STA-CoT: Structured Target-Centric Agentic Chain-of-Thought for Consistent Multi-Image Geological Reasoning

ACL ARR 2025 May Submission6797 Authors

Abstract: Reliable multi-image geological reasoning is essential for automating expert tasks in remote-sensing mineral exploration, yet remains challenging for multimodal large language models (MLLMs) due to the need for locating target areas, accurate cross-image referencing, and consistency over long reasoning chains. We propose STA-CoT, a Structured Target-centric Agentic Chain-of-Thought framework that orchestrates planning, execution, and verification agents to decompose, ground, and iteratively refine reasoning steps over geological and hyperspectral image sets. By aligning each reasoning step to specific image target areas and enforcing consistency through agentic verification and majority voting, STA-CoT robustly mitigates tool errors, long-chain inconsistencies, and error propagation. We rigorously evaluate STA-CoT on MineBench, a dedicated benchmark for multi-image mineral exploration, demonstrating substantial improvements over existing multimodal chain-of-thought and agentic baselines. Our results establish STA-CoT as a reliable and robust solution for consistent multi-image geological reasoning, advancing automated scientific discovery in mineral exploration.

Paper Type: Long

Research Area: Multimodality and Language Grounding to Vision, Robotics and Beyond

Research Area Keywords: vision question answering, cross-modal application, cross-modal information extraction

Contribution Types: Approaches to low-resource settings, Data analysis

Languages Studied: English

Submission Number: 6797