Toward Self-Adaptive Physical AI: Can LLM Agents Manage Long-Horizon Physical Tasks?

arXiv:2609.13436v1 Announce Type: new Abstract: 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.

LLM agents show promise in managing physical tasks and adapting to changes

The research presents a demonstrable capability of zero-shot LLM agents successfully managing long-term physical tasks and adapting to environmental changes, which could reduce reliance on human intervention in important physical domains like agriculture. This suggests positive material impact with some uncertainty as it is early stage and not yet peer reviewed.

This is an unreviewed preprint demonstrating a narrow research result in simulated agricultural task management, not a deployed system. The implication for human flourishing is minor and indirect, mostly a small step in autonomous agent capability with typical research caveats.

SOURCE     arXiv cs.AI (tier 1)
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DOCUMENT   e3a0c90e-35a3-484c-93fd-71d7d6fcf252
           https://arxiv.org/abs/2609.13436
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EVIDENCE   2 extracted excerpts
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MODEL RUN  3 runs, methodology 1.0.1
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SCORE      +7  (Mixed or uncertain)
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CONFIDENCE 67%