A Communication-Theoretic Framework for LLM Agents: Cost-Aware Adaptive Reliability

• URL: http://arxiv.org/abs/2605.09121v1
• Date: Sat, 09 May 2026 19:14:33 GMT
• Title: A Communication-Theoretic Framework for LLM Agents: Cost-Aware Adaptive Reliability
• Abstract summary: We study reliability techniques for large language models built on large language models (LLMs)<n>We show that each of these reliability techniques is a special case of one of six classical reliability operators.<n>We introduce a cost-nearest-neighbor router whose single Lagrangian knob traverses the quality-cost frontier.
• Score: 0.20625936401496228
• License: http://creativecommons.org/licenses/by-nc-nd/4.0/
• Abstract: Agents built on large language models (LLMs) rely on a range of reliability techniques, including retry, majority voting, and self-consistency, that have been developed in parallel rather than within a common analytical framework. We observe that an LLM sampled at temperature $T$ is a discrete stochastic channel $p(y \mid x)$ in the sense of Shannon's coding theory, and use this identity as the entry point for such a framework grounded in communication theory. Each of these techniques is a special case of one of six classical reliability operators: diversity combining, hybrid retransmission, iterative generator-critic decoding, rateless sampling, structured redundant verification, and difficulty-adaptive routing. Within the framework we give two closed-form results: a noise-variance threshold above which uniform averaging beats quality-weighted averaging, and a contractivity criterion for generator-critic refinement, consistent with a contractive-to-divergent transition we observe between 3B- and 14B-parameter models. We further introduce a cost-aware semantic-nearest-neighbor router whose single Lagrangian knob traverses the quality-cost frontier without retraining. Across six channel configurations spanning local and cloud models on 69 hard tasks, no fixed model-technique-budget choice dominates, motivating per-task allocation. On a 300-item hard split of MMLU, GSM8K, and HumanEval, our router occupies the full empirical Pareto frontier: at matched quality, its normalized cost is ${\approx}56$\% lower than the strongest fixed technique; at matched normalized cost, it improves quality by ${\approx}7$\% ($26$\% over single-shot decoding). These results argue for consolidating these reliability techniques into a single tunable layer informed by channel coding.

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