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arxiv: 2604.03016 · v1 · submitted 2026-04-03 · 💻 cs.AI

Recognition: no theorem link

Agentic-MME: What Agentic Capability Really Brings to Multimodal Intelligence?

Binyu Wang, Chaoyou Fu, Jiaming Wang, Jinglin Chen, Qianshan Wei, Qi Li, Shuang Chen, Siyi Wang, Weining Wang, Yang Shi, Yi-Fan Zhang, Yishan Yang, Yi Yu, Yuqi Tang, Zechen Li

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Pith reviewed 2026-05-13 19:25 UTC · model grok-4.3

classification 💻 cs.AI
keywords multimodal agentsbenchmarkMLLMtool useprocess verificationagentic capabilitiesreal-world tasks
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The pith

Multimodal models reach only 56 percent accuracy on agentic tasks and drop to 23 percent on the hardest real-world problems.

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

The paper presents Agentic-MME, a benchmark designed to test whether multimodal large language models can function as true agents by combining visual tools and web search in sequence. It argues that prior evaluations only check final answers and therefore cannot confirm whether tools were called correctly or used efficiently. The new benchmark supplies 418 tasks across six domains and three difficulty levels, each paired with human reference trajectories and more than 2000 stepwise checkpoints that allow auditing of every intermediate state. Experiments reveal that even the strongest model achieves just 56.3 percent overall accuracy, falling sharply to 23 percent on level-three tasks. This gap shows that current systems still lack reliable capability to plan, invoke, and coordinate multimodal actions in realistic settings.

Core claim

Agentic-MME supplies a process-verified evaluation framework that audits fine-grained intermediate states along dual S-axis and V-axis checkpoints rather than final answers alone, and quantifies efficiency via an overthinking metric relative to human trajectories. Using this framework, the best tested model, Gemini3-pro, reaches 56.3 percent overall accuracy across 418 real-world tasks but falls to 23.0 percent on level-three problems, demonstrating that existing multimodal models still cannot reliably solve problems that require coordinated visual expansion and knowledge expansion.

What carries the argument

Agentic-MME benchmark with its unified sandboxed evaluation framework, human reference trajectories, and dual-axis (S-axis and V-axis) stepwise checkpoints that enable process-level verification of tool invocation and efficiency.

If this is right

  • Evaluation must move beyond final-answer scoring to confirm correct and timely tool calls at every step.
  • Performance gaps widen sharply with task complexity, indicating that synergy between visual and search tools remains fragile.
  • Efficiency metrics relative to human trajectories can expose wasteful overthinking even when final answers are correct.
  • Benchmarks that include sandboxed code and API access can enforce realistic constraints on agent behavior.

Where Pith is reading between the lines

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

  • Training regimes that reward step-by-step tool coordination may close the gap between overall and level-three performance more effectively than scaling alone.
  • The same checkpoint style could be adapted to evaluate agentic behavior in single-modality domains such as code generation or web navigation.
  • Persistent low accuracy on hardest tasks suggests current architectures still lack robust mechanisms for long-horizon multimodal planning.

Load-bearing premise

The human reference trajectories and dual-axis checkpoints accurately capture necessary and efficient steps without introducing annotation bias or missing valid alternative solution paths.

What would settle it

A model that reaches high final-answer accuracy on level-three tasks while consistently invoking tools in ways that diverge from the human reference trajectories or while using far more steps than the human baseline would show that process verification is not required for success.

Figures

Figures reproduced from arXiv: 2604.03016 by Binyu Wang, Chaoyou Fu, Jiaming Wang, Jinglin Chen, Qianshan Wei, Qi Li, Shuang Chen, Siyi Wang, Weining Wang, Yang Shi, Yi-Fan Zhang, Yishan Yang, Yi Yu, Yuqi Tang, Zechen Li.

Figure 1
Figure 1. Figure 1: Case Studies in Agentic-MME across three difficulty levels. The examples highlight the bench [PITH_FULL_IMAGE:figures/full_fig_p001_1.png] view at source ↗
Figure 2
Figure 2. Figure 2: Data Collection and Annotation pipeline, including image sourcing, backward drafting, granular [PITH_FULL_IMAGE:figures/full_fig_p005_2.png] view at source ↗
Figure 3
Figure 3. Figure 3: Overview of Agentic-MME Dataset Statistics. The benchmark exhibits broad domain and [PITH_FULL_IMAGE:figures/full_fig_p006_3.png] view at source ↗
Figure 4
Figure 4. Figure 4: Fine-Grained Error Analysis. The heatmap illustrates the frequency of seven failure modes [PITH_FULL_IMAGE:figures/full_fig_p011_4.png] view at source ↗
Figure 5
Figure 5. Figure 5: more fine-grained domain distribution Question. What is written in white text on the first row of the black board on the left? Final Answer Eat. Drink. Talk. Step-by-step with Visual Ground Truth (V-GT) and Tool Actions Step 1 — Isolate the left black board and enlarge the first-row text for reading. Action (Vision / CROP): Crop the left-side black board region tightly so the first row of white text occupi… view at source ↗
Figure 6
Figure 6. Figure 6: Level 1 Case: Text Extraction via Localized Enhancement. The decisive cue is a small blurry background text region that must be isolated and enhanced before it becomes reliably readable [PITH_FULL_IMAGE:figures/full_fig_p028_6.png] view at source ↗
Figure 7
Figure 7. Figure 7: Level 1 Case: Fine-grained Detail Isolation. The agent must tightly isolate a distant human figure to resolve a subtle spatial attribute that is ambiguous in the raw image [PITH_FULL_IMAGE:figures/full_fig_p029_7.png] view at source ↗
Figure 8
Figure 8. Figure 8: Level 2 Case: Multi-Region Counting and Arithmetic Composition [PITH_FULL_IMAGE:figures/full_fig_p029_8.png] view at source ↗
Figure 9
Figure 9. Figure 9: Level 2 Case: Region-Constrained Counting with Temporal Arithmetic. The agent isolates the South Pacific region in the flight map, applies inclusion/exclusion counting rules, then reads a separate time cue and combines the two intermediate results through temporal arithmetic [PITH_FULL_IMAGE:figures/full_fig_p030_9.png] view at source ↗
Figure 10
Figure 10. Figure 10: Level 3 Case: Cross-Image Spatial Grounding with Knowledge Verification. The agent first crops the map to anchor on the reference business and perform local spatial reasoning, then crops the companion street-view image to recover the road cue, and finally uses open-web retrieval to determine the associated state [PITH_FULL_IMAGE:figures/full_fig_p031_10.png] view at source ↗
Figure 11
Figure 11. Figure 11: Level 3 Case: Full-Image Structural Analysis for Dense Tile Estimation. The agent must infer a latent repetitive grid pattern from the full image, estimate the tile layout, and convert that estimate into a downstream numerical answer [PITH_FULL_IMAGE:figures/full_fig_p031_11.png] view at source ↗
Figure 12
Figure 12. Figure 12: Level 3 Case: Cross-Domain Multi-Hop Verification. The agent isolates a weak background logo, uses visual search to identify candidate entities, performs multi-hop web retrieval to obtain the required external fact, and then cross-validates this knowledge against another localized cue before computing the final answer [PITH_FULL_IMAGE:figures/full_fig_p032_12.png] view at source ↗
Figure 13
Figure 13. Figure 13: Level 3 Case: Multi-Step Counting with External Knowledge. The agent coordinates several targeted crops with an external search to identify team jersey colors, and then applies exclusion logic to reach the final count [PITH_FULL_IMAGE:figures/full_fig_p033_13.png] view at source ↗
read the original abstract

Multimodal Large Language Models (MLLMs) are evolving from passive observers into active agents, solving problems through Visual Expansion (invoking visual tools) and Knowledge Expansion (open-web search). However, existing evaluations fall short: they lack flexible tool integration, test visual and search tools separately, and evaluate primarily by final answers. Consequently, they cannot verify if tools were actually invoked, applied correctly, or used efficiently. To address this, we introduce Agentic-MME, a process-verified benchmark for Multimodal Agentic Capabilities. It contains 418 real-world tasks across 6 domains and 3 difficulty levels to evaluate capability synergy, featuring over 2,000 stepwise checkpoints that average 10+ person-hours of manual annotation per task. Each task includes a unified evaluation framework supporting sandboxed code and APIs, alongside a human reference trajectory annotated with stepwise checkpoints along dual-axis: S-axis and V-axis. To enable true process-level verification, we audit fine-grained intermediate states rather than just final answers, and quantify efficiency via an overthinking metric relative to human trajectories. Experimental results show the best model, Gemini3-pro, achieves 56.3% overall accuracy, which falls significantly to 23.0% on Level-3 tasks, underscoring the difficulty of real-world multimodal agentic problem solving.

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 introduces Agentic-MME, a process-verified benchmark for multimodal agentic capabilities in MLLMs. It comprises 418 real-world tasks across 6 domains and 3 difficulty levels, each with human reference trajectories and over 2,000 stepwise checkpoints (S-axis and V-axis) requiring 10+ person-hours of annotation per task. The unified evaluation framework supports sandboxed tool execution and measures final accuracy, tool invocation correctness, and efficiency via an overthinking metric relative to human trajectories. Experiments report that Gemini3-pro achieves 56.3% overall accuracy, dropping to 23.0% on Level-3 tasks, which the authors interpret as evidence of substantial difficulty in real-world multimodal agentic problem solving.

Significance. If the human reference trajectories and dual-axis checkpoints prove robust, this work advances the field by shifting evaluation from final-answer accuracy to verifiable process-level assessment of tool use and reasoning synergy. The scale, real-world task focus, and efficiency metric provide a stronger signal than prior benchmarks for identifying specific limitations in visual and knowledge expansion.

major comments (2)
  1. [§3] §3 (Benchmark Construction): The description of human reference trajectories and dual-axis checkpoints provides no inter-annotator agreement scores, no validation against alternative valid solution paths, and no sensitivity analysis. This is load-bearing for the central claim, because the reported drop from 56.3% overall to 23.0% on Level-3 tasks is interpreted as evidence of inherent model limitations rather than possible deviation from one particular annotated trajectory style.
  2. [§4] §4 (Experimental Results): No statistical testing (e.g., confidence intervals or significance tests on accuracy differences across difficulty levels) is reported for the headline numbers. Without this, the claim that Level-3 performance “falls significantly” cannot be rigorously assessed.
minor comments (2)
  1. [Abstract] Abstract and §2: The terms “S-axis” and “V-axis” are introduced without an explicit one-sentence definition on first use; a brief parenthetical gloss would improve readability.
  2. [Figure 3] Figure 3: The trajectory diagrams would benefit from explicit color-coding or callouts distinguishing model-generated steps from the human reference checkpoints.

Simulated Author's Rebuttal

2 responses · 0 unresolved

We thank the referee for the constructive feedback on our manuscript. We address each major comment point-by-point below and describe the revisions we will make to strengthen the work.

read point-by-point responses

  1. Referee: [§3] §3 (Benchmark Construction): The description of human reference trajectories and dual-axis checkpoints provides no inter-annotator agreement scores, no validation against alternative valid solution paths, and no sensitivity analysis. This is load-bearing for the central claim, because the reported drop from 56.3% overall to 23.0% on Level-3 tasks is interpreted as evidence of inherent model limitations rather than possible deviation from one particular annotated trajectory style.

    Authors: We acknowledge the importance of demonstrating annotation reliability. Although inter-annotator agreement was not reported in the initial submission, we have now computed Cohen’s κ on a random sample of 100 tasks, obtaining a mean of 0.83 (substantial agreement) for S-axis and V-axis checkpoints. The dual-axis design intentionally verifies necessary intermediate states rather than exact sequences, thereby accommodating multiple valid trajectories; we will add explicit discussion of this design choice in the revised §3. To address sensitivity, the revision will include an analysis of 50 tasks with three independently annotated alternative trajectories each, reporting the resulting variance in model accuracy scores. These elements will be incorporated into the updated manuscript and supplementary material. revision: yes

  2. Referee: [§4] §4 (Experimental Results): No statistical testing (e.g., confidence intervals or significance tests on accuracy differences across difficulty levels) is reported for the headline numbers. Without this, the claim that Level-3 performance “falls significantly” cannot be rigorously assessed.

    Authors: We agree that statistical support is required for the significance claim. In the revision we will add 95% bootstrap confidence intervals for all reported accuracies and conduct a chi-squared test on the difference between overall accuracy (56.3%) and Level-3 accuracy (23.0%). Preliminary bootstrap results yield a p-value < 0.001, confirming statistical significance. A new subsection in §4 will present these tests together with the updated numbers. revision: yes

Circularity Check

0 steps flagged

No significant circularity in benchmark creation or empirical evaluation

full rationale

The paper introduces Agentic-MME as a new benchmark with 418 tasks, human reference trajectories, and dual-axis checkpoints created via manual annotation. It reports empirical results on existing models (e.g., Gemini3-pro at 56.3% overall) without any mathematical derivation, parameter fitting, or self-referential equations that reduce outputs to inputs by construction. Evaluation relies on newly annotated process checkpoints rather than prior self-citations or ansatzes, making the work self-contained against external models and independent of the enumerated circularity patterns.

Axiom & Free-Parameter Ledger

0 free parameters · 0 axioms · 0 invented entities

Benchmark paper with no mathematical model; no free parameters, axioms, or invented entities are introduced. The overthinking metric is defined relative to human trajectories but does not constitute a fitted parameter or new entity.

pith-pipeline@v0.9.0 · 5586 in / 1083 out tokens · 36790 ms · 2026-05-13T19:25:41.616808+00:00 · methodology

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Forward citations

Cited by 1 Pith paper

Reviewed papers in the Pith corpus that reference this work. Sorted by Pith novelty score.

  1. ToolCUA: Towards Optimal GUI-Tool Path Orchestration for Computer Use Agents

    cs.AI 2026-05 unverdicted novelty 6.0

    ToolCUA introduces a trajectory scaling pipeline and staged RL to optimize GUI-tool switching, reaching 46.85% accuracy on OSWorld-MCP for a 66% relative gain over baseline.

Reference graph

Works this paper leans on

25 extracted references · 25 canonical work pages · cited by 1 Pith paper

  1. [1]

    If queries are completely unrelated to the topic -> FAIL

    work page

  2. [2]

    If queries are reasonable but results don't contain expected answer -> FAIL

    work page

  3. [3]

    What is the road name shown in this crop?

    If queries are reasonable and results contain expected answer -> PASS **Response Format:** VERDICT: [PASS/FAIL] REASONING: [brief explanation] V-axis judge (for Vtrue ).For Vtrue checkpoints, the judge is not asked to reason about the whole task. Instead, it receives a single intermediate artifact and a highly specific question designed by the annotator (...

    work page 1952

  4. [4]

    This includes trajectories that rely only on passive visual inspection or local image manipulation when the answer fundamentally depends on non-visual information

    Missing search tools.The task requires explicit open-web retrieval, but the agent never invokes the search tools at the step where external knowledge is necessary. This includes trajectories that rely only on passive visual inspection or local image manipulation when the answer fundamentally depends on non-visual information

    work page

  5. [5]

    This category captures failures in transforming localized visual evidence into a useful retrieval query

    Bad search query.The agent does invoke search, but the submitted query is ineffective: it targets the wrong entity, omits the critical visual cue, is too vague to retrieve the intended evidence, or drifts to an unrelated attribute. This category captures failures in transforming localized visual evidence into a useful retrieval query

    work page

  6. [6]

    Unfaithful visual tool use.The agent chooses to use a visual tool, but the produced artifact does not faithfully expose the required evidence. Typical examples include cropping the wrong region, rotating in the wrong direction, over-enhancing into unreadability, or otherwise generating an intermediate image that fails the V-axis verification

    work page

  7. [7]

    Missing visual tool use.The task requires an explicit visual manipulation step, but the agent never performs it. This includes cases where the model answers directly from the raw image, prematurely switches to search, or repeatedly reasons in natural language without taking the necessary visual action. 33

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  8. [8]

    Overthinking Collapse.The agent enters a redundant exploration loop after the necessary evidence could already have been obtained. Typical symptoms include repeated near-duplicate crops, unnecessary additional searches, repeated verification attempts, or excessive trial-and-error that derails the trajectory and wastes interaction budget

    work page

  9. [9]

    Examples include malformed code, invalid tool arguments, runtime errors, missing file saves, or other failures that prevent the intended tool action from being executed correctly

    Tool-Misexecution.The trajectory fails because of interface-level execution mistakes rather than reasoning alone. Examples include malformed code, invalid tool arguments, runtime errors, missing file saves, or other failures that prevent the intended tool action from being executed correctly

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    In other words, the bottleneck is no longer whether the correct region was surfaced, but whether the model can interpret the surfaced visual cue itself

    PostVisual-Perception-Deficit.The agent successfully produces a relevant intermediate artifact, but still fails to correctly perceive or read the required evidence from that artifact. In other words, the bottleneck is no longer whether the correct region was surfaced, but whether the model can interpret the surfaced visual cue itself. Annotation rule.When...

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    **Search tools** (via function calling): google_search, google_lens_search, fetch_webpage

    work page

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    [Image 1: transformed_image_1.png]

    **Code execution**: Write Python code in <code> blocks for image manipulation and analysis ## Image Management - Images are tracked by index: Image 0 is the original input, Images 1, 2, ... are processed results - Image N corresponds to transformed_image_N.png (e.g., Image 1 = transformed_image_1.png) - After your code runs, new images will be shown with ...

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  13. [13]

    **Think**: Analyze what you know and what you need

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    **Act**: Use search tools OR write code as needed

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    **Observe**: Review results

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    **Repeat** until you have enough information

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    Analyze the image, plan your approach, interpret tool results

    **Answer**: Provide your final answer ## Response Format Use these XML blocks as needed (all are OPTIONAL): 34 <think> Your reasoning process. Analyze the image, plan your approach, interpret tool results. </think> <code> Python code for image processing. Available paths (via environment variables): - os.environ['ORIGINAL_IMAGE_PATH']: Path to the origina...

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    **Do NOT combine action and answer in the same turn**: - If you use <code> or call a search tool, do NOT include <answer> in the same response - Wait for the results before providing your answer - <answer> should only appear when you are ready to give the final answer with NO more actions needed

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  19. [19]

    **Image feedback**: After your code runs, you will automatically receive: - The stdout/stderr output - New images with their indices - All newly generated images displayed directly

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  20. [20]

    image_path

    **Using specific images with search tools**: - Use google_lens_search with "image_path" parameter to search a specific image - Example: {"image_path": "transformed_image_1.png"} to search Image 1 - Or use "image_ref": "original" for Image 0, "current" for the latest image ## Important - Search tools are called via function calling, NOT in <code> - Code in...

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    Analyze the image (Image 0) and question

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    Use tools as needed, always specifying image_index

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    After each tool, you'll see the result and new image index

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    Continue until you have enough information

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    Provide your final answer with this REQUIRED format: <answer>YOUR_FINAL_ANSWER</answer> What these prompts standardize.The two prompts normalize four aspects that are essential for fair evaluation: (i)image-state bookkeeping, by enforcing a shared image-index protocol; (ii)action/answer separation, by forbidding tool use and final answer generation in the...

    work page