arxiv: 2605.00905 · v1 · submitted 2026-04-29 · 💻 cs.CL · cs.AI· cs.CV

Recognition: unknown

DIAGRAMS: A Review Framework for Reasoning-Level Attribution in Diagram QA

Anirudh Iyengar Kaniyar Narayana Iyengar , Tampu Ravi Kumar , Manan Suri , Raviteja Bommireddy , Dinesh Manocha , Puneet Mathur , Vivek Gupta

Authors on Pith no claims yet

Pith reviewed 2026-05-09 21:07 UTC · model grok-4.3

classification 💻 cs.CL cs.AIcs.CV

keywords diagram question answeringreasoning-level attributionevidence selectionannotation frameworkmeta-schemadataset adaptersvisual reasoningreview framework

0 comments

The pith

DIAGRAMS proposes visual regions for reasoning in diagram QA using a meta-schema and adapters, achieving 85.39 percent precision and 75.30 percent recall against human reviewer selections across six datasets.

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

The paper introduces a review framework that suggests the visual regions needed to reason through a diagram question and its answer, rather than only marking the final answer location. It decouples the annotation interface from any one dataset's format by using an internal meta-schema plus lightweight adapters, so the same tool works on charts, maps, circuits, and infographics. When regions or even QA pairs are missing, the system generates candidates for human review and refinement. Readers would care because building structured, reasoning-level evidence for diagram questions has required heavy manual work; the reported agreement levels show this burden can be reduced without losing fidelity to human judgments.

Core claim

The central claim is that a lightweight, schema-driven review interface with QA-conditioned evidence selection can propose the complete set of regions required for reasoning-level attribution in Diagram QA, attaining micro-averaged 85.39 percent precision and 75.30 percent recall against reviewer-final selections across six datasets and thereby reducing manual region creation while preserving agreement with intended attributions.

What carries the argument

The QA-conditioned evidence selection process inside a meta-schema and dataset-adapter architecture that decouples interface logic from dataset-specific JSON structures and proposes reasoning regions for human verification.

If this is right

The same interface supports generation of missing QA pairs or candidate regions followed by human verification and refinement.
High agreement with final reviewer choices enables reliable dataset auditing and creation of grounded supervision data.
The approach directly supports grounded evaluation of diagram QA models by linking answers to their full reasoning regions.
Manual effort for building structured evidence sets is reduced while the completeness of reasoning attributions is maintained.

Where Pith is reading between the lines

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

The decoupling via meta-schema could be reused for annotation pipelines in other visual reasoning domains such as scientific figures or infographics.
Model proposals that reach 75 percent recall offer a practical starting point for scaling training data that requires explicit reasoning paths.
Public release of the demo and package implies the method can become a shared resource for consistent attribution standards across multiple diagram datasets.

Load-bearing premise

The QA-conditioned evidence selection reliably surfaces every region a human would need for reasoning without systematic omissions or dataset-specific biases, and the meta-schema generalizes without hidden per-dataset tuning.

What would settle it

Apply the framework to a seventh Diagram QA dataset never seen during development, collect independent human reviewer selections of reasoning regions, and measure whether precision or recall drops substantially below the reported figures or whether key reasoning regions are consistently omitted.

Figures

Figures reproduced from arXiv: 2605.00905 by Anirudh Iyengar Kaniyar Narayana Iyengar, Dinesh Manocha, Manan Suri, Puneet Mathur, Raviteja Bommireddy, Tampu Ravi Kumar, Vivek Gupta.

**Figure 1.** Figure 1: Overview of the DIAGRAMS architecture. The framework operates in three stages: (1) Data Ingestion, which converts dataset-specific records into a unified meta-schema; (2) Review Engine, which renders normalized QA items and candidate regions for human verification; and (3) Assisted Evidence Proposal, where a multimodal backend performs QA-conditioned evidence selection and optional QA or region generation.… view at source ↗

**Figure 2.** Figure 2: Selection-based attribution. The reviewer [PITH_FULL_IMAGE:figures/full_fig_p003_2.png] view at source ↗

**Figure 4.** Figure 4: Human verification and export. The reviewer [PITH_FULL_IMAGE:figures/full_fig_p004_4.png] view at source ↗

read the original abstract

Diagram question answering (Diagram QA) requires reasoning-level attribution that links each question-answer pair to all visual regions needed to derive the answer, rather than only the region containing the final response. Creating such structured evidence across diagrams, charts, maps, circuits, and infographics is time-consuming, and existing annotation tools tightly couple their interfaces to dataset-specific formats. We present DIAGRAMS, a lightweight, schema-driven review framework that decouples interface logic from dataset-specific JSON structures through an internal meta-schema and dataset adapters. Given an image and QA pair with optional candidate regions, the system performs QA-conditioned evidence selection and proposes the regions required for reasoning. When QA pairs or candidate regions are missing, it generates them and supports human verification and refinement. Across six Diagram QA datasets, model-suggested evidence achieves 85.39% precision and 75.30% recall against reviewer-final selections (micro-averaged). These results indicate that the review-first framework reduces manual region creation while maintaining high agreement with final reasoning-level attributions. We release a public demo and installable package to support dataset auditing, grounded supervision creation, and grounded evaluation.

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.

Desk Editor's Note private letter to a colleague

DIAGRAMS is a useful engineering tool for flexible diagram QA annotation with decent reported agreement, but the metrics likely overstate the model's independent value because reviewers refine after seeing proposals.

read the letter

The main point is that DIAGRAMS gives a practical way to annotate reasoning-level regions in diagram QA across different datasets without rewriting interfaces each time, and the numbers they report are reasonable on the surface. The meta-schema plus adapters let them handle six different dataset formats with one core system, and the QA-conditioned region proposal plus human refinement loop is a straightforward way to speed up the work. Shipping a demo and installable package is the right move for something like this. They get 85.39% precision and 75.30% recall micro-averaged against the final human selections, which suggests the suggestions are often close to what reviewers keep. That part is solid engineering and directly addresses the pain of manual region creation for grounded supervision. The soft spot is the evaluation setup. Reviewers see the model proposals before they finalize, so the agreement numbers are not independent. Without a baseline of annotations done from scratch or inter-annotator agreement without the tool, it is hard to know how much time is actually saved or whether the model is missing important regions that humans would have added on their own. The abstract also leaves out details on the region suggestion model itself and how regions are defined or scored beyond the aggregates. This is mainly for people who build or audit diagram QA datasets and need a reusable annotation pipeline. A reader in that position would get immediate value from the adapters and the public release, even if they would want to run their own controls. It is not a methodological breakthrough, but the core contribution is clear enough and the practical focus is honest. I would send it to peer review so referees can ask for the missing baselines and implementation specifics.

Referee Report

2 major / 1 minor

Summary. The paper presents DIAGRAMS, a lightweight schema-driven review framework for reasoning-level attribution in Diagram QA. It decouples the interface from dataset-specific formats via an internal meta-schema and adapters, performs QA-conditioned evidence selection to propose regions (or generates them when missing), and supports human verification/refinement. Across six Diagram QA datasets, model-suggested evidence achieves 85.39% micro-averaged precision and 75.30% recall against reviewer-final selections, supporting the claim that the framework reduces manual region creation while maintaining high agreement with final attributions. A public demo and installable package are released.

Significance. If the central empirical claim holds under independent evaluation, the work offers a practical contribution to grounded annotation in visual reasoning by lowering the cost of creating reasoning-level evidence across diagrams, charts, and infographics. The release of reusable tooling could aid dataset auditing and supervised training in the Diagram QA community.

major comments (2)

The central performance claim (85.39% precision, 75.30% recall) compares model suggestions directly to reviewer-final selections. However, the framework description indicates that reviewers see and can refine the model proposals before finalizing, creating a risk that final attributions are influenced by the initial suggestions. This undermines the independence of the agreement metric and the assertion that the framework reduces manual effort without altering the final reasoning-level attributions. A baseline of scratch annotations performed without model input (or inter-annotator agreement without the tool) is needed to validate the claim.
The abstract (and presumably the evaluation section) provides no details on the underlying model or method for QA-conditioned region suggestion, how regions are formally defined or delimited, inter-annotator agreement on the final selections, or any comparison to non-model-assisted annotation. These omissions leave the reported precision/recall figures under-supported as evidence for the framework's effectiveness.

minor comments (1)

The abstract would be strengthened by a one-sentence description of the region suggestion mechanism or region definition to contextualize the numerical results for readers.

Simulated Author's Rebuttal

2 responses · 1 unresolved

We thank the referee for the careful review and constructive comments. We address each major point below, clarifying our evaluation design while acknowledging its limitations. We will revise the manuscript to improve clarity and add necessary details and discussion.

read point-by-point responses

Referee: The central performance claim (85.39% precision, 75.30% recall) compares model suggestions directly to reviewer-final selections. However, the framework description indicates that reviewers see and can refine the model proposals before finalizing, creating a risk that final attributions are influenced by the initial suggestions. This undermines the independence of the agreement metric and the assertion that the framework reduces manual effort without altering the final reasoning-level attributions. A baseline of scratch annotations performed without model input (or inter-annotator agreement without the tool) is needed to validate the claim.

Authors: We agree that the reported metrics reflect agreement after reviewers have seen and potentially refined the model proposals, which introduces the possibility of anchoring effects. The framework is explicitly designed as a review-first tool in which humans retain final control; the high agreement indicates that model suggestions are largely consistent with human reasoning and therefore reduce the amount of manual region creation required. However, we did not perform a controlled scratch-annotation baseline or measure inter-annotator agreement in the absence of the tool. We will add an explicit limitations paragraph discussing this design choice and the potential for bias, but we cannot supply the requested baseline data without new annotation experiments. revision: partial
Referee: The abstract (and presumably the evaluation section) provides no details on the underlying model or method for QA-conditioned region suggestion, how regions are formally defined or delimited, inter-annotator agreement on the final selections, or any comparison to non-model-assisted annotation. These omissions leave the reported precision/recall figures under-supported as evidence for the framework's effectiveness.

Authors: We appreciate the call for greater self-containment. While the body of the paper describes the meta-schema, dataset adapters, and QA-conditioned selection process, we acknowledge that the abstract and evaluation section would benefit from additional explicit information. In the revision we will expand the abstract to note the core method, clarify that regions are delimited as bounding boxes or polygons over diagram elements, report inter-annotator agreement on the final selections, and include a brief comparison of model-assisted versus fully manual annotation effort. revision: yes

standing simulated objections not resolved

The request for a scratch-annotation baseline or inter-annotator agreement measured without the tool, which would require new controlled annotation experiments beyond the scope of the current study.

Circularity Check

0 steps flagged

No circularity: empirical claims rest on direct human comparison without derivations or self-referential reductions

full rationale

The paper describes a schema-driven review framework for Diagram QA and reports empirical precision/recall figures from model suggestions versus reviewer-final selections across six datasets. No equations, derivations, fitted parameters, or predictive steps exist that could reduce to inputs by construction. Central claims are supported by direct annotation agreement metrics rather than any self-citation chain, uniqueness theorem, or ansatz smuggling. The evaluation setup compares outputs to independent human refinements, satisfying the criteria for a self-contained, non-circular result.

Axiom & Free-Parameter Ledger

0 free parameters · 2 axioms · 0 invented entities

The framework rests on standard assumptions about human annotation quality and dataset representativeness with no free parameters or new postulated entities.

axioms (2)

domain assumption Human reviewers provide reliable ground-truth evidence selections for evaluation.
The precision and recall metrics are computed against reviewer-final selections.
domain assumption The six Diagram QA datasets are sufficiently representative for claiming general utility.
Results are presented as evidence that the framework works across datasets.

pith-pipeline@v0.9.0 · 5535 in / 1352 out tokens · 43692 ms · 2026-05-09T21:07:49.161768+00:00 · methodology

discussion (0)

Reference graph

Works this paper leans on

26 extracted references · 5 canonical work pages · 2 internal anchors

[1]

International journal of computer vision , volume=

LabelMe: a database and web-based tool for image annotation , author=. International journal of computer vision , volume=. 2008 , publisher=

2008
[2]

Proceedings of the 27th ACM international conference on multimedia , pages=

The VIA annotation software for images, audio and video , author=. Proceedings of the 27th ACM international conference on multimedia , pages=
[3]

Proceedings of the 29th International Conference on Intelligent User Interfaces , pages=

Snapper: Accelerating Bounding Box Annotation in Object Detection Tasks with Find-and-Snap Tooling , author=. Proceedings of the 29th International Conference on Intelligent User Interfaces , pages=
[4]

2020 25th International Conference on Pattern Recognition (ICPR) , pages=

Iterative bounding box annotation for object detection , author=. 2020 25th International Conference on Pattern Recognition (ICPR) , pages=. 2021 , organization=

2020
[5]

Computer Vision and Image Understanding , volume=

Human attention in visual question answering: Do humans and deep networks look at the same regions? , author=. Computer Vision and Image Understanding , volume=. 2017 , publisher=

2017
[6]

Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition , pages=

On the general value of evidence, and bilingual scene-text visual question answering , author=. Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition , pages=
[7]

Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition , pages=

Grounding answers for visual questions asked by visually impaired people , author=. Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition , pages=
[8]

arXiv preprint arXiv:2509.10345 , year=

Towards understanding visual grounding in visual language models , author=. arXiv preprint arXiv:2509.10345 , year=

work page arXiv
[9]

2019 , author=

Computer vision annotation tool: A universal approach to data annotation. 2019 , author=. URL https://github. com/openvinotoolkit/cvat , year=

2019
[10]

Open source software available from https://github

Label studio: Data labeling software , author=. Open source software available from https://github. com/heartexlabs/label-studio , volume=
[11]

Proceedings of the IEEE international conference on computer vision , pages=

Grad-cam: Visual explanations from deep networks via gradient-based localization , author=. Proceedings of the IEEE international conference on computer vision , pages=
[12]

Proceedings of the IEEE/CVF international conference on computer vision , pages=

Taking a hint: Leveraging explanations to make vision and language models more grounded , author=. Proceedings of the IEEE/CVF international conference on computer vision , pages=
[13]

European conference on computer vision , pages=

A diagram is worth a dozen images , author=. European conference on computer vision , pages=. 2016 , organization=

2016
[14]

Findings of the association for computational linguistics: ACL 2022 , pages=

ChartQA: A benchmark for question answering about charts with visual and logical reasoning , author=. Findings of the association for computational linguistics: ACL 2022 , pages=

2022
[15]

Joint European Conference on Machine Learning and Knowledge Discovery in Databases , pages=

CircuitVQA: A visual question answering dataset for electrical circuit images , author=. Joint European Conference on Machine Learning and Knowledge Discovery in Databases , pages=. 2024 , organization=

2024
[16]

Proceedings of the IEEE/CVF Winter Conference on Applications of Computer Vision , pages=

InfographicVQA , author=. Proceedings of the IEEE/CVF Winter Conference on Applications of Computer Vision , pages=
[17]

arXiv preprint arXiv:2507.11625 , year=

MapIQ: Evaluating multimodal large language models for map question answering , author=. arXiv preprint arXiv:2507.11625 , year=

work page arXiv
[18]

Mapwise: Evaluating vision-language models for advanced map queries , author=. Proceedings of the 2025 Conference of the Nations of the Americas Chapter of the Association for Computational Linguistics: Human Language Technologies (Volume 1: Long Papers) , pages=

2025
[19]

IJCNLP-AACL , year=

InterChart: Benchmarking Visual Reasoning Across Decomposed and Distributed Chart Information , author=. IJCNLP-AACL , year=
[20]

Proceedings of the IEEE conference on computer vision and pattern recognition , pages=

Dvqa: Understanding data visualizations via question answering , author=. Proceedings of the IEEE conference on computer vision and pattern recognition , pages=
[21]

Multimodal Chain-of-Thought Reasoning in Language Models

Multimodal chain-of-thought reasoning in language models , author=. arXiv preprint arXiv:2302.00923 , year=

work page internal anchor Pith review arXiv
[22]

Advances in neural information processing systems , volume=

Flamingo: a visual language model for few-shot learning , author=. Advances in neural information processing systems , volume=
[23]

Kosmos-2: Grounding Multimodal Large Language Models to the World

Kosmos-2: Grounding multimodal large language models to the world , author=. arXiv preprint arXiv:2306.14824 , year=

work page internal anchor Pith review arXiv
[24]

Ferret: Refer and ground anything anywhere at any granular- ity

Ferret: Refer and ground anything anywhere at any granularity , author=. arXiv preprint arXiv:2310.07704 , year=

work page arXiv
[25]

Proceedings of the IEEE/CVF conference on computer vision and pattern recognition , pages=

Gqa: A new dataset for real-world visual reasoning and compositional question answering , author=. Proceedings of the IEEE/CVF conference on computer vision and pattern recognition , pages=
[26]

Proceedings of the IEEE conference on computer vision and pattern recognition , pages=

Multimodal explanations: Justifying decisions and pointing to the evidence , author=. Proceedings of the IEEE conference on computer vision and pattern recognition , pages=