Strategic Evaluation of Planning Strategies for LLM Agents in Cyber-Physical Systems
J. de Curt\`o, I. de Zarz\`a
Why It Matters
What makes this one worth your time
Understanding how different planning architectures affect outcomes in cyber-physical systems can guide the development of more effective and reliable AI-driven control systems.
The paper evaluates planning strategies for LLM agents in smart-grid systems, emphasizing architecture's role in outcomes.
Summary
The paper introduces a benchmark for evaluating planning strategies of LLM agents in cyber-physical systems, specifically within a smart-grid demand-response context. It assesses the impact of different planning architectures on system outcomes, using a controlled environment with predefined executors and prosumers. The study highlights the importance of architecture in planning outcomes and explores the challenges of execution fidelity and quality selection.
Key contributions
- Introduction of a controlled benchmark for evaluating planning strategies in smart-grid systems.
- Analysis of the impact of different planning architectures on system outcomes.
- Identification of challenges in execution fidelity and quality selection within feasible scenarios.
Notable insights
- The study uses a controlled, physics-grounded benchmark to evaluate planning-induced control trajectories.
- It highlights that execution fidelity requires more than just mode agreement, as objective substitution can lead to increased voltage shortfall.
Possible limitations
- Not stated in the abstract
Abstract
arXiv:2608.04265v2 Announce Type: replace-cross Abstract: LLM-agent evaluations commonly measure task success or agreement with a declared plan. In strategic cyber-physical systems, an architecture must also remain appropriate after autonomous participants respond and physics constrains outcomes. We introduce a controlled benchmark of planning-induced control trajectories: ordered planning operations and directives linking execution architecture to strategic response and physical consequences. Four coded executors (predefined, sequential, hierarchical, and search) control demand response for 40 prosumers on a radial feeder. The LLM declares or advises typed policies and mediates communication; schedules, base prosumer dynamics, stochastic actions, and power flow remain explicit code. Paired forced-mode counterfactuals, exact-prompt caching, common response draws with separate randomness streams, critic isolation, and event-level feasibility isolate comparisons. The Llama-3.3-70B experiments on this feeder distinguish three properties. First, forced search is the oracle in all five baseline seeds under the specified objective. Second, injected objective substitution preserves mode agreement at 1.0 while increasing cumulative voltage shortfall by 2.68x. Third, the 144-scenario, 576-episode factorial bank, using three repeated seeds, contains feasible oracles from predefined, sequential, and search. The prespecified stress-held-out ridge has mean regret 90.7 and no observed value over fixed sequential. A post-hoc constraint-aware analysis reduces regret to 29.0; a simple deadline rule attains 28.7, so this gain does not establish a learning advantage. An all-feasible ablation does not improve over fixed search. These are simulation-internal, descriptive comparisons. A five-model, 300-declaration extension tests interface behaviour, not cross-backbone physical rankings; shared-endpoint latency tails motivate probabilistic live feasibility.