Anagent For Enhancing Scientific Table & Figure Analysis

• URL: http://arxiv.org/abs/2602.10081v2
• Date: Thu, 12 Feb 2026 02:51:40 GMT
• Title: Anagent For Enhancing Scientific Table & Figure Analysis
• Authors: Xuehang Guo, Zhiyong Lu, Tom Hope, Qingyun Wang,
• Abstract summary: Anagent is a framework for enhanced scientific table & figure analysis through four specialized agents.<n>Anagent achieves substantial improvements across 9 broad domains with 170 domains.<n>We show that task-oriented reasoning and context-aware problem-solving are essential for high-quality scientific table & figure analysis.
• Score: 13.604302149501557
• License: http://creativecommons.org/licenses/by/4.0/
• Abstract: In scientific research, analysis requires accurately interpreting complex multimodal knowledge, integrating evidence from different sources, and drawing inferences grounded in domain-specific knowledge. However, current artificial intelligence (AI) systems struggle to consistently demonstrate such capabilities. The complexity and variability of scientific tables and figures, combined with heterogeneous structures and long-context requirements, pose fundamental obstacles to scientific table \& figure analysis. To quantify these challenges, we introduce AnaBench, a large-scale benchmark featuring $63,178$ instances from nine scientific domains, systematically categorized along seven complexity dimensions. To tackle these challenges, we propose Anagent, a multi-agent framework for enhanced scientific table \& figure analysis through four specialized agents: Planner decomposes tasks into actionable subtasks, Expert retrieves task-specific information through targeted tool execution, Solver synthesizes information to generate coherent analysis, and Critic performs iterative refinement through five-dimensional quality assessment. We further develop modular training strategies that leverage supervised finetuning and specialized reinforcement learning to optimize individual capabilities while maintaining effective collaboration. Comprehensive evaluation across 9 broad domains with 170 subdomains demonstrates that Anagent achieves substantial improvements, up to $\uparrow 13.43\%$ in training-free settings and $\uparrow 42.12\%$ with finetuning, while revealing that task-oriented reasoning and context-aware problem-solving are essential for high-quality scientific table \& figure analysis. Our project page: https://xhguo7.github.io/Anagent/.

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