STA - C o T : Structured Target-Centric Agentic Chain-of-Thought for Consistent Multi-Image Geological Reasoning (2025)

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.

## introduction multi-image geological reasoning is the task of synthesizing and interpreting geological evidence from multiple remote-sensing images of a region to produce a cross-image rationale over targeted areas in the region @xcite . among its most impactful applications is mineral exploration @xcite , where experts integrate geological and hyperspectral imagery to identify and assess economically valuable mineral deposits. the ability to automate mineral exploration holds tremendous significance, as it accelerates resource discovery essential for global technological infrastructure and sustainability @xcite . a domain expert may spend hours manually correlating structured faults, alteration zones, and spectral signatures across several images to narrow down large survey regions into a small set of promising candidates -demonstrating how automation can drastically reduce the number of regions requiring costly field investigation @xcite . the performance of multi-image geological reasoning fundamentally relies on a model's ability to reason across both spatial and spectral modalities, integrating diverse geological cues distributed among multiple images over one region into a coherent, evidence-based conclusion. this motivates the adoption of multimodal large language models (mllms), which unify visual and textual inputs, as well as the multimodal chain-of-thought (mcot) framework that decomposes complex visual reasoning into interpretable, step-by-step rationales @xcite . recent advances have further strengthened these reasoning frameworks by introducing prompt-based @xcite , structured planning @xcite , tool-augmented decision-making @xcite , and mechanisms for consistency @xcite and verification @xcite . such techniques en-able mllms to not only elaborate their reasoning processes but also actively validate and refine their intermediate outputs, leading to accurate answers. however, these techniques cannot be directly applied to multi-image geological reasoning, as, besides proper geological knowledge, the task uniquely demands (i) accurate interesting target location within each image, (ii) maintenance of a long and complex reasoning chain, and (iii) precise cross-image referencing that exceeds the typical capabilities of existing approaches. in our pilot experiments, when we adopt mcot methods with tool-augmentation and vanilla consistency & verification for this task, we observed three primary categories of failure as in figure 1 : tool output errors, long-chain inconsistencies, and error propagation. these errors arise precisely because this task amplifies the risk of faulty target location by tools due to a lack of domain knowledge, increases the chance for intermediate mistakes to accumulate and propagate along extended reasoning chains, and makes it difficult to maintain consistent references and logic across multiple images -ultimately undermining the reliability of the conclusions. motivated by these challenges, we seek to enable reliable and domain-adapted multi-image reasoning for geological exploration. to this end, we present structured target-centric agentic chain-of-thought, sta-cot, a novel framework designed to deliver consistent and accurate multi-image geological reasoning. it orchestrates specialized agentic modules within a structured chain-of-thought paradigm, explicitly aligning reasoning steps with domain knowledge, visual targets, and cross-image dependencies. by structuring the reasoning process around targeted regions and incorporating agent-based planning, execution, and verification, sta-cot mitigates the unique complexities of geological multi-image tasks. specifically, it coordinates three closely integrated agents: a planner that retrieves relevant geological knowledge and decomposes the complex multimodal task into a sequence of manageable sub-tasks; an executor that performs these sub-tasks by invoking specialized tools and integrates outputs via a rule-based controller to ensure accurate visual alignment; and a verifier that continuously monitors intermediate reasoning steps, triggering a stepwise refinement process to direct the executor to revise faulty outputs, thereby preventing error propagation and maintaining causal stability throughout the chain. as such, sta-cot grounds each reasoning step to specific image regions and systematically tracks cross-image relationships through structured agentic planning. consistency is maintained by continually verifying intermediate outputs, while a majority voting mechanism further reduces long-chain inconsistencies, yielding stable and reliable conclusions in complex multi-image geological reasoning. to rigorously evaluate our approach, we leverage minebench, a dedicated benchmark designed for multi-image mineral exploration tasks, which integrates both geological and hyperspectral remote-sensing data to emulate real-world exploration scenarios @xcite . empirical results demonstrate that sta-cot achieves state-of-theart performance, substantially outperforming prior mcot, multi-agent, and tool-augmented mllm baselines. these affirm the framework's effectiveness in delivering reliable and consistent geological reasoning across complex multi-image contexts. multimodal chain-of-thought. multimodal chain-of-thought (mcot) @xcite reasoning frameworks have rapidly advanced from early prompt-based works that generate stepwise textual rationales grounded in visual content @xcite to sophisticated paradigms like retrieved-augmented, structured and tool-augmented multimodal reasoning. retrieval-augmented generation (rag) injects external or domain-specific knowledge to inform inferences in complex scenarios @xcite . and, structured and planning-based reasoning approaches @xcite , such as graph-of-thought (got) @xcite and compositional cot (ccot) @xcite , explicitly model the dependencies and relationships between reasoning steps, often leveraging graph-based or compositional representations to capture intricate cross-modal interactions. meanwhile, agentic tool-augmented and chain-of-action (coa) methods, including kam-cot @xcite , mm-verify @xcite , and det-cot @xcite , extend mcot by integrating external tools (e.g., visual annotators, object detection, and recognition) to enable interactive perception and iterative refinement of visual evidence. despite these advances, most existing paradigms are evaluated on single-image or short-context tasks, lack robust mechanisms for explicitly track-ing cross-image references. in contrast, sta-cot is designed to tackle the unique challenges of multi-image geological reasoning, where retrievedaugmented domain-contextual planning, accurate alignment and referencing of targeted regions across images, and structured management of long and error-prone reasoning chains are indispensable for consistent decision making. consistency and verification. maintaining logical consistency is particularly challenging in safetycritical or scientific domains like mineral exploration, where errors at intermediate reasoning steps can undermine overall trustworthiness @xcite . existing approaches to consistency in reasoning generally operate at the chain or output level, employing strategies such as majority vote @xcite and post-hoc verification @xcite . while these methods can improve the plausibility of final predictions by selecting among alternative reasoning chains, they offer limited mechanisms for diagnosing and repairing errors within the reasoning process itself @xcite . such coarse-grained consistency enforcement is often insufficient in domains where interpretability and reliability at every reasoning step are required. in contrast, our sta-cot framework addresses this critical gap by introducing an agent-driven, iterative refinement mechanism that enables stepwise error detection and targeted repair, thus supporting robust and trustworthy multimodal reasoning. ## methodology to enable automated mineral exploration with remote-sensing imagery, we formally define the reasoning objective as follows. task formulation. given a mineral-exploring query q (e.g., 'given the following remote-sensing images, can deposits be detected? answer: a. yes b. no.') and a set of remote-sensing images i representing a targeted region, the objective is to determine the presence or absence of deposits: to address the complexity of multi-image geological reasoning -requiring integration of spatial and spectral evidence, cross-image referencing, and logical consistency -we adopt an agentic reasoning framework, as inspired by prior works @xcite @xcite . within this framework, the model not only produces a final answer but also outputs an interpretable reasoning chain: ## conclusion we proposed sta-cot, a structured agentic reasoning framework for consistent multi-image geological reasoning in mineral exploration. sta-cot integrates domain-informed planning, targetcentric execution, and iterative verification to address cross-image dependencies and long-chain inconsistencies. evaluated on the minebench benchmark, sta-cot outperforms prior methods in both accuracy and consistency, particularly excelling in visual-grounded execution and stepwise error correction. our results demonstrate the framework's robustness, efficiency, and practical value for automating expert-level geological reasoning using remote-sensing data.