Source-linked AI summary
Toward Self-Adaptive Physical AI: Can LLM Agents Manage Long-Horizon Physical Tasks?
Varun Kaushik, Yayun Tan, Xiaofan Yu
TL;DR
Long-horizon physical tasks require repeated observation and consequential decisions, yet existing approaches may need substantial training or human guidance and may not adapt to environmental change. The paper evaluates a zero-shot multi-agent crop-management framework; it reports competitive outcomes under unchanged conditions and stronger performance than RL under environmental shift.
Problem
Whether LLM agents can autonomously manage long-horizon physical tasks and adapt to environmental changes remains largely unexplored, while existing approaches may require extensive data or retraining.
Source: §1 · p. 1Method
A zero-shot multi-agent framework combines role separation, constrained information access, independent verification, and reflection-guided feedback for crop management.
Source: §1 · p. 2Results
The framework achieves competitive crop performance with fewer resources than RL in the original environment and significantly outperforms RL when environmental conditions shift.
Source: §1 · p. 2, §5.2.2 · p. 8, §7 · p. 9Takeaways & Limitations
Structured multi-agent reasoning and outcome-based feedback offer a promising direction for self-adaptive physical AI systems.
Source: §7 · p. 9The framework was evaluated entirely in the WOFOSTGym crop environment, which does not capture complete real-world factors.
Source: §6 · p. 9
Abstract
from arXiv · showhide
Large Language Model (LLM) agents offer a promising path toward autonomously managing long-term physical tasks without human intervention. However, physical tasks require agents to continuously observe the environment, make consequential actions, and remain effective as the environment changes. Existing approaches either require substantial data and retraining, or primarily focus on agents operating in the virtual world. In this work, we explore the feasibility of building a self-adaptive physical AI agent that manages long-term physical tasks in a zero-shot manner and adapts to environmental changes without human intervention. We design a multi-agent framework that integrates planning, tool calling, observation, and verification, and evaluate it on agricultural tasks against reinforcement learning (RL) agents under different weather patterns. Our results show that zero-shot LLM agents can achieve comparable management outcomes to RL agents under the same weather pattern and adapt more effectively than RL when evaluated under a shifted environment, highlighting a promising path toward self-adaptive physical AI agents.
1 Introduction
Long-horizon physical tasks demand repeated observation, consequential decisions, and adaptation, while existing approaches can require extensive training or falter under environmental shifts. This work evaluates a zero-shot multi-agent framework for autonomous crop management and adaptation.
1 Introduction
- Long-horizon physical tasks require repeated observation, consequential decisions, and verification, and could otherwise demand repetitive human effort.The paper calls this class of systems physical AI agents.Source: §1 · p. 1
- Physical AI faces costly data demands and environmental shifts that can make trained controllers ineffective, while most existing LLM agents operate virtually and rely on human guidance or verification.The paper asks whether agents can manage long-horizon physical tasks zero-shot and adapt autonomously to environmental changes.Source: §1 · pp. 1–2
- A multi-agent framework combines role separation, constrained information access, independent verification, and reflection-guided feedback to manage long-term physical tasks without task-specific training or human intervention.The framework closes the loop among planning, tool use, observation, and verification.Source: §1 · p. 2
- The zero-shot framework achieves competitive outcomes under unchanged conditions, substantially outperforms RL under environmental shift, and uses a fraction of RL’s fertilizer and irrigation.The evaluation spans crop-management simulations of up to 241 days.Source: §1 · p. 2
2 Related Work
Prior work spans classical control and reinforcement learning for physical processes, as well as LLM agents that plan and act in digital or embodied settings. The paper positions autonomous adaptation to ongoing physical-process changes without retraining or human intervention as insufficiently studied.
2 Related Work
- Physical-world agents include traditional control or reinforcement-learning systems and LLM agents that use language-model reasoning to plan and act.The paper distinguishes these as two broad categories of prior work.Source: §2 · p. 2
2.1 Traditional agents for cyber-physical systems
- Classical rule-based, PID, and model-predictive controllers work in characterized systems but rely on hand-specified rules or explicit models; reinforcement learning remains training-dependent.The paper contrasts these approaches with adapting from live environmental feedback without predefined rules, system models, or policy training.Source: §2.1 · p. 3
2.2 LLM-based agents
- Most LLM-agent demonstrations use digital or simulated worlds, while embodied systems often depend on robot demonstrations or pretrained control policies, leaving autonomous adaptation in ongoing physical processes with limited evidence.Physical feedback can be noisy and partial, and trials can cost time, materials, or safety.Source: §2.2 · p. 3
3 Problem definition
The paper frames crop management as a representative long-horizon physical task with irreversible interventions and delayed outcomes. Its formulation selects actions from evolving observations and interaction history while balancing crop yield against resource use.
3 Problem definition
- Agricultural management stress-tests embodied decision-making through growing seasons lasting months, irreversible irrigation or fertilizer actions, and yield observed only at harvest.The paper uses crop and plant growing as a representative physical AI scenario.Source: §3 · p. 3
- At each decision step, the agent observes crop and growing conditions and selects a management action, with choices depending on both the current environment and prior interventions.The history h_t includes previous observations and actions plus the current observation, and the policy maps that history to an action.Source: §3 · p. 3
- The multi-objective formulation prioritizes maximum crop yield at physiological maturity while penalizing cumulative resource use, with λ controlling the yield–resource trade-off.Resource cost is accumulated across actions through physiological maturity.Source: §3 · pp. 3–4
4 Physical AI Agent Framework Design
The framework organizes physical-task management as a closed loop of specialized agents, constrained decisions, action checks, and feedback. It is designed to ground decisions and adapt subsequent reasoning to observed outcomes.
4.1 Overview
- Specialized layers transform daily crop and environment observations into structured summaries, propose and revise management actions, check operational limits, execute actions, and feed outcomes back into later decisions.The framework’s loop is illustrated in Figure 1.Source: §4.1 · p. 4
4.2 Grounding multi-agent decision making for hallucination mitigation
- Hallucination mitigation separates agent responsibilities and limits information or authority, grounding observations and summaries before interventions are selected.Deterministic perception agents report assigned variables; the Manager synthesizes their reports but cannot introduce measurements or select interventions.Source: §4.2 · p. 5
- The Decision Agent proposes an intervention and amount, the Revision Agent independently checks its quantity, and a deterministic Safety Guard validates the revised action before execution.The Revision Agent may adjust the amount but cannot change the intervention type; the guard checks validity, eligibility, quantity, and encoding.Source: §4.2 · p. 5
4.3 Reflection-guided adaptation to changing environments
- The Reflection Agent sends within-episode, role-specific feedback to the Manager, Decision, and Revision Agents so later reasoning incorporates observed outcomes as conditions evolve.Feedback addresses interpretation support, intervention effectiveness, and action quantity; it updates reasoning without implying parameter learning or permanent policy changes.Source: §4.3 · pp. 5–6, Figure 3 · p. 6
5 Experiments
Experiments compare zero-shot agentic management with PPO across unchanged and shifted WOFOSTGym environments; the framework is competitive without shifts, outperforms transferred RL after shifts, and uses fewer resources.
5.1 Experimental setup
- In WOFOSTGym, policies share 15 observations and 17 actions across 241-day wheat episodes; Environment B shifts weather year, location, and soil while retaining wheat.PPO is trained in Environment A and transferred to B, whereas the agentic framework uses structured reports and operates without task-specific training or parameter updates.Source: §5.1 · pp. 6–7
5.2 Results
- Results compare cumulative reward and maximum yield in unchanged and shifted settings, then compare total resource use.These are the three evaluation metrics described for the results.Source: §5.2 · p. 7, §5.1 · p. 7
5.2.2 Performance under environment shift
- Under shift, reward was 453, 956 vs 4, 798 and yield was 6, 519 kg/ha vs 110 kg/ha for the agentic framework and transferred RL, respectively.Reflection feedback also adjusted decisions: after potassium availability rose from 0.138 to 1.284, the agent avoided another potassium intervention and applied a small amount of nitrogen.Source: Figure 4 · p. 8, Figure 5 · p. 8, §5.2.2 · p. 8
5.2.1 Performance under unchanged conditions
- Without environmental shift, yield was 4, 152 kg/ha vs 5, 976 kg/ha and reward was 216, 390 vs 340, 724 for the zero-shot framework and trained RL, respectively.The framework achieved about 69% of RL yield despite operating zero-shot.Source: §5.2.1 · p. 8, Figure 4 · p. 8, Figure 5 · p. 8
5.2.3 Resource usage across environments
- Across both environments, the agentic framework uses less than 28% of RL resources in every fertilizer and irrigation category.In Environment A, it uses 68/12/72 kg/ha of N/P/K and 21 cm irrigation, versus RL’s 554/142/258 kg/ha and 141 cm.Source: §5.2.3 · pp. 8–9, Table 3 · p. 9
6 Limitations
The evaluation was conducted entirely in the WOFOSTGym crop environment, which does not capture complete real-world factors.
6 Limitations
- Evaluation was entirely within WOFOSTGym, so the study does not capture complete real-world factors.The authors identify physical-plant testing with sensors and actuators as future work to assess robustness and practical applicability under real growing conditions.Source: §6 · p. 9
7 Conclusion
The conclusion presents zero-shot LLM agents as a potential approach to long-horizon physical-task management, highlighting crop performance, resource use, and adaptation under environmental shifts.
7 Conclusion
- The framework achieved competitive crop performance with substantially fewer resources in the original environment and significantly outperformed RL under shifted conditions without retraining.The authors identify structured multi-agent reasoning and outcome-based feedback as a promising direction for self-adaptive physical AI.Source: §7 · p. 9