstream, but agentic paradigms make retrieval count and strategy learnable. 2. Engineering: Latency budgets, retrieval step limits, early stopping, and caching are hard constraints for real deployment. 3. Safety: Open retrieval introduces poisoning and bias risks; source allowlists and output filtering are advisable. 4. Evaluation: Offline metric gains should be cross-checked with interleaving experiments and human audits.