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Agent-X: Evaluating Deep Multimodal Reasoning in Vision-Centric Agentic Tasks

ICLR 2026 Conference SubmissionAnonymous Authors
OpenReview Score: 6.0Download Report PDF
Vision centric AgentsDeep ReasoningVLMsTool Use Evaluation
Abstract:

Deep reasoning is fundamental for solving complex tasks, especially in vision-centric scenarios that demand sequential, multimodal understanding. However, existing benchmarks typically evaluate agents with fully synthetic, single-turn queries, limited visual modalities, and lack a framework to assess reasoning quality over multiple steps as required in real-world settings. To address this, we introduce Agent-X, a large-scale benchmark for evaluating vision-centric agents’ multistep and deep reasoning capabilities in real-world, multimodal settings. AgentX features 828 agentic tasks with authentic visual contexts, including images, multi-image comparisons, videos, and instructional text. These tasks span six major agentic environments: general visual reasoning, web browsing, security and surveillance, autonomous driving, sports, and math reasoning. Our benchmark requires agents to integrate tool use with explicit, stepwise decision-making in these diverse settings. In addition, we propose a fine-grained, step-level evaluation framework that assesses the correctness and logical coherence of each reasoning step and the effectiveness of tool usage throughout the task. Our results reveal that even the best-performing models, including GPT, Gemini, and Qwen families, struggle to solve multi-step vision tasks, achieving less than 50% full-chain success. These findings highlight key bottlenecks in current LMM reasoning and tool-use capabilities and identify future research directions in vision-centric agentic reasoning models

Disclaimer
This report is AI-GENERATED using Large Language Models and WisPaper (A scholar search engine). It analyzes academic papers' tasks and contributions against retrieved prior work. While this system identifies POTENTIAL overlaps and novel directions, ITS COVERAGE IS NOT EXHAUSTIVE AND JUDGMENTS ARE APPROXIMATE. These results are intended to assist human reviewers and SHOULD NOT be relied upon as a definitive verdict on novelty.
NOTE that some papers exist in multiple, slightly different versions (e.g., with different titles or URLs). The system may retrieve several versions of the same underlying work. The current automated pipeline does not reliably align or distinguish these cases, so human reviewers will need to disambiguate them manually.
If you have any questions, please contact: mingzhang23@m.fudan.edu.cn

Overview

Overall Novelty Assessment

The paper introduces Agent-X, a benchmark for evaluating vision-centric agents' multistep reasoning across 828 tasks spanning six domains (web browsing, autonomous driving, surveillance, sports, math, general visual reasoning). It resides in the Vision-Centric Agentic Reasoning leaf, which contains only one sibling paper (VideoReasonBench). This represents a relatively sparse research direction within the broader Agentic Task Benchmarks branch, suggesting the specific focus on deep, step-level reasoning evaluation in diverse visual contexts is not yet heavily populated.

The taxonomy reveals neighboring leaves focused on Web and GUI Agent Benchmarks (VisualWebArena, WebVoyager, Windows Agent Arena) and Embodied and Physical Agent Benchmarks (EgoPlan, RoboVQA, Embodied Benchmark). Agent-X diverges from web-specific navigation tasks by emphasizing authentic visual contexts (images, videos, multi-image comparisons) across broader domains. It connects to Static Visual Reasoning Benchmarks through its inclusion of math and general visual reasoning, but distinguishes itself by requiring sequential, multi-step decision-making rather than single-turn queries. The scope_note for Vision-Centric Agentic Reasoning explicitly excludes web-specific and GUI-specific benchmarks, positioning Agent-X as a bridge between static visual reasoning and interactive agentic tasks.

Among 30 candidates examined, the Agent-X benchmark contribution (Contribution A) shows no clear refutation across 10 candidates, suggesting novelty in its specific combination of scale, domain diversity, and authentic visual contexts. The fine-grained step-level evaluation framework (Contribution B) similarly examined 10 candidates with no refutations, indicating this assessment approach may be distinctive. However, the semi-automated pipeline for task construction (Contribution C) encountered 2 refutable candidates among 10 examined, suggesting prior work on automated benchmark generation exists. The limited search scope means these findings reflect top-30 semantic matches rather than exhaustive coverage.

Based on the limited literature search, Agent-X appears to occupy a relatively novel position by combining vision-centric diversity, step-level evaluation, and authentic multimodal contexts. The sparse population of its taxonomy leaf and low refutation rates across most contributions suggest meaningful differentiation from existing benchmarks. However, the analysis covers only top-30 semantic matches and does not capture the full landscape of vision-centric evaluation frameworks or recent concurrent work in this rapidly evolving area.

Taxonomy

Core-task Taxonomy Papers
50
3
Claimed Contributions
30
Contribution Candidate Papers Compared
2
Refutable Paper

Research Landscape Overview

Core task: evaluating deep multimodal reasoning in vision-centric agentic tasks. The field has organized itself around several complementary branches. Multimodal Reasoning Frameworks and Methodologies explore how models integrate visual and linguistic signals, often through chain-of-thought mechanisms (Multimodal Chain-of-Thought[4], GLM-4V Thinking[3]) or visual programming approaches (Visual Programming[12], ViperGPT[20]). Benchmarking and Evaluation Frameworks provide standardized testbeds for measuring agent performance across web navigation (VisualWebArena[1], WebVoyager[15]), GUI interaction (Windows Agent Arena[25], VisualAgentBench[31]), and specialized domains. Application Domains and Specialized Systems target concrete use cases such as autonomous driving (DriveAgent[16], VLR-Driver[19]) and medical reasoning (MedAgent-Pro[43]), while Supporting Capabilities and Mechanisms address foundational needs like memory (Long-Term Memory Agent[27]) and uncertainty handling (Uncertainty-Aware Agentic[13]). Surveys and Conceptual Frameworks (Vision Language Survey[5], Agentic Multimodal Survey[35]) synthesize these threads into broader perspectives. A particularly active line of work focuses on vision-centric agentic reasoning benchmarks that demand multi-step visual understanding and decision-making. Agent-X[0] sits squarely within this cluster, emphasizing deep multimodal reasoning evaluation alongside neighbors like VideoReasonBench[34], which similarly probes temporal visual reasoning in video contexts. Compared to earlier web-focused benchmarks (VisualWebArena[1], WebVoyager[15]) that primarily test navigation and interaction, Agent-X[0] and VideoReasonBench[34] push toward richer reasoning over visual sequences and complex scene understanding. Meanwhile, works like Insight-v[2] and Chain-of-Vision[8] explore how to elicit and structure intermediate reasoning steps, highlighting ongoing questions about whether explicit reasoning traces improve agent robustness or simply add interpretability. The tension between task-specific fine-tuning (Visual Agentic Fine-Tuning[37]) and zero-shot generalization remains a central open challenge across these vision-centric benchmarks.

Claimed Contributions

Agent-X benchmark for vision-centric agentic reasoning

The authors propose a large-scale benchmark comprising 828 agentic tasks with authentic visual contexts (images, videos, multi-image comparisons) spanning six major environments. The benchmark requires agents to integrate tool use with explicit, stepwise decision-making in diverse real-world settings.

10 retrieved papers
Fine-grained step-level evaluation framework

The authors introduce a comprehensive evaluation framework with three modes (Step-by-Step, Deep Reasoning, and Outcome) and multiple metrics to assess reasoning quality, tool usage correctness, and logical coherence at each step, moving beyond surface-level final-answer evaluation.

10 retrieved papers
Semi-automated pipeline for task construction

The authors develop a semi-automated pipeline that combines LMM-generated queries with human refinement and validation to create realistic, tool-augmented reasoning tasks. This approach ensures scalability while maintaining quality through hybrid annotation of queries, reasoning traces, and ground-truth answers.

10 retrieved papers
Can Refute

Core Task Comparisons

Comparisons with papers in the same taxonomy category

Contribution Analysis

Detailed comparisons for each claimed contribution

Contribution

Agent-X benchmark for vision-centric agentic reasoning

The authors propose a large-scale benchmark comprising 828 agentic tasks with authentic visual contexts (images, videos, multi-image comparisons) spanning six major environments. The benchmark requires agents to integrate tool use with explicit, stepwise decision-making in diverse real-world settings.

Contribution

Fine-grained step-level evaluation framework

The authors introduce a comprehensive evaluation framework with three modes (Step-by-Step, Deep Reasoning, and Outcome) and multiple metrics to assess reasoning quality, tool usage correctness, and logical coherence at each step, moving beyond surface-level final-answer evaluation.

Contribution

Semi-automated pipeline for task construction

The authors develop a semi-automated pipeline that combines LMM-generated queries with human refinement and validation to create realistic, tool-augmented reasoning tasks. This approach ensures scalability while maintaining quality through hybrid annotation of queries, reasoning traces, and ground-truth answers.

Table of Contents