A Tree-Based Search Algorithm with Global Pheromone and Local Signal Guidance for Scientific Chart Reasoning

• Published in: Mathematics (Basel) Vol. 13; no. 17; p. 2739
• Main Authors: Zhou, Min, Qi, Zhiheng, Zhu, Tianlin, Vijg, Jan, Huang, Xiaoshui
• Format: Journal Article
• Language: English
• Published: Basel MDPI AG 01.09.2025
• Subjects: Algorithms; Analysis; Ant colony optimization; Charts; Collaboration; Combinatorial analysis; Data analysis; Decision making; Effectiveness; Efficiency; Feedback; Language; Large language models; Markov decision processes; Markov processes; Monte Carlo methods; Multiagent systems; Natural language; Natural language processing; Optimization; Pheromones; Questions; Reasoning; Search algorithms; Search methods; stochastic search algorithms; Success; Transition probabilities; Visual tasks
• ISSN: 2227-7390; 2227-7390
• DOI: 10.3390/math13172739

Chart reasoning, a critical task for automating data interpretation in domains such as aiding scientific data analysis and medical diagnostics, leverages large-scale vision language models (VLMs) to interpret chart images and answer natural language questions, enabling semantic understanding that enhances knowledge accessibility and supports data-driven decision making across diverse domains. In this work, we formalize chart reasoning as a sequential decision-making problem governed by a Markov Decision Process (MDP), thereby providing a mathematically grounded framework for analyzing visual question answering tasks. While recent advances such as multi-step reasoning with Monte Carlo tree search (MCTS) offer interpretable and stochastic planning capabilities, these methods often suffer from redundant path exploration and inefficient reward propagation. To address these challenges, we propose a novel algorithmic framework that integrates a pheromone-guided search strategy inspired by Ant Colony Optimization (ACO). In our approach, chart reasoning is cast as a combinatorial optimization problem over a dynamically evolving search tree, where path desirability is governed by pheromone concentration functions that capture global phenomena across search episodes and are reinforced through trajectory-level rewards. Transition probabilities are further modulated by local signals, which are evaluations derived from the immediate linguistic feedback of large language models. This enables fine grained decision making at each step while preserving long-term planning efficacy. Extensive experiments across four benchmark datasets, ChartQA, MathVista, GRAB, and ChartX, demonstrate the effectiveness of our approach, with multi-agent reasoning and pheromone guidance yielding success rate improvements of +18.4% and +7.6%, respectively.