STA-CoT: Structured Target-Centric Agentic Chain-of-Thought for Consistent Multi-Image Geological Reasoning
Bibliographic record
Abstract
Reliable multi-image geological reasoning is essential for automating expert tasks in remotesensing 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.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.004 | 0.015 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.002 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.003 |
| Scholarly communication | 0.003 | 0.004 |
| Open science | 0.005 | 0.007 |
| Research integrity | 0.002 | 0.003 |
| Insufficient payload (model declined to judge) | 0.005 | 0.002 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".