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AstroReason-Bench: Evaluating Unified Agentic Planning across Heterogeneous Space Planning Problems
Weiyi Wang, Xinchi Chen, Jingjing Gong, Xuanjing Huang, Xipeng Qiu
TL;DR
Agentic planning has been evaluated mostly in symbolic or weakly grounded environments, leaving performance under realistic space-planning constraints underexplored. AstroReason-Bench unifies heterogeneous Space Planning Problems through physics-aligned environments and agent-oriented tools, and its evaluation finds that agents remain behind specialized solvers while showing zero-shot adaptability to novel structures. The benchmark therefore serves as a diagnostic testbed for agentic planning in space.
Problem
Existing agent benchmarks provide limited evidence about performance in space-planning domains with strict physical constraints and long-horizon decision-making.
Method
AstroReason-Bench evaluates heterogeneous Space Planning Problems through a unified physics-aligned protocol with semantic MCP and programmatic Python interfaces.
Results
Agents substantially underperform specialized solvers, yet show zero-shot adaptability and compound-constraint reasoning on some novel problem structures.
Takeaways & Limitations
AstroReason-Bench provides a rigorous diagnostic platform for studying adaptability, transfer, and learning in agentic space planning.
Takeaways & Limitations
The study focuses on Flash-class models in a standard ReAct scaffold, and its agent–solver comparison is not compute-matched.
Abstract
from arXiv · showhide
Recent advances in agentic Large Language Models (LLMs) have positioned them as generalist planners capable of reasoning and acting across diverse tasks. However, existing agent benchmarks largely focus on symbolic or weakly grounded environments, leaving their performance in physics-constrained real-world domains underexplored. We introduce AstroReason-Bench, a comprehensive benchmark for evaluating agentic planning in Space Planning Problems (SPP), a family of high-stakes problems with heterogeneous objectives, strict physical constraints, and long-horizon decision-making. AstroReason-Bench integrates multiple scheduling regimes, including ground station communication and agile Earth observation, and provides a unified agent-oriented interaction protocol. Evaluating on a range of state-of-the-art open- and closed-source agentic LLM systems, we find that current agents substantially underperform specialized solvers, highlighting key limitations of generalist planning under realistic constraints. AstroReason-Bench offers a challenging and diagnostic testbed for future agentic research.
1 Introduction
Agentic systems are increasingly framed as generalist planners, but existing benchmarks provide limited evidence about their reliability under physical constraints and long-horizon requirements. AstroReason-Bench addresses this gap by unifying heterogeneous space planning problems under standardized agent-oriented evaluation and finds substantial limitations relative to specialized methods.
- Existing agent benchmarks emphasize symbolic, text-based, or weakly grounded environments rather than planning governed by physical laws and irreversible feasibility boundaries.
- Space Planning Problems combine heterogeneous objectives, strict physical and temporal constraints, large combinatorial action spaces, and long-horizon decision-making.
- Isolated SPP benchmarks use incompatible assumptions, interfaces, and metrics, limiting evaluation of one agent’s adaptation across structurally diverse planning environments.
- AstroReason-Bench unifies representative SPP sub-problems through a physics-aligned, agent-oriented interaction and evaluation protocol.The benchmark treats the tasks as a heterogeneous family for stress-testing generalist planners.
- The evaluation covers state-of-the-art open- and closed-source agentic LLMs using minimal MCP tools for observing states, invoking simulators, and executing scheduling decisions.
- Current agentic systems show a substantial performance gap relative to specialized optimization methods, exposing limitations in physics-grounded planning.
2 Related Works
Prior work develops specialized optimization paradigms for distinct satellite scheduling tasks, while newer agent benchmarks assess reasoning and tool use mainly in abstract environments. These lines of work leave a need for evaluation across heterogeneous, physically grounded space-planning settings.
- Satellite scheduling research uses task-specific methods spanning heuristic repair, MILP, reinforcement learning, ALNS, PSO, polygon decomposition, and tailored repeat ground tracks.
- Agent benchmarks evaluate symbolic reasoning, tool use, and execution feedback but generally abstract away high-fidelity physical constraints and domain-specific dynamics.
3 The AstroReason-Bench Suite
AstroReason-Bench combines five physics-constrained space-planning challenges with standardized metrics and procedurally generated, physically valid scenarios. The suite covers communication, observation, coverage, stereo imaging, and latency-sensitive planning over long horizons.
- Simulation Environment & Constraints: The simulation uses SGP4 orbital propagation and enforces resource, kinematic, and concurrency constraints.Energy and data storage are modeled as coupled buffers, while agile-satellite maneuvers require slew and settling time.
- AstroReason-Bench integrates five distinct planning challenges, adapting SatNet and adding four procedurally generated tasks.
- Benchmark Tasks: SatNet measures unsatisfied communication demand using RMS and maximum unsatisfied ratios.
- Benchmark Tasks: Revisit Optimization minimizes observation gaps, while Opportunistic Mapping measures the percentage of fulfilled observation quotas.
- Benchmark Tasks: Regional Coverage maximizes polygonal area captured by continuous imaging swaths using Area-based Recall.
- Benchmark Tasks: Stereo Imaging requires synchronized observation doublets, whereas Latency-Optimization balances persistent connectivity against opportunistic observation.
- Benchmark Tasks: Latency-Optimization evaluates communication Availability and Mean Latency based on valid routing paths and shortest-path propagation delay.
- Procedural Dataset Generation: Scenarios use realistic constellation samples, accessible targets, fixed four-day horizons, controlled resource-to-request ratios, and standard JSON/YAML serialization.
4 Environment and Interface Design
AstroReason-Bench wraps high-fidelity physics models in an agent-ready architecture that separates simulation, state management, interfaces, and cognition. Agents can explore semantically through MCP while using Python for arithmetic and programmatic control.
- The architecture addresses the lack of standardized autonomous-agent interfaces to high-fidelity space-planning simulators.
- The Physics Layer provides stateless orbital, maneuver, and resource computation grounded in SGP4, slew kinematics, and resource models.
- The Scenario Layer maintains inventories, staged actions, and persistent state while locking updates across interfaces.
- The Interface Layer exposes semantic MCP tools for inspection and feedback alongside a Python API for arithmetic and programmatic operations.
- The Cognitive Layer evaluates an LLM in a ReAct loop that maintains a mission plan and refines it through lower-layer interactions.
5 Experiments
The experiments assess agentic systems against optimization baselines and examine their reasoning behavior through qualitative workflow case studies.
- Evaluation covers quantitative benchmarking against traditional optimization baselines and qualitative case studies of agentic reasoning workflows.
5.1 Experiment Setup
The experiment setup runs autonomous LLM agents across five benchmark categories and compares them with heuristic, MILP, reinforcement-learning, and metaheuristic references. The baselines are intentionally reference implementations with limited tuning and computation.
- 150 full mission simulations span five benchmark categories, with agents querying orbital APIs, staging actions, and committing physically validated plans.
- Six frontier LLM agents each complete 25 runs under a two-hour per-case timeout and constrained memory and CPU resources.
- SatNet comparisons use greedy heuristics, Δ-MILP, and PPO reinforcement learning, while novel benchmarks use greedy scheduling and simulated annealing references.
- The baselines are not carefully tuned or optimized for throughput, and each run is limited to approximately 20 minutes.The passage notes that additional computation would likely improve baseline performance; prior MILP optimization required approximately 20 hours.
5.2 Main Results
Across heterogeneous space-planning benchmarks, agents show mixed capabilities: specialized solvers dominate exhaustive optimization, while agents achieve modest success on compound constraints and novel structures.
- SatNet: LLM agents achieve U_rms scores of 0.53–0.59 on SatNet, improving over simple heuristics but trailing MILP and RL specialists.The Δ-MILP solver reaches U_rms=0.30, while RL (PPO) reaches 0.32.
- Revisit Optimization: SA achieves the best Revisit Optimization performance with M_gap=13.65h, while Claude Sonnet 4.5 leads agents at M_gap=18.83h.Claude maintains full mapping coverage while using gap-aware scheduling.
- Regional Coverage: Regional Coverage remains difficult for all approaches, with Gemini 3 Flash reaching only 11% coverage because agents must plan ground-track-aligned observation strips.Strip orientation mismatch and storage exhaustion are identified as primary failure modes.
- Stereo Imaging: Stereo Imaging yields 0% coverage for both baselines but up to 18% for LLM agents that reason about synchronized stereo pairs and coupled constraints.Successful agents search for temporal doublets and stage both observations together.
- Latency Optimization: Latency Optimization defeats nearly all agents, with connection coverage M_com=0; Kat Coder Pro is the sole exception at M_com=0.07 and M_lat=58.4 ms.The successful agent scheduled coordinated multi-hop satellite handoffs in two of five cases.
- Summary of Findings: Across benchmarks, specialized solvers dominate exhaustive search, whereas agents show modest success when recognizing novel compound constraints and network topologies zero-shot.The results position adaptability to unfamiliar problem structures, rather than raw optimization power, as the agents’ relative strength.
5.3 Case Studies
The case studies show that agents fail when they commit to geometrically naive plans or act before exploring relevant environmental structure. Structured planning and multi-hop reasoning can improve outcomes, but tool use and domain knowledge remain incompletely integrated.
- Reasoning About Physical Impossibility: Nearly all agents achieved 0% connection coverage because they searched for a single satellite visible to both ground stations instead of considering relay chains.Earth’s curvature and LEO orbital altitudes make the single-satellite solution geometrically impossible in most scenarios.
- Reasoning About Physical Impossibility: Kat Coder Pro achieved minimal end-to-end connectivity by computing inter-satellite link windows and staging an ISL backbone across three QIANFAN satellites.The successful strategy changed the problem from finding a common view to constructing a network path.
- The Exploration-Exploitation Gap: Agents in regional coverage registered strips before querying ground tracks, producing near-zero coverage because the strips poorly matched orbital geometry.A representative Claude Sonnet 4.5 run registered five randomly oriented Bay of Bengal strips with limited access windows.
- The Exploration-Exploitation Gap: The case studies suggest that structured reasoning phases can unlock domain knowledge, while access to tools or literature alone does not overcome action bias.Agents need prompted exploratory workflows before committing to strategies.
6 Conclusions
AstroReason-Bench evaluates generalist agentic planners across heterogeneous space planning problems using a shared physics engine and agent-oriented interface. Agents show zero-shot adaptability and compound-constraint reasoning, but lag behind specialized methods in resource management and long-horizon spatial reasoning.
- AstroReason-Bench unifies diverse mission profiles under a shared physics engine and agent-oriented interface to expose capabilities and limitations of LLM-based agents.
- Agents demonstrate zero-shot adaptability and reasoning about compound constraints, yet remain behind specialized logic in resource management and long-horizon spatial reasoning.
- AstroReason-Bench provides a testbed for developing agents intended to operate in the constrained environment of space.
Limitations
The study’s conclusions are bounded by its model, workflow, sampling, compute, and benchmark-scope choices. These constraints make the results diagnostic rather than definitive evidence of absolute optimality or comprehensive autonomous space engineering.
- Model and workflow scope: The evaluation uses Flash-class models in a standard ReAct scaffold, so larger models and explicit planning or self-correction workflows may perform better.
- Evaluation uncertainty: Stochastic tool use and limited scenarios may leave reported averages unable to fully capture workflow variance, motivating more episodes and confidence intervals.
- Comparison boundary: The comparison with specialized optimizers is not compute-matched because solvers use extensive offline training while agents operate under fixed online interaction budgets.
- Benchmark scope: The benchmark currently centers on operational scheduling and resource management rather than architectural system design or deep-space trajectory planning.
Ethics Statement
AstroReason-Bench uses public orbital data and procedurally generated scenarios, with automated sandboxed evaluations and safeguards that abstract away low-level spacecraft operations. The authors report no personal or sensitive geographic data and plan to release code and datasets under documented licenses.
- Data and privacy: The benchmark uses publicly available Two-Line Elements and procedurally generated scenarios without personally identifiable information or sensitive geographic attributes.
- Reproducibility: Code and datasets will be released under documented upstream licenses, while fixed timeouts and resource caps support cost-aware reproducibility.
- Safety and compliance: Evaluations are automated and sandboxed, with model licenses observed and aggregate metrics reported without proprietary internals.
- Safety and compliance: The suite abstracts spacecraft operations to high-level scheduling and resource allocation, excluding low-level control and real-world infrastructure procedures to mitigate dual-use risks.