On the Dynamics of Observation and Semantics

TL;DR

This paper challenges the static view of semantics in visual intelligence, proposing a physically grounded model where semantics emerge from the interaction of bounded agents with their environment, leading to the necessity of symbolic structures.

Contribution

It introduces the Observation Semantics Fiber Bundle framework and proves that physical constraints enforce the emergence of symbolic, discrete, and compositional semantic structures.

Findings

Semantic manifold undergoes a phase transition to a discrete form.

Physical limits impose a bound on internal state complexity.

Language and logic are ontological necessities, not cultural artifacts.

Abstract

A dominant paradigm in visual intelligence treats semantics as a static property of latent representations, assuming that meaning can be discovered through geometric proximity in high dimensional embedding spaces. In this work, we argue that this view is physically incomplete. We propose that intelligence is not a passive mirror of reality but a property of a physically realizable agent, a system bounded by finite memory, finite compute, and finite energy interacting with a high entropy environment. We formalize this interaction through the kinematic structure of an Observation Semantics Fiber Bundle, where raw sensory observation data (the fiber) is projected onto a low entropy causal semantic manifold (the base). We prove that for any bounded agent, the thermodynamic cost of information processing (Landauer's Principle) imposes a strict limit on the complexity of internal state transitions. We term this limit the Semantic Constant B. From these physical constraints, we derive the necessity of symbolic structure. We show that to model a combinatorial world within the bound B, the semantic manifold must undergo a phase transition, it must crystallize into a discrete, compositional, and factorized form. Thus, language and logic are not cultural artifacts but ontological necessities the solid state of information required to prevent thermal collapse. We conclude that understanding is not the recovery of a hidden latent variable, but the construction of a causal quotient that renders the world algorithmically compressible and causally predictable.