arxiv: 2604.07146 · v2 · submitted 2026-04-08 · 💻 cs.CV

Recognition: 2 theorem links

· Lean Theorem

Learning to Search: A Decision-Based Agent for Knowledge-Based Visual Question Answering

Zhuohong Chen , Zhenxian Wu , Yunyao Yu , Hangrui Xu , Zirui Liao , Zhifang Liu , Xiangwen Deng , Pen Jiao

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Haoqian Wang

Authors on Pith no claims yet

Pith reviewed 2026-05-10 17:49 UTC · model grok-4.3

classification 💻 cs.CV

keywords knowledge-based visual question answeringsearch agentmulti-step decision makingretrieval-augmented generationtrajectory supervisionadaptive retrievalaction selectionvisual reasoning

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The pith

A search agent learns to choose retrieval or answer actions step by step to solve knowledge-based visual questions more adaptively than fixed pipelines.

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

The paper argues that fixed retrieval-augmented pipelines for knowledge-based visual question answering cannot adapt well to different question types because they separate retrieval from reasoning and cannot decide when to search or stop. Modeling the task instead as a multi-step decision process lets an agent observe its current information state and pick among four actions: answer the question, retrieve images, retrieve text, or generate a caption. An automated pipeline first records full trajectories of these decisions and tool uses, then uses the trajectories as supervision to fine-tune the agent. If this works, the retrieved evidence aligns more closely with the question and the system handles rare entities and long-tail facts more reliably than prior methods.

Core claim

We reformulate KB-VQA as a search-agent problem and model the solving process as a multi-step decision-making procedure. At each step, the agent selects one of four actions-Answer, Image Retrieval, Text Retrieval, and Caption-based on its current information state. We further design an automated pipeline to collect multi-step trajectories that record the agent's reasoning process, tool usage, and intermediate decisions. These trajectories are then used as supervision for fine-tuning.

What carries the argument

The multi-step decision agent that selects among Answer, Image Retrieval, Text Retrieval, and Caption actions according to its current information state, trained on trajectories collected by an automated pipeline.

Load-bearing premise

The automated pipeline reliably produces high-quality trajectories that give effective, unbiased supervision for learning good decision policies.

What would settle it

Running the trained agent on the InfoSeek or E-VQA test sets and measuring no gain in accuracy over standard fixed retrieval-augmented baselines would falsify the claimed advantage.

Figures

Figures reproduced from arXiv: 2604.07146 by Hangrui Xu, Haoqian Wang, Pen Jiao, Xiangwen Deng, Yunyao Yu, Zhenxian Wu, Zhifang Liu, Zhuohong Chen, Zirui Liao.

**Figure 1.** Figure 1: Comparison of three paradigms for KB-VQA: MLLMs without retrieval, MLLMs with RAG, and our search agent. (a) MLLMs without Retrieval lack access to external knowledge and often fail on long-tail queries. (b) MLLMs with RAG introduce external context but may suffer from noisy or mismatched retrieval due to fixed pipelines. (c) Our Search Agent formulates KB-VQA as a multi-step decision process, enabling ada… view at source ↗

**Figure 2.** Figure 2: Overview of the proposed DBAgent framework and trajectory-based training pipeline. Top: Inference-time decision-making, where the agent dynamically selects among multiple actions (e.g., image retrieval, text retrieval, captioning, and answering) based on the current information state. Below: Construction of multi-step reasoning trajectories, data curation rules, and linearization into structured token sequ… view at source ↗

**Figure 3.** Figure 3: ablation studies on the scale of knowledge [PITH_FULL_IMAGE:figures/full_fig_p008_3.png] view at source ↗

**Figure 4.** Figure 4: Direct answering without retrieval. The model [PITH_FULL_IMAGE:figures/full_fig_p022_4.png] view at source ↗

**Figure 5.** Figure 5: Single-step image retrieval. The model cannot confidently identify the food from the image, triggers [PITH_FULL_IMAGE:figures/full_fig_p023_5.png] view at source ↗

**Figure 6.** Figure 6: Single-step text retrieval. The model recognizes the mountain but lacks specific knowledge about its [PITH_FULL_IMAGE:figures/full_fig_p023_6.png] view at source ↗

**Figure 7.** Figure 7: Two-step trajectory with caption-guided refinement. The model first performs image retrieval to identify [PITH_FULL_IMAGE:figures/full_fig_p024_7.png] view at source ↗

**Figure 8.** Figure 8: Multi-step text query refinement. The initial query fails to return the required attribute, so the model [PITH_FULL_IMAGE:figures/full_fig_p024_8.png] view at source ↗

**Figure 9.** Figure 9: Failure mode I: correct retrieval but incorrect reasoning. Although the retrieved evidence contains the [PITH_FULL_IMAGE:figures/full_fig_p025_9.png] view at source ↗

**Figure 10.** Figure 10: Failure mode II: incorrect retrieval. The retrieved evidence is about a different or loosely related entity, [PITH_FULL_IMAGE:figures/full_fig_p025_10.png] view at source ↗

read the original abstract

Knowledge-based visual question answering (KB-VQA) requires vision-language models to understand images and use external knowledge, especially for rare entities and long-tail facts. Most existing retrieval-augmented generation (RAG) methods adopt a fixed pipeline that sequentially retrieves information, filters it, and then produces an answer. Such a design makes it difficult to adapt to diverse question types. Moreover, it separates retrieval from reasoning, making it hard for the model to decide when to search, how to refine queries, or when to stop. As a result, the retrieved evidence is often poorly aligned with the question. To address these limitations, we reformulate KB-VQA as a search-agent problem and model the solving process as a multi-step decision-making procedure. At each step, the agent selects one of four actions-Answer, Image Retrieval, Text Retrieval, and Caption-based on its current information state. We further design an automated pipeline to collect multi-step trajectories that record the agent's reasoning process, tool usage, and intermediate decisions. These trajectories are then used as supervision for fine-tuning. Experiments on InfoSeek and E-VQA demonstrate that our method achieves state-of-the-art performance, consistently outperforming prior baselines and confirming the effectiveness of our framework.

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

The paper recasts KB-VQA as a four-action decision agent trained on auto-collected trajectories, but the SOTA claims rest on an abstract with no numbers or ablations, so the gains are hard to attribute.

read the letter

The main takeaway is that this work moves KB-VQA away from fixed retrieval-then-answer pipelines by letting the model pick among Answer, Image Retrieval, Text Retrieval, or Caption at each step and trains it on trajectories gathered by an automated pipeline. That framing directly targets the rigidity the authors point out in prior RAG setups, where the model cannot decide when to fetch more evidence or when to stop based on what it already has. The automated collection step is a reasonable way to generate supervision without hand-labeling every decision sequence, and it fits the goal of handling long-tail knowledge questions that need different retrieval behaviors. The paper is clear about the motivation and the action set, which gives the idea a concrete shape that readers can build on. The central problem is that the abstract asserts state-of-the-art results on InfoSeek and E-VQA but supplies no baselines, metrics, error breakdowns, or ablation numbers. Without those, it is impossible to judge whether the agent structure itself produces the improvement or whether the gains come from the underlying retrievers, the trajectory generator, or other unstated changes. The stress-test worry about the collection pipeline embedding its own heuristics is on point here; if the recorded paths mostly reflect whatever solver was used to create them, fine-tuning may amount to imitation rather than learning genuinely better stopping or refinement policies. The four-action design also leaves open questions about state representation and loop prevention that would need checking in the full text. This is for researchers already working on adaptive retrieval in vision-language models who want to test agent-style control instead of static pipelines. A reader in that niche can extract the decision framing and try it, but would need the experimental details to assess the results. The idea is focused enough and addresses a documented limitation in the literature that it should go to peer review rather than a desk reject; referees can verify the numbers and the trajectory quality directly.

Referee Report

2 major / 1 minor

Summary. The paper proposes reformulating KB-VQA as a search-agent problem modeled as a multi-step decision-making procedure. The agent selects one of four actions—Answer, Image Retrieval, Text Retrieval, or Caption—at each step based on its current information state. An automated pipeline collects multi-step trajectories that record reasoning, tool usage, and decisions for use as supervision to fine-tune the model. Experiments on the InfoSeek and E-VQA datasets are claimed to demonstrate state-of-the-art performance, outperforming prior baselines.

Significance. Should the empirical claims prove robust upon detailed verification, this work has the potential to improve KB-VQA systems by integrating retrieval and reasoning through learned adaptive policies rather than rigid sequential pipelines. This could lead to better handling of diverse question types and improved alignment of retrieved evidence with questions involving rare or long-tail knowledge.

major comments (2)

[Method section (trajectory collection)] The automated pipeline used to collect multi-step trajectories for supervision is described only at a high level. No specifics are given on the heuristics for action selection, prevention of loops or suboptimal paths, or validation that the trajectories provide high-quality, unbiased supervision. This is a load-bearing component for the claim that the decision framework yields superior policies, as the gains could stem from imitating the pipeline rather than learning optimal decisions.
[Experiments section] The manuscript asserts state-of-the-art results on InfoSeek and E-VQA but supplies no experimental details, baselines, metrics, error analysis, or ablation studies in the provided text. This absence prevents assessment of whether the performance improvements are attributable to the proposed agent framework.

minor comments (1)

The abstract refers to a 'Caption-based' action without clarifying whether this involves generating or retrieving captions; this should be defined precisely in the method for clarity.

Simulated Author's Rebuttal

2 responses · 0 unresolved

We thank the referee for the constructive feedback. The comments identify key areas where additional clarity and detail will strengthen the manuscript. We address each major comment below and commit to a revised version that incorporates the requested expansions.

read point-by-point responses

Referee: [Method section (trajectory collection)] The automated pipeline used to collect multi-step trajectories for supervision is described only at a high level. No specifics are given on the heuristics for action selection, prevention of loops or suboptimal paths, or validation that the trajectories provide high-quality, unbiased supervision. This is a load-bearing component for the claim that the decision framework yields superior policies, as the gains could stem from imitating the pipeline rather than learning optimal decisions.

Authors: We agree that the current description of the automated pipeline is high-level and that more specifics are needed to substantiate the quality of the supervision. In the revised manuscript we will expand the Methods section with concrete details on the heuristics for action selection (including the exact rules and thresholds used to decide between Answer, Image Retrieval, Text Retrieval, and Caption), mechanisms to detect and prevent loops (such as state hashing and maximum-step caps), and validation procedures (including sampling rates for manual review, quantitative checks for path diversity, and bias metrics comparing trajectory distributions to the original datasets). These additions will clarify how the collected trajectories support learning of adaptive policies rather than simple imitation. revision: yes
Referee: [Experiments section] The manuscript asserts state-of-the-art results on InfoSeek and E-VQA but supplies no experimental details, baselines, metrics, error analysis, or ablation studies in the provided text. This absence prevents assessment of whether the performance improvements are attributable to the proposed agent framework.

Authors: The full manuscript contains an Experiments section reporting results on InfoSeek and E-VQA, but we acknowledge that the version reviewed may have presented these results too concisely. In the revision we will expand this section to include complete experimental details: full descriptions of all baselines (prior KB-VQA and RAG methods), the exact evaluation metrics, comprehensive error analysis broken down by question type and knowledge rarity, and ablation studies isolating the contribution of the learned decision policy versus fixed pipelines. Additional tables and figures will be added to make the attribution of gains to the agent framework explicit and verifiable. revision: yes

Circularity Check

0 steps flagged

No significant circularity; empirical agent framework evaluated on external benchmarks

full rationale

The paper reformulates KB-VQA as a four-action search agent, collects trajectories via an automated pipeline for fine-tuning supervision, and reports SOTA results on InfoSeek and E-VQA. No equations, fitted parameters renamed as predictions, self-citations, or uniqueness theorems appear in the provided text. The central claim rests on empirical performance against external benchmarks rather than any derivation that reduces to its inputs by construction. This is a standard self-contained empirical setup with no load-bearing self-referential steps.

Axiom & Free-Parameter Ledger

0 free parameters · 2 axioms · 0 invented entities

The central claim rests on the assumption that the four actions suffice for diverse KB-VQA questions and that automatically generated trajectories provide useful supervision; these are domain assumptions not independently verified in the abstract.

axioms (2)

domain assumption The four actions (Answer, Image Retrieval, Text Retrieval, Caption) are sufficient to solve KB-VQA tasks
Invoked when reformulating the problem as a search-agent decision process.
domain assumption Automatically collected trajectories can serve as effective supervision for fine-tuning the agent's policy
Core premise of the training pipeline described in the abstract.

pith-pipeline@v0.9.0 · 5546 in / 1316 out tokens · 87592 ms · 2026-05-10T17:49:17.612778+00:00 · methodology

discussion (0)

Lean theorems connected to this paper

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IndisputableMonolith/Cost/FunctionalEquation.lean washburn_uniqueness_aczel unclear

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unclear
Relation between the paper passage and the cited Recognition theorem.

We further design an automated pipeline to collect multi-step trajectories that record the agent's reasoning process, tool usage, and intermediate decisions.

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Reference graph

Works this paper leans on

5 extracted references · 4 canonical work pages · 2 internal anchors

[1]

Can pre-trained vision and language models answer visual information-seeking questions? arXiv preprint arXiv:2302.11713, 2023

Can pre-trained vision and language models answer visual information-seeking questions?ArXiv, abs/2302.11713. Zhuo Chen, Zhengxian Wu, Zirui Liao, Shenao Jiang, Hangrui Xu, Yang Chen, Chaokui Su, Xiaoyu Liu, and Haoqian Wang. 2026. R3g: A reasoning- retrieval-reranking framework for vision-centric an- swer generation.ArXiv, abs/2602.00104. Changin Choi, W...

work page arXiv 2026
[2]

ReSeek: A Self-Correcting Framework for Search Agents with Instructive Rewards

Cross-modal retrieval for knowledge-based visual question answering. InEuropean Conference on Information Retrieval. Shiyu Li, Yang Tang, Yifan Wang, Peiming Li, and Xi Chen. 2025. Reseek: A self-correcting framework for search agents with instructive rewards.ArXiv, abs/2510.00568. Yangning Li, Yinghui Li, Xinyu Wang, Yong Jiang, Zhen Zhang, Xinran Zheng,...

work page internal anchor Pith review arXiv 2025
[3]

arXiv preprint arXiv:2410.08876 (2024),https://arxiv.org/abs/2410.08876

Rora-vlm: Robust retrieval-augmented vision language models.ArXiv, abs/2410.08876. Xin Su, Man Luo, Kris W Pan, Tien Pei Chou, Vasudev Lal, and Phillip Howard. 2024. Sk-vqa: Synthetic knowledge generation at scale for training context- augmented multimodal llms.ArXiv, abs/2406.19593. Michael Sullivan, Mareike Hartmann, and Alexander Koller. 2025. Procedur...

work page arXiv 2024
[4]

PORTool: Importance-Aware Policy Optimization with Rewarded Tree for Multi-Tool-Integrated Reasoning

Portool: Tool-use llm training with rewarded tree.ArXiv, abs/2510.26020. Zhengxian Wu, Kai Shi, Chuanrui Zhang, Zirui Liao, Jun Yang, Ni Yang, Qiuying Peng, Luyuan Zhang, Hangrui Xu, Tianhuang Su, Zhenyu Yang, Haonan Lu, and Haoqian Wang. 2026. When models judge themselves: Unsupervised self-evolution for multi- modal reasoning. Junbin Xiao, Nanxin Huang,...

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[5]

ground truth

Fine-grained knowledge structuring and re- trieval for visual question answering. Yuxiang Zheng, Dayuan Fu, Xiangkun Hu, Xiaojie Cai, Lyumanshan Ye, Pengrui Lu, and Pengfei Liu. 2025. Deepresearcher: Scaling deep research via reinforce- ment learning in real-world environments. InCon- ference on Empirical Methods in Natural Language Processing. A Datasets...

2025