Earth-Agent-Pro: Towards Real-World Full-Chain Earth Observation with Agents
Zhutao Lv, Chenhao Dang, Yi Feng, Yanpei Gong, Xiaolei Wang, Junyan Ye, Conghui He, Weijia Li
cs.CV · cs.AI · cs.CL
Quick verdict
New skill-guided EO agent + 744-q benchmark; +20.95 pts over ReAct (GPT-5), but planner trains on the test workflows' skeletons — promising, needs scrutiny.
Summary
The paper introduces Earth-Agent-Pro, a Plan-and-Execute agent framework for full-chain Earth observation (EO) tasks, together with Earth-Bench-Pro, a 744-question benchmark built from 248 expert-curated task cores under three matched regimes (instruction following, autonomous planning, open-world execution). The framework separates a Planner, which composes tool workflows under expert-authored skills via a four-role debate, from an Executor that grounds tool arguments node-by-node, backed by workflow-centered structured memory and suffix-only repair. Role-specialized LoRA adapters are trained on 2,480 dependency-preserving trajectories derived from the benchmark's 248 seed workflows: sequence-level SFT for planning and node-level GRPO with exact-match rewards for argument grounding. With a shared GPT-5 backbone, the framework reports 66.13% LLM-as-Judge accuracy on the 248 open-world questions, +20.95 points over ReAct, and +24.44 on Tools-In-Order; joint adapters lift tuned Qwen3.5-9B from 38.31% to 50.00%. Limitations: parameter accuracy peaks at 32.77% and distance-measurement accuracy is near zero; on Product tasks Codex (75.00%) beats the best variant (55.00%); the headline gains rest on the authors' LLM-as-Judge metric while embedding match is worst among frameworks (13.31%). Most fundamentally, the training data instantiate the same 248 workflow skeletons as the test tasks, so trajectory-fidelity gains may partly reflect memorized skeletons; code and data are 'coming soon', and no significance testing or judge-model specification is provided.
Review complete
Assessment at a glance
5
risk flags identified
● high 1
● medium 3
● low 1
4 strengths · 5 weaknesses · 5 author questions
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Key claims under review
- Open-world execution — runtime data discovery, acquisition, and open-ended evidence-grounded answering — is the hardest of the three matched regimes across all evaluated backbones (Fig. 2).
- Under a shared GPT-5 backbone, tool set, and skills, Earth-Agent-Pro outperforms ReAct, AFlow, and OpenEarthAgent on Earth-Bench-OW: 66.13% vs 45.18% LLM-as-Judge and 71.97% vs 47.53% Tools-In-Order (Table VI).
- Role-specialized adapters are complementary: joint Planner SFT and Executor GRPO raise Qwen3.5-9B from 38.31% to 50.00% LLM-as-Judge, with the planner driving workflow metrics and the executor driving answer and parameter metrics (Tables VIII–X).
- Earth-Bench-Pro is claimed to be the only compared EO benchmark covering all seven listed dimensions, including runtime real-data acquisition, executable trajectories, and open-ended quantitative QA (Table I).
- Expert skills and role-specialized training are complementary: adding skills raises the fine-tuned 9B judge accuracy by 12.90 points (Table VII).
Strengths
Matched three-regime benchmark design
The same 248 task cores are instantiated as Instruction-Following, Autonomous-Planning, and Open-World questions (Sec. III-A), so difficulty can be attributed to removed scaffolding rather than task mix; Open-World Execution is hardest for every system plotted (Fig. 2).
Execution-grounded annotation with quality control
Experts re-executed all 248 solutions, regenerated answers from tool outputs, and checked query-requirements, cross-call dependency, and answer-evidence consistency (Sec. III-C); the verified workflows contain 1,765 calls (avg 7.1/task, 84 tools).
Clean component attribution
Candid limitation reporting
Weaknesses
Training data share skeletons with the test set
Headline rests on an unvalidated LLM judge
No statistical uncertainty anywhere
Arbitrary correctness threshold and underspecified scoring
Author-graded comparisons and unavailable artifacts
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Scope & Claims
Earth-Agent-Pro targets *open-world EO execution*: from a high-level request (phenomenon, region, period), the agent must discover and acquire data at runtime, preprocess it, run domain computations, and produce an open-ended answer grounded in execution evidence. The paper ships a pair: Earth-Bench-Pro (248 task cores × 3 matched regimes = 744 questions, Sec. III) and the agent framework (Secs. IV–V). Central claims: (1) Open-World Execution is the hardest regime across all plotted backbones (Fig. 2); (2) under a shared GPT-5 backbone, tool set, and skills, the framework beats ReAct, AFlow, and OpenEarthAgent — 66.13% vs 45.18% LLM-as-Judge, 71.97% vs 47.53% Tools-In-Order (Table VI); (3) role-specialized LoRA adapters lift Qwen3.5-9B from 38.31% to 50.00% (Tables II, VIII); (4) the benchmark uniquely covers seven evaluated dimensions (Table I).
The remaining sections cover Methodology Assessment, Statistical Evidence, Results & Reproducibility, Limitations & Risks, and Verdict & Recommendations.
- All 4 strengths & 5 weaknesses, analyzed in detail
- 5 risk flags with severity, section & reviewer questions
- Methods & statistical evidence assessment
- 5 questions a peer reviewer would ask
- Full six-dimension evaluation report & recommendations
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Frequently asked questions
What is Earth-Agent-Pro?
A plan-and-execute LLM agent framework for Earth observation tasks: a planner composes tool workflows guided by expert-written skills, an executor grounds each tool call against runtime data, and structured memory lets the system repair only the affected workflow suffix when a step fails. It is specialized with role-specific LoRA adapters (planner supervised fine-tuning plus executor group-relative policy optimization).
What is Earth-Bench-Pro and how big is it?
A 744-question benchmark built from 248 expert-curated Earth observation task cores, each instantiated in three matched regimes: instruction following (the tool order is given), autonomous planning (the agent must find the order), and open-world execution (the agent must discover and acquire data at runtime and answer open-endedly). The open-world set alone has 248 questions with about 7.1 tool calls per task.
How much better is Earth-Agent-Pro than ReAct?
With the same GPT-5 backbone, tools, and skills, it scores 66.13% vs 45.18% LLM-as-Judge accuracy (+20.95 points) and 71.97% vs 47.53% Tools-In-Order (+24.44 points) on the 248 open-world execution questions, per the paper's controlled framework comparison.
Is the code or dataset available?
The paper states that code and datasets 'will be released soon'; at review time they are not yet available, and the evaluation environment is a custom 112-tool Earth observation stack, so independent verification is currently not possible.
About this review
This is an AI-generated preliminary review by Evolve.Pub, produced from the openly available text of the paper. It is not peer review and does not represent the authors' views. The canonical record of the paper is at the publisher link above.
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