LLM-based Search Assistant with Holistically Guided MCTS for Intricate Information Seeking (SIGIR 2025)
This is a forum index entry for an academic paper accepted at SIGIR 2025, listed in the *Search Assistance* section of an awesome-list style collection.
Paper Details
| Field | Content | |---|---| | Title | LLM-based Search Assistant with Holistically Guided MCTS for Intricate Information Seeking | | Venue | SIGIR 2025 | | Source | https://dl.acm.org/doi/abs/10.1145/3726302.3730025 | | Type | Academic paper | | Category | Search Assistance |
Key points
- The paper proposes an LLM-based search assistant that uses Monte Carlo Tree Search (MCTS) with a holistic guidance mechanism to handle intricate (complex, multi-step) information seeking tasks.
- It targets the problem of multi-hop, open-domain information acquisition, where a single retrieval pass is insufficient and the agent must decide whether, when, and how many times to retrieve.
- The work fits into the agentic search paradigm: rather than a fixed retrieve–rerank–generate pipeline, retrieval actions become part of a sequential decision-making process governed by an inference-time search strategy.
- Asking Clarification Questions to Handle Ambiguity in Open-Domain QA
- Asking Clarifying Questions in Open-Domain Information-Seeking Conversations
- DiAL: Diversity-Aware Listwise Ranking for Query Auto-Complete (EMNLP)
- Enhancing Discoverability in Enterprise Conversational Systems
- Evaluating Auto-Complete Ranking for Diversity and Relevance (ECIR 2025)
- Evaluation and Continual Improvement for an Enterprise AI Assistant
Context Within the Field
The post situates the paper in the evolution of neural information retrieval: from BM25 and dense bi-encoders, to cross-encoder reranking, generative retrieval, and finally LLM agents with tool calling. In the LLM era, the new design variables are the inference budget and the action space (whether to retrieve, how many retrieval steps to take, and which tools to invoke). MCTS-style planning is one approach to allocating that budget adaptively.
Engineering Deployment Checklist (from the post)
| Item | Question | Suggestion | |---|---|---| | Data | Does training/indexing contain PII? Version control? | Partitioned indexes, anonymization, rollback-capable embedding versions | | Latency | What is the p99 budget? How many retrieval steps? | Cascaded retrieval with early stopping, query caching, async reranking | | Quality | Do offline gains translate to online CTR/satisfaction? | Interleaving experiments, human audits, citation verification | | Safety | Does open retrieval introduce poisoning/bias? | Source allowlists, adversarial detection, output filtering | | Cost | Token and GPU cost per query? | Small-model routing, distillation, hybrid sparse+dense retrieval |
Limitations and Caveats
The forum post is an index-style entry and does not reproduce the paper's abstract, experiments, or quantitative results. General limitations noted for work in this area include benchmark scale constraints, mismatch between benchmarks and real user distributions, and safety risks of agentic systems on the open web. Readers should verify exact methods and numbers against the original PDF at the ACM DOI link above.