What a fascinating paper on AI agents.

A lot of the issues we see with AI agents today revolve around wrong assumptions the LLMs make.

This leads to problems like hallucination, cost inefficiencies, unreliable tool calls and much more.

I think if we can solve this problem, even current LLMs would significantly improve in terms of performance and efficiency.

The problem is that context acquisition is treated as afterthought, but it shouldn't be that way.

Users tend to leave out constraints when prompting. So the agent agent needs to guess the default, or spend tokens on a clarifying question, a retrieval call, a tool call, or a prompt trial.

This new work gives this problem an objective function. Context acquisition becomes active inference over a latent task state. An inner step updates beliefs, and an outer step picks 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. That is directly implementable today as a scoring rule.

They coin it as Optimal Question Asking, with exact posteriors and a dynamic programming oracle, then benchmark frontier models on binary and multiway tasks from 25 to 300 candidates. So you can measure the gap between your agent and the true optimum.

Paper: https://t.co/XKMXNeXRJU

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