Context, Reasoning, and Hierarchy: A Cost-Performance Study of Compound LLM Agent Design in an Adversarial POMDP
The study investigates the design of compound LLM agents in adversarial environments, focusing on context, reasoning, and task decomposition. It evaluates various configurations to determine which design choices enhance performance without excessively increasing costs. Findings suggest prioritizing programmatic infrastructure and clean task decomposition over complex reasoning strategies.
- ▪The study examines compound LLM agent design in a cyber defense environment modeled as a Partially Observable Markov Decision Process.
- ▪Programmatic state abstraction significantly improves performance, yielding up to 76% better mean returns compared to raw observations.
- ▪Distributing deliberation tools across a hierarchy can degrade performance, leading to a phenomenon termed 'deliberation cascade'.
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Computer Science > Artificial Intelligence arXiv:2605.16205 (cs) [Submitted on 15 May 2026] Title:Context, Reasoning, and Hierarchy: A Cost-Performance Study of Compound LLM Agent Design in an Adversarial POMDP Authors:Igor Bogdanov, Chung-Horng Lung, Thomas Kunz, Jie Gao, Adrian Taylor, Marzia Zaman View a PDF of the paper titled Context, Reasoning, and Hierarchy: A Cost-Performance Study of Compound LLM Agent Design in an Adversarial POMDP, by Igor Bogdanov and 5 other authors View PDF HTML (experimental) Abstract:Deploying compound LLM agents in adversarial, partially observable sequential environments requires navigating several design dimensions: (1) what the agent sees, (2) how it reasons, and (3) how tasks are decomposed across components.
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