Active Inference as Context Acquisition for AI Agents

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[Submitted on 8 Jun 2026]

Title:Active Inference as Context Acquisition for AI Agents

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Abstract:Interactive AI agents must acquire the right context as efficiently as possible. When a user omits a constraint, preference, file, or task variable, an agent can proceed with a default assumption or spend tokens on a clarifying question, retrieval call, tool call, or prompt trial. We formulate this tradeoff as active inference for context acquisition. An inner inference step updates beliefs over a latent task state, and an outer decision selects the next context action, task action, or stop action to minimize expected free energy under cost. In deterministic settings, the epistemic term reduces to expected information gain, optionally normalized by token cost. We instantiate the framework in Optimal Question Asking (OQA), with exact posteriors and a dynamic programming oracle, and benchmark frontier language models on binary and multiway categorical tasks from 25 to 300 candidates. We also study clarification before generation and automated prompt optimization under token budgets. The formulation is model-agnostic and views active inference as a design principle for the context-acquisition layer of AI agents.

Subjects:

Artificial Intelligence (cs.AI); Computation and Language (cs.CL); Machine Learning (cs.LG)

Cite as: arXiv:2608.19202 [cs.AI]

(or arXiv:2608.19202v1 [cs.AI] for this version)

https://doi.org/10.48550/arXiv.2608.19202

arXiv-issued DOI via DataCite

Submission history

From: Sanchayan Dutta [view email] [v1] Mon, 8 Jun 2026 02:47:35 UTC (3,340 KB)

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