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[2602.18494] On the Dynamics of Observation and Semantics
Summary
The paper discusses the limitations of viewing semantics as static in visual intelligence, proposing a dynamic framework that integrates physical constraints into understanding intelligence and observation.
Why It Matters
This research challenges traditional views in AI by emphasizing the physical limitations of agents and the necessity of symbolic structures for effective information processing. It offers a new perspective on how intelligence interacts with complex environments, which is crucial for advancing AI systems.
Key Takeaways
- Intelligence is framed as an active interaction with the environment rather than a passive reflection.
- The concept of the Semantic Constant B highlights thermodynamic limits on information processing.
- Symbolic structures are essential for modeling complex worlds within physical constraints.
- Understanding is redefined as constructing a causal quotient for algorithmic compressibility.
- The paper proposes a new framework for integrating semantics into AI models.
Computer Science > Artificial Intelligence arXiv:2602.18494 (cs) [Submitted on 14 Feb 2026] Title:On the Dynamics of Observation and Semantics Authors:Xiu Li View a PDF of the paper titled On the Dynamics of Observation and Semantics, by Xiu Li View PDF HTML (experimental) 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 factorize...
