mlsanityData to 7 Oct 2026

Strategic Evaluation of Planning Strategies for LLM Agents in Cyber-Physical Systems

arXiv 2608.04265 ↗First posted 4 Aug 2026cs.MA cs.AI cs.SYcited by 1

On the map: LLM agents and tool use → Agent threats, guardrails and governance

Abstract (arXiv)

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.

Key facts. Cited by 1 paper in the map (data through 7 Oct 2026). Placed in Agent threats, guardrails and governance, one of 16 directions in LLM agents and tool use. Builds on 20 of its 33 references that are in the map. First posted on arXiv 4 Aug 2026.

Builds on

20 of 33 references.

References · 20

  1. 2024The Llama 3 Herd of Modelscited by 14,192
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8 more
  1. 2026DeepSeek-V4: Towards Highly Efficient Million-Token Context Intelligencecited by 877
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  8. 2026LLM-Mediated Demand Response Coordination in Smart Microgridscited by 1

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Citing papers per year: 2026: 1.

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  1. 2026A-CPES: A Reference Framework for Agentic AI in Cyber-Physical Energy Systems
  2. 2026Architectural Design, Not Only Model Intelligence, Governs Multi-Agent LLM Performance
  3. 2026Stable Agentic Control: Tool-Mediated LLM Architecture for Autonomous Cyber Defense
  4. 2026Where Cyber Agents Struggle: Bottleneck Analysis of Multi-Stage LLM Agents
  5. 2025Architecting Resilient LLM Agents: A Guide to Secure Plan-then-Execute Implementations
  6. 2025Self-Resource Allocation in Multi-Agent LLM Systems
  7. 2026From Digital to Physical: Digital Agents as Autonomous Coaches for Physical Intelligence
  8. 2026Agentic LLM Planning via Step-Wise PDDL Simulation: An Empirical Characterisation