SimpleDeepSearcher: Deep Information Seeking via Web-Powered Reasoning Trajectory Synthesis (arXiv, May 2025)

Forum topic · 小凯 · 2026-07-05

Summary

This forum post catalogs SimpleDeepSearcher, a May 2025 arXiv paper (arXiv:2505.16834) on deep information seeking through web-powered reasoning trajectory synthesis. Authored by Shuang Sun, Huatong Song, Yuhao Wang, Ruiyang Ren, Jinhao Jiang, Junjie Zhang and 13 co-authors, the work addresses how large language models can perform multi-hop, iterative web research rather than one-shot retrieval. The post frames the paper within the LLM-era search stack, discussing how reasoning budgets and action spaces (whether to retrieve, how many times, which tools to call) are becoming learnable components. It situates SimpleDeepSearcher among related surveys on Deep Research systems, LLM-based search agents, and agentic reasoning, and provides engineering checklists covering latency budgets, retrieval depth, caching, safety filtering, and per-query cost. Note that the post itself is largely boilerplate: it includes a generic IR background, generic evaluation protocols (MS MARCO, BEIR, nDCG@10, Recall@k), and placeholder summaries rather than detailed results from the paper. Quantitative findings should be verified against the original PDF. Tags: deep research, retrieval-augmented generation, trajectory synthesis, LLM agents.

Overview

This zhichai.net entry catalogs SimpleDeepSearcher: Deep Information Seeking via Web-Powered Reasoning Trajectory Synthesis, a May 2025 arXiv paper.

| Field | Value | |-------|-------| | Authors | Shuang Sun, Huatong Song, Yuhao Wang, Ruiyang Ren, Jinhao Jiang, Junjie Zhang, et al. (13 authors) | | Published | May 2025 | | Source | https://arxiv.org/abs/2505.16834 | | Type | Academic paper | | Section | Deep Research |

Key points

Core idea: Synthesize reasoning trajectories for deep information seeking by leveraging web-powered search, enabling LLMs to perform iterative, multi-hop research rather than single-shot retrieval.
Positioning: The paper sits at the intersection of information retrieval and LLM agents, addressing how to redistribute responsibilities among retrieval, ranking, generation, and tool calling in the LLM era — where the new variables are *reasoning budget* and *action space* (whether to retrieve, how many times, which tools to invoke).
Related context: Evolution from BM25 → dense bi-encoders → cross-encoders → late interaction → generative retrieval → LLM agents; RAG and agentic search extend external knowledge access into an iterative, verifiable, plannable process.

Editorial caveats

This forum entry is largely boilerplate/templated content: the one-line summary, method outline, and experiment sections contain placeholders or generic IR background rather than the paper's actual details. The listed evaluation setup (MS MARCO, BEIR, Natural Questions; nDCG@10, MRR, Recall@k) and comparison baselines (BM25, dense retrieval, rerankers, no-retrieval LLMs) reflect the template's standard protocol, not confirmed specifics of this paper.

Verify all quantitative claims against the original PDF before citing.

Practical engineering checklist (from the post)

| Item | Question | Suggestion | |------|----------|------------| | Data | PII in training/index? versioning? | Partitioned indexes, anonymization, rollback-safe embeddings | | Latency | p99 budget? retrieval steps? | Cascades + early stopping, query caching, async reranking | | Quality | Do offline gains translate online? | Interleaving experiments, human audits, citation verification | | Safety | Open-web poisoning/bias? | Source whitelists, adversarial detection, output filtering | | Cost | Tokens/GPU per query? | Small-model routing, distillation, hybrid sparse+dense |