ForestHG-Trace: Traceable Long-Horizon Ecological Reasoning over Large-Scale Forest Scenes

2026-05-26

AI Summary

This work addresses the lack of traceability and long-range reasoning capability in multi-step inference for large-scale forest remote sensing ecological question answering. It proposes modeling multimodal NEON forest scenes as an ecological hypergraph to support high-order relational reasoning. By leveraging a large language model (LLM)-guided agent that invokes deterministic tools—such as read, filter, and aggregate—the approach generates replayable execution trajectories and compact evidence records, thereby enabling an auditable long-range reasoning mechanism. The contributions include an ecological hypergraph representation, a tool-calling agent framework, a method for generating executable reasoning traces, and ForestTraceQA, a multi-type ecological question-answering benchmark. Experiments demonstrate that the proposed method significantly improves answer accuracy and execution faithfulness on ForestTraceQA, confirming that reasoning depth is a critical bottleneck in long-range ecological QA.

Abstract

Remote sensing question answering (RS-QA) often requires more than direct semantic prediction, especially in large-scale forest scenes where ecological analysis involves multi-step filtering, numerical aggregation, neighborhood reasoning, and verifiable evidence. We introduce ForestHG-Trace, a framework for traceable long-horizon ecological reasoning over forest environments. It represents multimodal NEON forest scenes as ecological hypergraphs, where tree instances, spatial units, semantic groups, and neighborhood relations support higher-order reasoning beyond pairwise scene graphs. An LLM-guided agent then invokes deterministic tools for reading, filtering, expansion, aggregation, comparison, and auditing, producing replayable execution traces and compact evidence records rather than only free-form answers. We further construct ForestTraceQA, an executable benchmark for evaluating ecological QA across diverse task types and reasoning depths. Experiments show that ForestHG-Trace substantially improves answer accuracy and execution faithfulness over single-step baselines and scene-graph agents, while highlighting execution depth as the main bottleneck for long-horizon ecological QA.

Problem

• remote sensing question answering
• ecological reasoning
• long-horizon reasoning
• forest scenes
• traceable reasoning

Innovation

• ecological hypergraph
• traceable reasoning
• LLM-guided agent
• executable QA benchmark
• long-horizon reasoning