ARK: A Dual-Axis Multimodal Retrieval Benchmark along Reasoning and Knowledge

arXiv:2602.09839 · cs.CV · Submitted 2026-02-10 · Read on arXiv

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Introduction to the show: ident: AI Radio. Generated commentary on the latest Artificial Intelligence papers.

Tom: Today's paper: "ARK: A Dual-Axis Multimodal Retrieval Benchmark along Reasoning and Knowledge".

Jane: ARK is introduced as a dual-axis multimodal retrieval benchmark designed to systematically analyze retrieval systems from two complementary perspectives: knowledge domains and reasoning skills.

Tom: First, who's behind it and why it matters.

Title and authors: Tom: We’re talking about "ARK: A Dual-Axis Multimodal Retrieval Benchmark along Reasoning and Knowledge." The authors are Lin, Ding, Zhou, Li, Yang, and Peng. It’s clear they were looking to address a gap where existing multimodal retrieval benchmarks didn't really diagnose professional knowledge or complex reasoning well.

Jane: That’s right; the title itself tells us the core idea is separating knowledge from reasoning in multimodal retrieval evaluations. They’re showing that you can't just look at one thing and assume you know what the system is missing.

Lu: The contribution they highlight is introducing this dual-axis design, where one axis covers five knowledge domains with seventeen subtypes and the other covers six categories of reasoning skills, like spatial or symbolic inference. This allows for a much finer level of analysis than what was previously available.

Meng: That sounds incredibly detailed, which is good for rigorous testing, but I wonder how scalable this setup is when you have to curate data across those many specific subtypes and reasoning types simultaneously? We need something practical for deployment.

Lalam: It’s about creating a more sophisticated testbed. If we can see the difference between failing because a model doesn't know chemistry versus failing because it can't visualize a chemical structure, that guides our training efforts much better.

The paper's summary: Tom: So, the paper summarizes ARK by explaining its structure: it evaluates retrieval with both unimodal and multimodal queries and candidates across sixteen different visual data types, like line charts or chemical structures. They also stress that most of their queries use targeted hard negatives that require multi-step reasoning rather than just superficial keyword matching.

Jane: That makes sense; they are intentionally designing queries to force the retrieval system to use genuine reasoning skills instead of just relying on simple semantic cues, which is a smart way to test true capability. They aren't giving easy wins; they are setting up a tough environment.

Lu: The paper emphasizes that accurate retrieval on ARK depends on combining domain knowledge with reasoning over multimodal evidence, and it shows that existing benchmarks often confuse these two factors by mixing them together. This separation is the main point of their contribution to multimodal retrieval research.

Meng: I see they are comparing this against older methods where people were just looking at performance numbers without knowing *why* a system failed, which is a limitation we have to keep in mind when we look at our own evaluation metrics.

Lalam: This summary really underscores the need for systems that integrate both knowledge and inference. It points toward future AI development that needs to handle complex tasks where understanding the context is as important as finding the correct piece of data.

The paper's improvements: Tom: Regarding improvements, ARK suggests focusing on closing the gap between knowledge-intensive and reasoning-intensive retrieval by showing that current systems struggle more with reasoning tasks than they do with pure knowledge recall. They also point out that scaling models alone isn't enough; you need specific interventions to help them improve their inference capabilities.

Jane: That’s interesting because it suggests the path forward isn't just about making the model bigger; it’s about giving it better tools during the retrieval process itself, like modifying how we ask questions or how we rank candidates after an initial search.

Lu: They specifically highlight that visual-centric reasoning is a major bottleneck, especially in areas like fine-grained visual reasoning and spatial reasoning, where systems struggle to localize subtle evidence or reason over geometric relations. This focuses the research on improving the spatial and fine-grained aspects of multimodal understanding.

Meng: If we look at practical applications, this means we need better tools for systems dealing with complex diagrams or schematics, because that’s where their reasoning difficulties are most apparent in engineering contexts.

Lalam: I think this focus on visual-centric reasoning is important because it speaks to how AI needs to improve its ability to "think with images," moving beyond just recognizing objects to understanding the relationships between them spatially.

Conclusion: Tom: So, wrapping up, the authors of "ARK: A Dual-Axis Multimodal Retrieval Benchmark along Reasoning and Knowledge" conclude that their benchmark provides a more discriminative testbed than prior work because it explicitly separates knowledge and reasoning axes. They show that closing the gap in multimodal retrieval requires both broader knowledge coverage and stronger, more transferable reasoning capabilities.

Jane: That’s the main message: we need to improve both sides of the equation—more knowledge *and* better inference skills—to build truly reliable multimodal systems for complex tasks. It’s a necessary step toward building more capable AI that can handle ambiguity better.

Lu: The paper shows that while scaling up models helps, it doesn't automatically fix the core reasoning bottlenecks they identified, and it suggests that explicit interventions at inference time, like query rewriting or re-ranking, offer consistent gains.

Meng: From an engineering view, those interventions give us a clear roadmap for where to invest our optimization efforts next—not just in model size, but in these specific architectural adjustments that enhance reasoning.

Lalam: I think this whole discussion about ARK highlights how critical it is to build AI that integrates domain knowledge with strong visual reasoning. It points toward systems that can handle nuanced tasks where understanding the context and the spatial relationships are what truly matter.

Yijie Lin, Guofeng Ding, Haochen Zhou, Haobin Li, Mouxing Yang

cs.CV

Submitted: 2026-02-10

Updated: 2026-09-29

Importance score: 86/100

The gist: ARK is introduced as a dual-axis multimodal retrieval benchmark designed to systematically analyze retrieval systems from two complementary perspectives: knowledge domains and reasoning skills.

Key concepts

ARK
A dual-axis multimodal retrieval benchmark created by Lin, Ding, Zhou, Li, Yang, and Peng. It systematically analyzes retrieval systems by evaluating them along two complementary axes: knowledge domains and reasoning skills.
Dual-Axis Design
The structure of ARK features two distinct axes for evaluation. One axis covers five knowledge domains with seventeen subtypes, while the other covers six categories of reasoning skills, such as spatial or symbolic inference. This allows for a finer analysis than previous benchmarks.
Knowledge vs. Reasoning Gap
The paper suggests that current multimodal retrieval systems struggle more with reasoning tasks than with pure knowledge recall. Closing this gap requires not just scaling models but implementing specific interventions at the inference stage, like query rewriting or re-ranking candidates.

Terminology

Summary

ARK is introduced as a dual-axis multimodal retrieval benchmark designed to systematically analyze retrieval systems from two complementary perspectives: knowledge domains and reasoning skills. This research addresses a gap in existing benchmarks by explicitly separating these factors, enabling the diagnosis of whether retrieval failures stem from missing domain knowledge or insufficient reasoning. By evaluating 23 representative retrievers across diverse knowledge and reasoning axes, ARK reveals a pronounced gap between knowledge-intensive and reasoning-intensive retrieval, highlighting fine-grained visual and spatial reasoning as persistent bottlenecks in current multimodal systems.

Benchmark Structure

ARK is structured along two orthogonal axes: i) knowledge domains (five domains with 17 subtypes) which characterize the content and expertise retrieval relies on, and ii) reasoning skills (six categories), which characterize the type of inference over multimodal evidence required to identify the correct candidate. The knowledge axis spans five meta-domains: (i) Visual Cognition, (ii) Natural Science, (iii) Formal Science, (iv) Humanities & Social Science, and (v) Engineering & Technology. The reasoning axis evaluates six categories: knowledge reasoning, spatial reasoning, logical reasoning, symbolic reasoning, fine-grained visual reasoning, and conceptual abstraction.

Query Design and Evaluation Strategy

To avoid shortcut matching during evaluation—a common issue in existing benchmarks—ARK employs a rigorous query design strategy. Most queries are paired with targeted hard negatives that require multi-step reasoning. This ensures that correct retrieval relies on genuine reasoning rather than superficial semantic cues. The evaluation also assesses retrieval with both unimodal and multimodal queries and candidates, covering 16 heterogeneous visual data types, such as tables, line charts, chemical structures, artworks, comics, and cognitive maps.

Key Findings from Evaluation

The empirical evaluation of 23 representative retrievers yielded three key findings:

  1. While current systems handle many knowledge-intensive queries reasonably well, they fall sharply behind on reasoning-intensive tasks. Performance also varies substantially across disciplines, indicating limited generalization and pronounced domain dependence.

  2. Visual-centric reasoning is a key bottleneck on ARK, with performance dropping markedly on vision-centric skills such as fine-grained visual reasoning and spatial reasoning. This suggests retrievers struggle to localize subtle evidence in high-resolution images and to reason over geometric or topological relations.

  3. While larger models generally achieve stronger retrieval performance, scaling alone does not eliminate the core reasoning bottlenecks. The paper shows that explicitly injecting reasoning at inference time through lightweight interventions such as query rewriting and re-ranking yields consistent gains, suggesting a practical path for improvement.

Data Curation Pipeline

The data curation process is a multi-stage pipeline combining taxonomy-driven collection, automated refinement, and human verification. This involves:

  1. Defining a taxonomy spanning five knowledge domains and six reasoning dimensions to guide the gallery selection.

  2. Collecting high-quality multimodal data types across the 16 subtypes (e.g., Natural Images, Chemical Structure Diagrams).

  3. Using multimodal LLMs to rewrite original queries and questions to ensure better alignment with intended knowledge constraints and reasoning requirements.

  4. Constructing targeted hard negatives by mining visually or semantically similar candidates and manually selecting distractors that share superficial cues but violate underlying conditions, such as geometric relations or logical constraints.

Inference-Time Interventions

The study investigates two inference-time interventions: query rewriting and re-ranking. Query rewriting uses a model like GPT-5.2 to transform raw queries into a semantically equivalent but more explicit form that highlights reasoning cues. Re-ranking involves retrieving the top candidates and applying a reranker for each pair to produce a refined ranking. The results indicate that query rewriting and re-ranking consistently improve retrieval, and they are complementary, with combining both yielding the best results. Query rewriting is noted to help most when queries under-specify instructions, while re-ranking enables stronger pairwise inference over top candidates.

Conclusion

ARK provides a more discriminative testbed than prior benchmarks because it explicitly separates knowledge and reasoning axes, allowing for fine-grained analysis across knowledge, reasoning, and their interactions. The findings underscore that closing the gap in multimodal retrieval requires not only broader knowledge coverage but also stronger and more transferable reasoning capabilities. While interventions like query rewriting provide consistent gains, substantial headroom remains for advancing reasoning-aware multimodal retrieval. ARK is intended to help illuminate current limitations and guide the development of more robust and reasoning-aware multimodal retrieval systems.

Impact Statement

ARK is constructed exclusively from publicly available materials and is intended for noncommercial academic research. It will be released under the CC BY-NC-SA 4.0 license, promoting transparent and responsible downstream use. ARK is designed to support the development of more reliable multimodal retrieval by fostering progress toward models that better integrate domain knowledge with visual reasoning.

Improvements for AI systems

Here are specific improvements to AI systems based on the ARK benchmark, focusing on addressing its identified limitations:

) Improved System Capabilities:

  1. Do not rely solely on surface-level semantic matching (e.g., simple object categories or keyword overlap).

  2. Perform multi-step, structured inference over multimodal evidence to solve complex queries.

  3. Exhibit high performance across specialized knowledge domains (e.g., formal science, engineering) and fine-grained visual reasoning tasks (e.g., identifying subtle details in high-resolution images, geometric transformations).

) Specific System Improvements:

  1. Implement a Dual-Axis evaluation framework that explicitly separates knowledge acquisition from reasoning inference during retrieval tasks to diagnose failure modes accurately.

  2. Develop mechanisms for Reasoning-Oriented Query Rewriting (as shown in Table 21), where the system analyzes the query, infers necessary domain concepts, and translates abstract reasoning logic into concrete visual constraints before searching.

  3. Integrate an explicit Re-ranking stage after initial retrieval using specialized rerankers (as shown in Table 7) to refine candidate scores based on pairwise inference over the top-k results.

  4. Incorporate vision-centric reasoning modules, such as those inspired by thinking with images, to improve the ability to localize subtle visual evidence in high-resolution images and reason over geometric/topological relations (Spatial Reasoning).

  5. Train models specifically on structured visual data types (e.g., chemical structures, circuit diagrams) and symbolic representations (e.g., TikZ code) to enhance their performance in Formal Science and Code-Drawing domains through Symbolic Reasoning capabilities.

) What the Improved AI System Can Do:

The improved system will be capable of performing complex, expert-level multimodal retrieval tasks that go far beyond simple object recognition. Specifically, it can:

  1. Identify specific biological species based on subtle morphological cues (e.g., distinguishing closely related organisms in high-resolution images).

  2. Solve physics and chemistry problems by correctly interpreting schematic diagrams and inferring the underlying physical or chemical mechanisms (e.g., identifying transition states or potential contours).

  3. Navigate complex spatial layouts, such as inferring global topologies from limited perspective views to generate accurate bird's-eye cognitive maps of scenes.

  4. Decode and reason over structured artifacts, such as interpreting TikZ code to visualize geometric relationships or infer Boolean logic from circuit diagrams and truth tables.

  5. Synthesize complex arguments by retrieving the correct economic charts that support a nuanced comparative analysis (push vs. pull motives) based on abstract skill-demand alignment, rather than just matching keywords.

  6. Retrieve art or historical comics based on implicit symbolic cues and genre conventions rather than explicit entity names or surface visual similarity alone.

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