Resolution Information: Limits of Ambiguity Resolution for Generative Communication

2026-05-12

AI Summary

This work addresses the fundamental limitation that semantic ambiguity in generative communication cannot be entirely eliminated. It introduces the notion of *resolution information* to quantify the minimal information update required for the receiver’s posterior belief to enter a low-ambiguity semantic region. By extending the geometric perspective of Shannon communication to generative communication for the first time—integrating tools from information geometry, variational inference, and large deviation theory—the study reveals that constraints imposed by generative models induce specific geometric structures on the posterior distribution, leading to an irreducible ambiguity floor absent in classical channel coding theory. The analysis demonstrates that while ambiguity can decay exponentially under ideal unconstrained conditions, practical constraints impose an insurmountable lower bound when semantic regions are polyhedral, thereby establishing resolution information as an operational measure of semantic disambiguation capability.

Abstract

In generative communication, the transmitter sends a compact generative description, such as model parameters or a latent representation, rather than raw data. The receiver uses this description to form a posterior belief over the underlying state and to resolve semantic ambiguity: which interpretation, decision, or action is supported by the received representation? Inspired by Shannon's geometric view of communication as uncertainty resolution, we introduce resolution information as the minimum information update, measured in nats, required to move the receiver's posterior belief into a low-ambiguity semantic region. Our work yields three main results. First, when the receiver can form any posterior belief, corresponding to the ideal unconstrained case, resolution information reduces to a binary divergence that depends only on each region's prior probability. In this case, the shape of the regions is irrelevant. Under repeated sampling, ambiguity decays exponentially with an exponent equal to the resolution information, giving it an operational meaning as an ambiguity exponent. Second, when the generative representation constrains the posterior family, as in practice, geometry becomes operational and can create irreducible ambiguity floors: half-spaces remain resolvable, whereas polytope-type regions can exhibit residual ambiguity that no amount of additional information can remove. These results reveal a fundamental departure from classical channel coding. In Shannon theory, codes can be designed so that decoding regions separate messages and error probability vanishes below capacity. In generative communication, the model itself induces a constrained posterior geometry that may prevent asymptotic ambiguity resolution. The resulting limit is not on rate, but on resolvability itself.

Problem

• generative communication
• semantic ambiguity
• resolution information
• posterior geometry
• ambiguity resolution

Innovation

• resolution information
• generative communication
• semantic ambiguity
• posterior geometry
• ambiguity exponent