ForestHG-Trace: Traceable Long-Horizon Ecological Reasoning over Large-Scale Forest Scenes
Quick Take
ForestHG-Trace introduces a framework for traceable ecological reasoning in large-scale forest scenes, enhancing accuracy and execution fidelity in remote sensing question answering. By utilizing ecological hypergraphs and an LLM-guided agent, it significantly outperforms single-step baselines and scene-graph agents, revealing execution depth as a critical challenge. The accompanying ForestTraceQA benchmark facilitates diverse ecological QA evaluations.
Key Points
- ForestHG-Trace employs ecological hypergraphs for advanced reasoning in forest environments.
- An LLM-guided agent performs multi-step ecological analysis, producing verifiable execution traces.
- The framework improves answer accuracy and execution fidelity over traditional methods.
- ForestTraceQA benchmark evaluates ecological QA across various tasks and reasoning depths.
- Execution depth is identified as the main bottleneck for long-horizon ecological QA.
Article Content
From source RSS / original summaryarXiv:2605. 27590v1 Announce Type: new 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.
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