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Preprint . 2026
License: CC BY
Data sources: ZENODO
https://doi.org/10.2139/ssrn.6...
Article . 2026 . Peer-reviewed
Data sources: Crossref
ZENODO
Preprint . 2026
License: CC BY
Data sources: Datacite
ZENODO
Preprint . 2026
License: CC BY
Data sources: Datacite
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Semantic Orbital Mechanics: Measuring and Guiding AI Conversation Dynamics

Authors: Jimenez Sanchez, Juan Jacobo;

Semantic Orbital Mechanics: Measuring and Guiding AI Conversation Dynamics

Abstract

Current alignment techniques focus largely on response optimization-ensuring individual model outputs match human preferences. However, alignment is fundamentally a dynamical process: meaning emerges not from isolated tokens but from the trajectory of interaction over time. In this paper, we introduce a physics-inspired framework for measuring and guiding these dynamics, treating conversation as an orbital system in high-dimensional semantic space. Building on the geometric verification of the S64 symbolic framework (Paper 02), we adopt the Semantic Grounding Index (SGI) from Marín's geometric hallucination detection work and reinterpret it as an orbital radius that measures the tension between local responsiveness (query gravity) and global context (history gravity). We define the Conversational Coherence Region as a stable orbit where exploration and grounding are balanced. We then introduce the Semantic Transducer-a telemetry system that decomposes embedding trajectories into actionable S64 signals: symbols, paths, and transformation phases. To validate this framework, we conduct a controlled steering experiment. By injecting fake telemetry metrics into an AI's system prompt, we test whether conversational orbits can be predictably altered. The results are surprising: conversations maintain orbital stability despite one participant's distorted perception. The orbit is robust; steering affects what is discussed but not how meaning moves. This finding reveals both the power and limits of orbital dynamics. The transducer provides reliable telemetry-trajectory metrics are invariant across 10 embedding backends. But orbital mechanics describes only the horizontal plane of conversation: position and velocity. Detecting semantic manipulation requires the vertical dimension-symbolic depth, transformation richness, contribution asymmetry-that reveals not just where meaning is but how deep it goes. This paper reframes the 3-Body Problem of human-AI interaction as a measurable dynamical system, while acknowledging that complete navigation requires the vertical instruments of Paper 04.

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selected citations
These citations are derived from selected sources.
This is an alternative to the "Influence" indicator, which also reflects the overall/total impact of an article in the research community at large, based on the underlying citation network (diachronically).
BIP!Citations provided by BIP!
popularity
This indicator reflects the "current" impact/attention (the "hype") of an article in the research community at large, based on the underlying citation network.
BIP!Popularity provided by BIP!
influence
This indicator reflects the overall/total impact of an article in the research community at large, based on the underlying citation network (diachronically).
BIP!Influence provided by BIP!
impulse
This indicator reflects the initial momentum of an article directly after its publication, based on the underlying citation network.
BIP!Impulse provided by BIP!
0
Average
Average
Average