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REVIEW 3 major objections 5 minor 18 references

Follow the Flow: Fine-grained Flowchart Attribution with Neurosymbolic Agents

T0 review · 3 major / 5 minor · reviewed 2026-08-07 · deepseek-v4-flash

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Pith's one-line read The paper introduces Fine-grained Flowchart Attribution and claims that its neurosymbolic agent, FlowPathAgent, grounds LLM answers in explicit graph paths over flowcharts, reducing visual hallucination and outperforming strong baselines…

desk verdict A useful new task and benchmark, but the headline gains are overstated and the evaluation has a circularity risk that the paper's own evidence doesn't close. read the letter →

arxiv 2506.01344 v1 pith:PPIQMY3G submitted 2025-06-02 cs.CL

Manan Suri , Puneet Mathur , Nedim Lipka , Franck Dernoncourt , Ryan A. Rossi , Vivek Gupta , Dinesh Manocha This is my paper
classification cs.CL
keywords Flowchartattributionneurosymbolicagentvisualhallucinationgraph-basedreasoningquestionansweringexplainabilitybenchmarkvision-languagemodels
verification ladder T0 review T1 audit T2 compute T3 formal

The pith

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

The reading

The paper proposes that hallucination in vision-language models answering flowchart questions can be reduced by making the model justify its answer on an explicit graph. It defines Fine-grained Flowchart Attribution as the task of finding the sequence of nodes and edges in a flowchart image whose regions ground a given statement. The proposed FlowPathAgent segments the flowchart, converts it into a symbolic graph, and lets an agent query that graph with tools to trace a supporting path. The authors also contribute FlowExplainBench, over 1,200 flowchart QA pairs with human-verified attribution paths in multiple styles and domains. They report that FlowPathAgent mitigates visual hallucinations and outperforms strong baselines by 10-14% on this benchmark.

What carries the argument

The load-bearing object is the symbolic graph built from the flowchart: nodes carry the text of each step, directed edges carry optional Yes/No conditions, and node labels from segmentation tie the graph back to visual regions in the image. FlowPathAgent's neurosymbolic loop operates on this graph: first segmentation labels components (FlowMask2Former), then a fine-tuned Qwen2-VL converts the labeled image to Mermaid code that is parsed into the graph, and finally an agent repeatedly selects nodes and invokes tools such as get_statement, get_ancestors, get_descendants, path_between, and shortest_path until it emits final_answer. Because only the initial planning step consumes the labeled image and all later steps operate on tool observations, the reasoning is decoupled from visual recognition.

What would settle it

Compare FlowPathAgent's attributed paths with the original flowchart in samples where Flow2Mermaid VLM's generated Mermaid code is known to deviate from the source (for example, a missing edge or an inverted Yes/No condition); if the agent still returns a path that is logically consistent with the graph but visually impossible on the image, the graph-faithfulness assumption is violated. A controlled version is to delete one edge from the parsed graph and check that the agent's path changes exactly as the deletion predicts.

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Extended reading notes

Core claim

The paper's central claim is that post hoc attribution of a flowchart QA response should be performed as graph-based reasoning over a symbolic representation of the diagram, not as direct visual grounding. FlowPathAgent labels every flowchart component by instance segmentation, converts the labeled image into Mermaid code with a fine-tuned Qwen2-VL, parses that code into a directed graph whose edges carry Yes/No conditions, and then runs an agent that selects nodes, calls graph tools, and assembles the attributed path. The task definition requires the attributed path to be the shortest sequence of regions that is contextually aligned with the statement and exclusive of unnecessary regions. On FlowExplainBench, the paper reports an overall F1 of 77.20 for FlowPathAgent, ahead of all baselines, with the strongest baseline, GPT-4o with Set-of-Marks prompting, at 70.75.

Load-bearing premise

The load-bearing premise is that the symbolic graph the conversion model builds from a flowchart image faithfully represents the visual diagram, so paths the agent finds in the graph correspond to real paths in the picture rather than to artifacts of a corrupted transcription.

Editorial extensions

If this is right

  • Attribution makes flowchart QA output verifiable: a user can inspect the exact nodes and decision branches that support a generated answer.
  • The method is modular and post hoc, so it can be attached to existing LLM systems without retraining the response generator.
  • Graph-based traversal keeps performance more stable on large flowcharts, where purely visual models degrade.
  • FlowExplainBench gives the community a quantitative, human-agreement-checked target for measuring attribution quality across styles, domains, and question types.

Reading between the lines

Editorial extensions of the paper, not claims the author makes directly.

  • Abstractly, the segmentation-to-graph-to-tools recipe could transfer to other structured diagrams such as UML diagrams, circuit schematics, or decision trees, wherever layout encodes control flow.
  • If graph faithfulness turns out to be the bottleneck, the highest-leverage improvement would be making the image-to-graph conversion verifiable, for example by letting the agent re-check edges against image crops.
  • Since attribution is post hoc, the same signal could be used as a safety filter: an answer whose optimal attributed path is empty or self-contradictory could be flagged for human review.
  • The benchmark's path criteria could also serve as a training signal for smaller models to produce attributions directly.
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Editorial analysis

A structured set of objections, weighed in public.

Desk editor's note, referee report, and a circularity audit.

Referee Report

3 major / 5 minor

Summary. This paper introduces Fine-grained Flowchart Attribution, the task of identifying the sequence of flowchart nodes/regions that ground a given question-answer statement, and proposes FlowPathAgent, a modular neurosymbolic system. FlowPathAgent first labels flowchart components with a fine-tuned Mask2Former variant (FlowMask2Former), converts the labeled flowchart into Mermaid code with a fine-tuned Qwen2-VL (Flow2Mermaid VLM), parses the Mermaid into a graph, and then uses an LLM agent equipped with graph tools (get_ancestors, path_between, etc.) to select an attributed path, which is mapped back to image regions. The authors also present FlowExplainBench, built from the FlowVQA test split across Code/Wiki/Instruct domains and four question types, with GPT-4-generated and human-verified (κ=0.89) node-path annotations. Experiments compare FlowPathAgent with Kosmos-2, LISA, SA2VA, VisProg, GPT-4o Bounding Box, and GPT-4o+SoM, reporting an overall F1 of 77.20 versus 70.75 for GPT-4o+SoM.

Significance. The paper proposes a genuinely new task and a benchmark that is carefully constructed: annotations are human-verified with high inter-annotator agreement, styles/domains/question types are balanced, and the benchmark construction details (including annotator guidelines and filtering) are documented. The neurosymbolic architecture is modular and the agent traces are interpretable. If the central claim—that FlowPathAgent reduces visual hallucinations by grounding answers in the flowchart structure—is supported, the work could make flowchart-based QA verifiable. The paper is transparent about its modular risks and includes a supplementary hand-drawn flowchart case study. However, the empirical support is currently incomplete: the headline improvement is not consistent with the reported numbers, and the evaluation does not yet directly establish structural fidelity of the image-to-graph conversion, which is the key link between the agent's symbolic reasoning and the visual content of the flowchart.

major comments (3)
  1. [Abstract; Section 7, Table 2] The abstract states that FlowPathAgent outperforms strong baselines by 10–14%, but Table 2 shows an overall F1 gain of 6.45 points over GPT-4o+SoM (77.20 vs 70.75), which is 9.1% relative; Section 7 reports a range of 6–65 percentage points. For the individual domains the relative improvements over GPT-4o+SoM are approximately 12.4% (Code), 8.3% (Wiki), and 8.1% (Instruct). Please clarify whether improvements are relative or absolute and update the abstract, introduction, and Section 7 so the reported margins are mutually consistent.
  2. [Sections 4.3, 5.2, 9; Table 3] The load-bearing uncertainty is whether Flow2Mermaid VLM's reconstructed Mermaid graph is structurally faithful to the visual flowchart. Ground-truth attributions are produced from the original Mermaid source code (Section 4.3, Step 1), while FlowPathAgent's reasoning uses the graph parsed from Flow2Mermaid's output (Section 5.2). If the reconstructed graph has wrong edge conditions, missing branches, or swapped statements, the agent can choose a path that is logically consistent with the graph but visually incorrect. The paper's own counter-evidence in Section 9—Word F1 of 0.89 and the IoU-binned analysis in Table 3—is not sufficient: Word F1 is a token-level measure, and IoU conditions only on segmentation quality, not on graph structure. I ask for a direct structural fidelity evaluation of Flow2Mermaid (node-level and edge-level precision/recall, including edge-condition accuracy) against the original Mermaid on a held-out subset, and an end-to-end breakdown of FlowPathAgent's F1 for structurally correct versus incorrect reconstructions.
  3. [Section 6.2] The evaluation metric is set-based. Although the task definition (Section 3) requires a sequence of regions and the running example (Fig 5) contrasts ordered paths (B→C→F→G→J→M→N vs C→F→G→J), Section 6.2 computes micro-averaged Precision/Recall/F1 over the set of nodes, which ignores order. A system that outputs the correct nodes in the wrong order receives full credit. Please add a sequence-aware metric (e.g., longest common subsequence or path edit distance) or otherwise justify that order is not needed for the attribution task.
minor comments (5)
  1. [Section 6.1; Table 2] LISA is cited as (Li et al., 2023b) in Section 6.1 and Appendix A.1, but Table 2 cites (Lai et al., 2024); the reference list contains both. Please use one consistent citation for this baseline.
  2. [Section 5.1] The fine-tuned model is referred to as FlowMask2Former in the text and tables, but Section 5.1 calls it FlowMask2Transformer; the citation for Mask2Former is also missing ('?' appears in the text). Please make the naming and citation consistent.
  3. [Section 7] The sentence 'As the complexity of the flowchart increases (i.e., as the number of nodes decreases), a performance dip is observed' appears to have the direction reversed; Fig 3 shows the dip in the long tail of large node counts.
  4. [Section 4.3] There is a typo: 'filtering srategy' should be 'filtering strategy'.
  5. [Appendix A.2] The fine-tuned VLM is called 'Mermaid2Graph' in this section, while it is called Flow2Mermaid elsewhere; please make the naming consistent.

Circularity Check

0 steps flagged · score 1.0 of 10

The evaluation is benchmark-anchored with no fitted-parameter predictions; the Mermaid-space overlap between GT annotation and agent reasoning is a validity caveat acknowledged by the paper, not a by-construction reduction, so I score 1.

full rationale

FlowPathAgent's derivation chain is not circular at any load-bearing step. (1) Evaluation is externally anchored: FlowExplainBench is built from the held-out test split of FlowVQA ('FlowExplainBench is constructed using the test split of the FlowVQA dataset', Section 4.1), and ground-truth attributions are human-verified with high agreement ('Cohen's Kappa (κ), shows a high level of agreement both between the two annotators (κ = 0.89)', Section 4.3). (2) No fitted input is renamed as a prediction: the only trainable modules, FlowMask2Former and Flow2Mermaid VLM, are trained exclusively on the style-diversified FlowVQA training split (Sections 5.1-5.2); the agent's attribution itself is zero-shot tool-based reasoning over the parsed graph with no exposure to any attribution label, so the reported F1 is not forced by a fit. (3) The shared 'minimal set' / optimality language in the annotator guidelines (Fig. 23) and the agent's system prompt (Fig. 19) instantiates the task definition of Section 3 (Optimality, Contextual Alignment, Exclusivity); annotation and inference following the same task specification is alignment, not circularity. (4) The strongest skeptical point, that ground truth is created on the Mermaid source ('We use GPT-4 to perform the initial attribution for corresponding QA pairs directly in the Mermaid source code', Section 4.3) while the agent reasons on a graph parsed from Flow2Mermaid output (Section 5.2), is a genuine evaluation-validity risk but not a by-construction reduction: the agent must still visually segment and transcribe the image (Flow2Mermaid reaches Word F1 0.89, not 1.0), and no equation in the paper identifies the agent's graph with the GT Mermaid. The paper itself flags the modular risk in Section 9 ('potential errors in these components may influence overall performance'), and its binned analysis (Table 3) is indirect evidence rather than a circular argument. What remains missing, as the skeptic notes, is a direct structural (edge- and condition-level) fidelity measurement for Flow2Mermaid; that is a missing-support concern affecting external validity, not circularity. Self-citations (MATSA, FlowVQA) appear only as related-work context and as data source; FlowVQA is an independently published external benchmark, so this is not load-bearing self-citation. Verdict: no circularity; score 1 for the minor validity caveat.

Assumptions & free parameters 2 free parameters · 4 assumptions · 0 invented entities

The empirical claim rests on three external supports: the FlowVQA test split as the source of charts, GPT-4 plus human verification as the source of labels, and the trained FlowMask2Former and Flow2Mermaid components. The unspecified planning VLM is an unstated assumption. The only hand-chosen evaluation parameters are the IoU mapping threshold and the agent step cap.

free parameters (2)
  • IoU threshold for region-node mapping = 0.7
    Used in evaluation to map predicted segmentation regions to ground-truth nodes (Section 6.2). Chosen by hand; affects precision and recall alignment between predicted and ground-truth nodes.
  • Max agent tool-call steps = 8
    The agent is capped at 8 tool-selection and execution cycles (Figure 4, Section 5.3). This cap influences whether the agent reaches final_answer and thus affects reported attributions.
assumptions (4)
  • domain assumption Flowcharts are faithfully represented by directed graphs with condition-labeled edges
    The formalization in Section 3 equates flowchart images with logical graphs (V,E) and grounds the entire method; segmentation and Mermaid conversion assume this equivalence is reliable.
  • domain assumption GPT-4 auto-labels on Mermaid source, verified by two human annotators (kappa 0.89), produce correct ground-truth attributions
    FlowExplainBench's gold labels come from this pipeline (Section 4.3); any systematic error in this labeling would propagate to all evaluation scores.
  • domain assumption The FlowVQA test split, restyled with four color and layout templates, is representative of the flowchart attribution task
    FlowExplainBench is built entirely from the FlowVQA test split (Section 4.1); generalization beyond these sources is not tested except for a small hand-drawn case study (Appendix D).
  • domain assumption The unspecified planning VLM possesses sufficient tool-calling and reasoning ability
    Section 5.3 says a VLM plans and attributes in token space, but the model is not named; the central result depends on this model's competence with the smolagents framework.

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Cite this review

Pith. "Pith review of Follow the Flow: Fine-grained Flowchart Attribution with Neurosymbolic Agents." pith.science (2026). https://pith.science/paper/PPIQMY3G

@misc{pith2026250601344,
  author       = {Pith},
  title        = {Pith review of: Follow the Flow: Fine-grained Flowchart Attribution with Neurosymbolic Agents},
  year         = {2026},
  howpublished = {\url{https://pith.science/paper/PPIQMY3G}},
  note         = {Machine review of arXiv:2506.01344}
}
read the original abstract

Flowcharts are a critical tool for visualizing decision-making processes. However, their non-linear structure and complex visual-textual relationships make it challenging to interpret them using LLMs, as vision-language models frequently hallucinate nonexistent connections and decision paths when analyzing these diagrams. This leads to compromised reliability for automated flowchart processing in critical domains such as logistics, health, and engineering. We introduce the task of Fine-grained Flowchart Attribution, which traces specific components grounding a flowchart referring LLM response. Flowchart Attribution ensures the verifiability of LLM predictions and improves explainability by linking generated responses to the flowchart's structure. We propose FlowPathAgent, a neurosymbolic agent that performs fine-grained post hoc attribution through graph-based reasoning. It first segments the flowchart, then converts it into a structured symbolic graph, and then employs an agentic approach to dynamically interact with the graph, to generate attribution paths. Additionally, we present FlowExplainBench, a novel benchmark for evaluating flowchart attributions across diverse styles, domains, and question types. Experimental results show that FlowPathAgent mitigates visual hallucinations in LLM answers over flowchart QA, outperforming strong baselines by 10-14% on our proposed FlowExplainBench dataset.

Figures

Figures reproduced from arXiv: 2506.01344 by the authors.

Figure 1
Figure 1. Attribution (represented by •−•−•) with FlowPathAgent ensures logical consistency in flowchart-based reasoning. FlowPathAgent uses a neurosymbolic approach to generate attribution paths ( ➊ & ➋) in the flowchart. This enhances interpretability and reliability in flowchart driven automated decision-making. et al., 2024; Ensmenger, 2016). Their structured yet visual nature makes them an effective medium for conveying … view at source ↗
Figure 2
Figure 2. Overview of FlowPathAgent. FlowPathAgent processes a flowchart image through segmentation-based component labeling, constructs a symbolic graph representation using Mermaid, and employs a neurosymbolic agent, that treats the flowchart as a symbolic graph to attribute nodes based on an input statement. The agent interacts with predefined tools to analyze and traverse the flowchart structure, producing attributions as… view at source ↗
Figure 3
Figure 3. Performance comparison of FlowPathAgent against baselines demonstrates superior effectiveness across long-tail distribution of node count in flowcharts. bfs dfs final_answer get_ancestors get_descendants get_statement in_degree max_in_degree max_out_degree out_degree path_between shortest_path get_neighbours fail Step 1 Step 2 Step 3 Step 4 Step 5 Step 6 Step 7 Step 8 [PITH_FULL_IMAGE:figures/full_fig_p007_3.png] view at source ↗
Figures from the paper (20 more)
Figure 4
Figure 4. Figure 4: Flow diagram of the sequence of tools used [PITH_FULL_IMAGE:figures/full_fig_p007_4.png]
Figure 5
Figure 5. Figure 5: Qualitative comparison of FlowPathAgent with baseline methods. The flowchart illustrates attributions generated by various baselines, highlighting the agentic trace of FlowPathAgent. We contrast its output with the next strongest baseline, GPT-4o+SoM, to showcase diffe…
Figure 6
Figure 6. Figure 6: Scatter plot of segmentation IoU versus Word [PITH_FULL_IMAGE:figures/full_fig_p008_6.png]
Figure 7
Figure 7. Figure 7: Box-plot distribution of time taken in each [PITH_FULL_IMAGE:figures/full_fig_p012_7.png]
Figure 11
Figure 11. Figure 11: Heatmap of duration of tool call execution, [PITH_FULL_IMAGE:figures/full_fig_p012_11.png]
Figure 9
Figure 9. Figure 9: shows the distribution of nodes in our bench￾mark [PITH_FULL_IMAGE:figures/full_fig_p012_9.png]
Figure 12
Figure 12. Figure 12: Overview of training split used for FlowMask2Former, and Flow2Mermaid VLM. The figure demon [PITH_FULL_IMAGE:figures/full_fig_p013_12.png]
Figure 14
Figure 14. Figure 14: Example from FlowExplainBench–Code. This example represents a Fact Retrieval question, and has a style type 2 (multiple colors). D Additional analysis on hand-constructed charts We conducted a supplemntary case study to an￾alyze FlowPathAgent’s generalization to real￾…
Figure 13
Figure 13. Figure 13: Example from FlowExplainBench–Instruct. This example represents an Applied Scenario question, and has a style type 1 (single color). Q: What is the maximum allowed length for the new string after spaces are replaced? A: The new string must not exceed 1000 characters i…
Figure 15
Figure 15. Figure 15: Example from [PITH_FULL_IMAGE:figures/full_fig_p016_15.png]
Figure 16
Figure 16. Figure 16: Qualitative comparison of FlowPathAgent with baselines via examples. 17 [PITH_FULL_IMAGE:figures/full_fig_p017_16.png]
Figure 17
Figure 17. Figure 17: (Continued) Qualitative comparison of FlowPathAgent with baselines via examples. 18 [PITH_FULL_IMAGE:figures/full_fig_p018_17.png]
Figure 18
Figure 18. Figure 18: Class Diagram of the FlowChart data structure representing directed graphs with conditional edges. 19 [PITH_FULL_IMAGE:figures/full_fig_p019_18.png]
Figure 21
Figure 21. Figure 21: Prompt Template used for initial automatic [PITH_FULL_IMAGE:figures/full_fig_p020_21.png]
Figure 22
Figure 22. Figure 22: Diversity of color schemes used to augment [PITH_FULL_IMAGE:figures/full_fig_p020_22.png]
Figure 23
Figure 23. Figure 23: Summary of instructions given to human annotators. [PITH_FULL_IMAGE:figures/full_fig_p021_23.png]
Figure 24
Figure 24. Figure 24: Human annotation platform for attribution annotation. [PITH_FULL_IMAGE:figures/full_fig_p022_24.png]
Figure 27
Figure 27. Figure 27: FlowPathAgent attributed D correctly. The [PITH_FULL_IMAGE:figures/full_fig_p023_27.png]
Figure 26
Figure 26. Figure 26: FlowPathAgent attributed E correctly. The [PITH_FULL_IMAGE:figures/full_fig_p023_26.png]
Figure 29
Figure 29. Figure 29: FlowPathAgent attributed A, B, C, E. The [PITH_FULL_IMAGE:figures/full_fig_p024_29.png]

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Reviewed August 7, 2026 · model on record in the stance chip above.