pith. sign in

arxiv: 2606.07689 · v1 · pith:34H3JFUMnew · submitted 2026-06-05 · 💻 cs.CV

Struct-Searcher: Agentic Structural Thinking Advances Multimodal Deep Information Seeking

Fan Zhang , Vireo Zhang , Shengju Qian , Haoxuan Li , Zheng Lian , Hao Wu , Yuan Gao , Xinyu Geng
show 2 more authors
Xin Wang Pheng-Ann Heng
This is my paper

Pith reviewed 2026-06-27 22:19 UTC · model grok-4.3

classification 💻 cs.CV
keywords multimodal information seekingagentic workflowstructural graphbelief revisionconflict-aware reasoningvision-language modelsplug-and-play agent
0
0 comments X

The pith

Struct-Searcher maintains an evolving multimodal structural graph to handle contradictions during deep information seeking.

A machine-rendered reading of the paper's core claim, the machinery that carries it, and where it could break.

Existing agentic workflows for multimodal research accumulate evidence linearly and lack ways to manage contradictions across text, images, and other sources. Struct-Searcher replaces this with a workflow grounded in belief revision theory that keeps an evolving structural graph of the gathered information. The graph is meant to surface conflicts explicitly and support reasoned updates. The method is presented as plug-and-play with any backbone model. If the approach works, it would allow agents to reach more accurate answers on complex queries that draw on heterogeneous online data.

Core claim

Struct-Searcher is a structural agentic workflow grounded in belief revision theory that explicitly maintains an evolving multimodal structural graph throughout the reasoning process, enabling effective conflict-aware multimodal deep information seeking.

What carries the argument

The evolving multimodal structural graph that tracks beliefs across modalities and surfaces contradictions for revision.

If this is right

  • Plug-and-play use across five different backbones produces an average 17.2 percent relative accuracy gain on BrowseComp-VL.
  • The method outperforms prior vision-language models and deep research agents by 3.7 percent on MM-BrowseComp, 1.5 percent on HLE-VL, and 0.7 percent on BrowseComp-VL.
  • The graph-based mechanism supplies a principled way to manage contradictory information from heterogeneous modalities.
  • The workflow remains model-agnostic and requires no changes to the underlying backbone.

Where Pith is reading between the lines

These are editorial extensions of the paper, not claims the author makes directly.

  • The same graph-maintenance pattern could transfer to other agent tasks that must reconcile conflicting evidence from multiple data types.
  • Real-world deployments might see fewer hallucinations when sources disagree, because contradictions are tracked explicitly rather than averaged.
  • Automated ways to initialize and prune the structural graph could become a separate research target if manual maintenance proves costly at scale.

Load-bearing premise

That grounding the workflow in belief revision theory and maintaining an evolving multimodal structural graph will produce effective conflict-aware reasoning.

What would settle it

A controlled test set of multimodal queries containing explicit contradictions where the structural-graph method shows no accuracy gain or lower accuracy than linear evidence-accumulation baselines.

read the original abstract

Deep research agents have attracted increasing attention for their ability to collect large-scale online information to acquire target knowledge, with recent efforts shifting from purely text-based information seeking to multimodal settings. However, existing agentic workflows are largely aligned with evidence accumulation models, which linearly aggregate evidence and lack principled mechanisms for handling contradictory information across heterogeneous modalities. Towards this end, we propose Struct-Searcher, a structural agentic workflow grounded in belief revision theory that explicitly maintains an evolving multimodal structural graph throughout the reasoning process, enabling effective conflict-aware multimodal deep information seeking. Extensive experiments across multiple benchmark datasets and backbone models demonstrate that Struct-Searcher is (1) plug-and-play and model-agnostic, yielding an average relative accuracy improvement of 17.2% on BrowseComp-VL across five different backbones. (2) top-performing, consistently outperforming state-of-the-art vision-language models (VLMs) and deep research agents, with relative accuracy improvements of 3.7% on MM-BrowseComp, 1.5% on HLE-VL, and 0.7% on BrowseComp-VL over the second-best competing approach.

Editorial analysis

A structured set of objections, weighed in public.

Desk editor's note, referee report, simulated authors' rebuttal, and a circularity audit. Tearing a paper down is the easy half of reading it; the pith above is the substance, this is the friction.

Referee Report

2 major / 2 minor

Summary. The manuscript proposes Struct-Searcher, an agentic workflow for multimodal deep information seeking grounded in belief revision theory. It explicitly maintains an evolving multimodal structural graph to enable conflict-aware reasoning across heterogeneous modalities, in contrast to linear evidence accumulation. The method is presented as plug-and-play and model-agnostic. Experiments across benchmarks (BrowseComp-VL, MM-BrowseComp, HLE-VL) and five backbones report an average 17.2% relative accuracy gain on BrowseComp-VL, plus smaller margins (3.7%, 1.5%, 0.7%) over the second-best competing approaches.

Significance. If the central mechanism proves responsible for the gains, the work would offer a principled, theory-grounded alternative to standard agentic search pipelines in multimodal settings. The multi-backbone evaluation and plug-and-play framing are positive features that could support broader adoption if the structural-graph component is shown to be the operative factor.

major comments (2)
  1. [Experiments section (and associated figures/tables reporting the 17.2% gain)] The central claim—that grounding the workflow in belief revision theory and maintaining an evolving multimodal structural graph produces effective conflict-aware reasoning responsible for the reported accuracy gains—requires an ablation that isolates this component. No such ablation (e.g., replacing the structural graph with linear evidence accumulation while holding other workflow elements fixed) appears in the experimental evaluation, leaving open the possibility that gains arise from unstated factors such as prompt engineering or search heuristics rather than the claimed mechanism.
  2. [Method section (description of the structural graph and belief revision)] Implementation details for constructing, updating, and performing belief revision over the multimodal structural graph are insufficient to verify that the mechanism actually resolves contradictions without introducing new errors. The abstract and method description supply no pseudocode, update rules, or conflict-resolution procedure, making it impossible to assess whether the graph maintenance is load-bearing for the empirical results.
minor comments (2)
  1. [Abstract and results tables] The abstract and results sections report relative accuracy improvements without accompanying absolute accuracies, standard deviations, or error bars, which hinders assessment of effect size and statistical reliability.
  2. [Experiments section] Dataset descriptions, benchmark construction details, and exact evaluation protocols for BrowseComp-VL, MM-BrowseComp, and HLE-VL are not provided, limiting reproducibility.

Simulated Author's Rebuttal

2 responses · 0 unresolved

We thank the referee for the constructive and detailed comments, which identify key opportunities to strengthen the empirical validation and methodological transparency of our work. We address each major comment below and will incorporate revisions to address the concerns raised.

read point-by-point responses

  1. Referee: [Experiments section (and associated figures/tables reporting the 17.2% gain)] The central claim—that grounding the workflow in belief revision theory and maintaining an evolving multimodal structural graph produces effective conflict-aware reasoning responsible for the reported accuracy gains—requires an ablation that isolates this component. No such ablation (e.g., replacing the structural graph with linear evidence accumulation while holding other workflow elements fixed) appears in the experimental evaluation, leaving open the possibility that gains arise from unstated factors such as prompt engineering or search heuristics rather than the claimed mechanism.

    Authors: We agree that an explicit ablation isolating the contribution of the multimodal structural graph is necessary to substantiate the central claim. In the revised manuscript we will add a controlled ablation that replaces the structural graph with linear evidence accumulation while holding all other workflow elements (including search heuristics and prompts) fixed. Results from this comparison across the same backbones and benchmarks will be reported to clarify whether the graph-based belief revision mechanism is responsible for the observed gains. revision: yes

  2. Referee: [Method section (description of the structural graph and belief revision)] Implementation details for constructing, updating, and performing belief revision over the multimodal structural graph are insufficient to verify that the mechanism actually resolves contradictions without introducing new errors. The abstract and method description supply no pseudocode, update rules, or conflict-resolution procedure, making it impossible to assess whether the graph maintenance is load-bearing for the empirical results.

    Authors: We acknowledge that the current description lacks sufficient implementation detail. The revised manuscript will include (1) pseudocode for multimodal graph construction and incremental updates, (2) the precise belief revision update rules grounded in the cited theory, and (3) the conflict-detection and resolution procedure used when contradictory evidence arrives across modalities. These additions will allow independent verification of the mechanism and its role in the reported results. revision: yes

Circularity Check

0 steps flagged

No circularity: empirical claims rest on benchmarks, not self-referential definitions or fitted predictions.

full rationale

The paper describes a proposed agentic workflow grounded in belief revision theory and an evolving multimodal structural graph, with performance evaluated via benchmark accuracy on BrowseComp-VL, MM-BrowseComp, and HLE-VL. No equations, parameter-fitting procedures, or derivation steps appear in the provided text. Claims of improvement (e.g., 17.2% relative gain) are presented as experimental outcomes rather than quantities defined in terms of the method itself. No self-citation load-bearing steps, uniqueness theorems, or ansatz smuggling are identifiable. The derivation chain is therefore self-contained against external benchmarks and does not reduce to its inputs by construction.

Axiom & Free-Parameter Ledger

0 free parameters · 1 axioms · 1 invented entities

Abstract-only review yields minimal ledger entries; the central claim rests on the unelaborated premise that belief revision theory supplies an effective conflict-resolution mechanism for the structural graph.

axioms (1)
  • domain assumption Belief revision theory supplies a principled mechanism for handling contradictory information across heterogeneous modalities.
    The workflow is explicitly grounded in this theory per the abstract.
invented entities (1)
  • evolving multimodal structural graph no independent evidence
    purpose: To maintain structure and enable conflict-aware reasoning throughout the agentic process.
    Introduced as the core representational device of the new workflow; no independent evidence supplied in the abstract.

pith-pipeline@v0.9.1-grok · 5763 in / 1324 out tokens · 23086 ms · 2026-06-27T22:19:12.131345+00:00 · methodology

discussion (0)

Sign in with ORCID, Apple, or X to comment. Anyone can read and Pith papers without signing in.

Reference graph

Works this paper leans on

60 extracted references · 17 canonical work pages · 7 internal anchors

  1. [1]

    OpenAI blog , url=

    Introducing OpenAI o3 and o4-mini , author=. OpenAI blog , url=

  2. [2]

    Tongyi DeepResearch Technical Report

    Tongyi deepresearch technical report , author=. arXiv preprint arXiv:2510.24701 , year=

    work page internal anchor Pith review Pith/arXiv arXiv

  3. [3]

    Humanity's Last Exam

    Humanity's last exam , author=. arXiv preprint arXiv:2501.14249 , year=

    work page internal anchor Pith review Pith/arXiv arXiv

  4. [4]

    BrowseComp: A Simple Yet Challenging Benchmark for Browsing Agents

    Browsecomp: A simple yet challenging benchmark for browsing agents , author=. arXiv preprint arXiv:2504.12516 , year=

    work page internal anchor Pith review Pith/arXiv arXiv

  5. [5]

    The Twelfth International Conference on Learning Representations , year=

    Gaia: a benchmark for general ai assistants , author=. The Twelfth International Conference on Learning Representations , year=

  6. [6]

    arXiv preprint arXiv:2505.22648 , year=

    Webdancer: Towards autonomous information seeking agency , author=. arXiv preprint arXiv:2505.22648 , year=

    work page arXiv

  7. [7]

    arXiv preprint arXiv:2509.13309 , year=

    Webresearcher: Unleashing unbounded reasoning capability in long-horizon agents , author=. arXiv preprint arXiv:2509.13309 , year=

    work page arXiv

  8. [8]

    arXiv preprint arXiv:2507.15061 , year=

    Webshaper: Agentically data synthesizing via information-seeking formalization , author=. arXiv preprint arXiv:2507.15061 , year=

    work page arXiv

  9. [9]

    WebWatcher: Breaking New Frontier of Vision-Language Deep Research Agent

    Webwatcher: Breaking new frontier of vision-language deep research agent , author=. arXiv preprint arXiv:2508.05748 , year=

    work page internal anchor Pith review Pith/arXiv arXiv

  10. [10]

    The eleventh international conference on learning representations , year=

    React: Synergizing reasoning and acting in language models , author=. The eleventh international conference on learning representations , year=

  11. [11]

    arXiv preprint arXiv:2509.25301 , year=

    Flash-searcher: Fast and effective web agents via dag-based parallel execution , author=. arXiv preprint arXiv:2509.25301 , year=

    work page arXiv

  12. [12]

    2025 8th International Conference on Artificial Intelligence and Big Data (ICAIBD) , pages=

    A survey on agent workflow--status and future , author=. 2025 8th International Conference on Artificial Intelligence and Big Data (ICAIBD) , pages=. 2025 , organization=

    2025

  13. [13]

    arXiv preprint arXiv:2510.24698 , year=

    ParallelMuse: Agentic Parallel Thinking for Deep Information Seeking , author=. arXiv preprint arXiv:2510.24698 , year=

    work page arXiv

  14. [14]

    BrowseComp-ZH: Benchmarking Web Browsing Ability of Large Language Models in Chinese

    Browsecomp-zh: Benchmarking web browsing ability of large language models in chinese , author=. arXiv preprint arXiv:2504.19314 , year=

    work page internal anchor Pith review Pith/arXiv arXiv

  15. [15]

    arXiv preprint arXiv:2501.07572 , year=

    Webwalker: Benchmarking llms in web traversal , author=. arXiv preprint arXiv:2501.07572 , year=

    work page arXiv

  16. [16]

    arXiv preprint arXiv:2506.13651 , year=

    xbench: Tracking Agents Productivity Scaling with Profession-Aligned Real-World Evaluations , author=. arXiv preprint arXiv:2506.13651 , year=

    work page arXiv

  17. [17]

    2003 , publisher=

    Belief revision , author=. 2003 , publisher=

    2003

  18. [18]

    Philosophy Compass , volume=

    Belief revision I: the AGM theory , author=. Philosophy Compass , volume=. 2013 , publisher=

    2013

  19. [19]

    Artificial intelligence , volume=

    On the logic of iterated belief revision , author=. Artificial intelligence , volume=. 1997 , publisher=

    1997

  20. [20]

    Mm-browsecomp: A comprehensive benchmark for multimodal browsing agents.arXiv preprint arXiv:2508.13186,

    Mm-browsecomp: A comprehensive benchmark for multimodal browsing agents , author=. arXiv preprint arXiv:2508.13186 , year=

    work page arXiv

  21. [21]

    OpenAI blog , url=

    Introducing GPT-4.1 in the API , author=. OpenAI blog , url=

  22. [22]

    ArXiv , year=

    GPT-4o System Card , author=. ArXiv , year=

  23. [23]

    OpenAI blog , url=

    GPT-4o mini: advancing cost-efficient intelligence , author=. OpenAI blog , url=

  24. [24]

    ArXiv , year=

    Qwen2.5-VL Technical Report , author=. ArXiv , year=

  25. [25]

    OpenAI blog , url=

    Introducing GPT-5 , author=. OpenAI blog , url=

  26. [26]

    Claude 3.7 Sonnet and Claude Code , author =

  27. [27]

    2025 , organization =

    Jie Ouyang and Ruiran Yan and Yucong Luo and Mingyue Cheng and Qi Liu and Zirui Liu and Shuo Yu and Daoyu Wang , title =. 2025 , organization =

    2025

  28. [28]

    arXiv preprint arXiv:2505.23885 , year=

    Owl: Optimized workforce learning for general multi-agent assistance in real-world task automation , author=. arXiv preprint arXiv:2505.23885 , year=

    work page arXiv

  29. [29]

    DeerFlow: Deep Research at Your Fingertipsat Your Fingertips , url =

    ByteDance , year =. DeerFlow: Deep Research at Your Fingertipsat Your Fingertips , url =

  30. [30]

    2025 , eprint=

    Benchmarking Multimodal Retrieval Augmented Generation with Dynamic VQA Dataset and Self-adaptive Planning Agent , author=. 2025 , eprint=

    2025

  31. [31]

    2025 , eprint=

    Kimi K2: Open Agentic Intelligence , author=. 2025 , eprint=

    2025

  32. [32]

    2025 , eprint=

    LongCat-Flash Technical Report , author=. 2025 , eprint=

    2025

  33. [33]

    OpenHands: An Open Platform for AI Software Developers as Generalist Agents

    Xingyao Wang and Boxuan Li and Yufan Song and Frank F. Xu and Xiangru Tang and Mingchen Zhuge and Jiayi Pan and Yueqi Song and Bowen Li and Jaskirat Singh and Hoang H. Tran and Fuqiang Li and Ren Ma and Mingzhang Zheng and Bill Qian and Yanjun Shao and Niklas Muennighoff and Yizhe Zhang and Binyuan Hui and Junyang Lin and Robert Brennan and Hao Peng and H...

    work page internal anchor Pith review Pith/arXiv arXiv

  34. [34]

    Langchain: Build context-aware reasoning applications , author =

  35. [35]

    2025 , eprint=

    SWE-RL: Advancing LLM Reasoning via Reinforcement Learning on Open Software Evolution , author=. 2025 , eprint=

    2025

  36. [36]

    2025 , eprint=

    Training Long-Context, Multi-Turn Software Engineering Agents with Reinforcement Learning , author=. 2025 , eprint=

    2025

  37. [37]

    2025 , eprint=

    Intern-S1: A Scientific Multimodal Foundation Model , author=. 2025 , eprint=

    2025

  38. [38]

    2025 , eprint=

    Kosmos: An AI Scientist for Autonomous Discovery , author=. 2025 , eprint=

    2025

  39. [39]

    2025 , eprint=

    Deep Research: A Survey of Autonomous Research Agents , author=. 2025 , eprint=

    2025

  40. [40]

    2025 , eprint=

    Deep Research: A Systematic Survey , author=. 2025 , eprint=

    2025

  41. [41]

    Search-o1: Agentic Search-Enhanced Large Reasoning Models

    Search-o1: Agentic search-enhanced large reasoning models , author=. arXiv preprint arXiv:2501.05366 , year=

    work page internal anchor Pith review Pith/arXiv arXiv

  42. [42]

    arXiv e-prints , pages=

    Agentorchestra: A hierarchical multi-agent framework for general-purpose task solving , author=. arXiv e-prints , pages=

  43. [43]

    2025 , note =

    Perplexity , title =. 2025 , note =

    2025

  44. [44]

    Serper API , author=

  45. [45]

    Jina Reader , author=

  46. [46]

    deepresearch , author=

  47. [47]

    2025 , note =

    Kimi-Researcher , title =. 2025 , note =

    2025

  48. [48]

    2025 , note =

    xAI , title =. 2025 , note =

    2025

  49. [49]

    2025 , eprint=

    R1-Searcher: Incentivizing the Search Capability in LLMs via Reinforcement Learning , author=. 2025 , eprint=

    2025

  50. [50]

    2025 , eprint=

    Multimodal DeepResearcher: Generating Text-Chart Interleaved Reports From Scratch with Agentic Framework , author=. 2025 , eprint=

    2025

  51. [51]

    2025 , eprint=

    A Comprehensive Survey of Deep Research: Systems, Methodologies, and Applications , author=. 2025 , eprint=

    2025

  52. [52]

    2024 , eprint=

    MMSearch: Benchmarking the Potential of Large Models as Multi-modal Search Engines , author=. 2024 , eprint=

    2024

  53. [53]

    2025 , eprint=

    Search-R1: Training LLMs to Reason and Leverage Search Engines with Reinforcement Learning , author=. 2025 , eprint=

    2025

  54. [54]

    2025 , eprint=

    Deep Research Brings Deeper Harm , author=. 2025 , eprint=

    2025

  55. [55]

    2025 , eprint=

    A Vision for Geo-Temporal Deep Research Systems: Towards Comprehensive, Transparent, and Reproducible Geo-Temporal Information Synthesis , author=. 2025 , eprint=

    2025

  56. [56]

    2025 , eprint=

    MMSearch-Plus: Benchmarking Provenance-Aware Search for Multimodal Browsing Agents , author=. 2025 , eprint=

    2025

  57. [57]

    arXiv preprint arXiv:2502.16033 , year=

    Multimodal inconsistency reasoning (mmir): A new benchmark for multimodal reasoning models , author=. arXiv preprint arXiv:2502.16033 , year=

    work page arXiv

  58. [58]

    2025 , eprint=

    Perception, Reason, Think, and Plan: A Survey on Large Multimodal Reasoning Models , author=. 2025 , eprint=

    2025

  59. [59]

    The Quantitative Methods for Psychology , volume=

    Evidence accumulation models: Current limitations and future directions , author=. The Quantitative Methods for Psychology , volume=

  60. [60]

    The journal of symbolic logic , volume=

    On the logic of theory change: Partial meet contraction and revision functions , author=. The journal of symbolic logic , volume=. 1985 , publisher=

    1985