# SearchAtlas: Analyzing Agentic Search Strategies via Evidential Query Graphs

arXiv: https://arxiv.org/abs/2609.10901 · HTML: https://mlsanity.com/paper/2609.10901/

## Key facts

- No citing papers in the map yet (data through 14 Oct 2026).
- Placed in Web, search, and research agents, one of 16 directions in LLM agents and tool use.
- Builds on 30 of its 38 references that are in the map.
- First posted on arXiv 9 Sep 2026.

## Placed in

- LLM agents and tool use / [Web, search, and research agents](https://mlsanity.com/agents/web-search-and-research-agents/)

## Builds on (most cited first, 30 of 30)

- [ReAct: Synergizing Reasoning and Acting in Language Models](https://mlsanity.com/paper/2210.03629/) (2022)
- [HotpotQA: A Dataset for Diverse, Explainable Multi-hop Question Answering](https://mlsanity.com/paper/1809.09600/) (2018)
- [Lost in the Middle: How Language Models Use Long Contexts](https://mlsanity.com/paper/2307.03172/) (2023)
- [WebArena: A Realistic Web Environment for Building Autonomous Agents](https://mlsanity.com/paper/2307.13854/) (2023)
- [GAIA: a benchmark for General AI Assistants](https://mlsanity.com/paper/2311.12983/) (2023)
- [Interleaving Retrieval with Chain-of-Thought Reasoning for Knowledge-Intensive Multi-Step Questions](https://mlsanity.com/paper/2212.10509/) (2022)
- [BrowseComp: A Simple Yet Challenging Benchmark for Browsing Agents](https://mlsanity.com/paper/2504.12516/) (2025)
- [DeepResearch Bench: A Comprehensive Benchmark for Deep Research Agents](https://mlsanity.com/paper/2506.11763/) (2025)
- [WebSailor: Navigating Super-human Reasoning for Web Agent](https://mlsanity.com/paper/2507.02592/) (2025)
- [WebWalker: Benchmarking LLMs in Web Traversal](https://mlsanity.com/paper/2501.07572/) (2025)
- [Fact, Fetch, and Reason: A Unified Evaluation of Retrieval-Augmented Generation](https://mlsanity.com/paper/2409.12941/) (2024)
- [DeepSearchQA: Bridging the Comprehensiveness Gap for Deep Research Agents](https://mlsanity.com/paper/2601.20975/) (2026)
- [DeepResearch Bench II: Diagnosing Deep Research Agents via Rubrics from Expert Reports](https://mlsanity.com/paper/2601.08536/) (2026)
- [AgentProcessBench: Diagnosing Step-Level Process Quality in Tool-Using Agents](https://mlsanity.com/paper/2603.14465/) (2026)
- [Serial Position Effects of Large Language Models](https://mlsanity.com/paper/2406.15981/) (2024)
- [Reinforced Internal-External Knowledge Synergistic Reasoning for Efficient Adaptive Search Agent](https://mlsanity.com/paper/2505.07596/) (2025)
- [Flash-Searcher: Fast and Effective Web Agents via DAG-Based Parallel Execution](https://mlsanity.com/paper/2509.25301/) (2025)
- [Beyond the Final Answer: Evaluating the Reasoning Trajectories of Tool-Augmented Agents](https://mlsanity.com/paper/2510.02837/) (2025)
- [Real-time Factuality Assessment from Adversarial Feedback](https://mlsanity.com/paper/2410.14651/) (2024)
- [ReasoningFlow: Semantic Structure of Complex Reasoning Traces](https://mlsanity.com/paper/2506.02532/) (2025)
- [RE-TRAC: REcursive TRAjectory Compression for Deep Search Agents](https://mlsanity.com/paper/2602.02486/) (2026)
- [Hybrid Reward Normalization for Process-supervised Non-verifiable Agentic Tasks](https://mlsanity.com/paper/2509.25598/) (2025)
- [S-DAG: A Subject-Based Directed Acyclic Graph for Multi-Agent Heterogeneous Reasoning](https://mlsanity.com/paper/2511.06727/) (2025)
- [WebGraphEval: Multi-Turn Trajectory Evaluation for Web Agents using Graph Representation](https://mlsanity.com/paper/2510.19205/) (2025)
- [AdaSearch: Balancing Parametric Knowledge and Search in Large Language Models via Reinforcement Learning](https://mlsanity.com/paper/2512.16883/) (2025)
- [Where Do Deep-Research Agents Go Wrong? Span-Level Error Localization in Agent Trajectories](https://mlsanity.com/paper/2606.02060/) (2026)
- [DAG-Math: Graph-of-Thought Guided Mathematical Reasoning in LLMs](https://mlsanity.com/paper/2510.19842/) (2025)
- [When Is Enough Not Enough? Illusory Completion in Search Agents](https://mlsanity.com/paper/2602.07549/) (2026)
- [Beyond Outcome Reward: Decoupling Search and Answering Improves LLM Agents](https://mlsanity.com/paper/2510.04695/) (2025)
- [K-BrowseComp: A Web Browsing Agent Benchmark Grounded in Korean Contexts](https://mlsanity.com/paper/2606.02404/) (2026)

## Abstract (arXiv)

LLM search agents are often evaluated on final-answer accuracy, overlooking the process. Analyzing a search strategy requires understanding how credible evidence is retrieved to address question constraints. This valuable information is buried in raw search trajectories that are long and difficult to parse. We introduce SearchAtlas, a framework that converts search trajectories into structured graphs whose edges represent how evidence is propagated across the reasoning trace, from the query that retrieves it to the final answer. Our automated parsing pipeline achieves a mean edge F1 of 86.0% against human-annotated graphs and remains consistent across repeated runs. We analyze five search agents on three benchmarks, revealing systematic differences in search scale and evidence aggregation. SearchAtlas exposes fragmented answer support, question constraints that do not reach the answer, and unverified parametric knowledge entering the response. These process failures are strongly associated with incorrect answers, even more so than an LLM judge given either the raw trajectory or the ordered query list, suggesting that the constructed graphs provide useful interpretability. Moreover, an audit of cases in which process-diagnostic scores disagree with final-answer correctness shows that they capture information not reducible to answer accuracy.