AIKernel Phase-1 Paper 04: Trajectory Governance Model

This technical note is Part II-4 of the AIKernel / AIOS Phase-1 specification series. It defines the Trajectory Governance Model as a deterministic governance layer for evaluating stochastic AI agent behavior after inference has begun. The paper formalizes LLM and SLM outputs as proposed semantic state transitions. It evaluates candidate actions, semantic drift, goal alignment, risk, uncertainty, repetition, convergence, and anomaly signals through a deterministic Policy Decision Point. The model maps stochastic proposals into controlled governance decisions such as Permit, Deny, AskConfirmation, Clarify, or Abort. This v0.2.0 edition is adapted from the prior standalone Trajectory Governance Model archived at DOI 10.5281/zenodo.20223205 and reorganized as Paper 04 of the AIKernel / AIOS Phase-1 paper series. It follows Paper 03, Pre-Inference Admissibility Governance, by governing runtime semantic trajectories and candidate actions after an inference transaction has been admitted. The English manuscript is the canonical version. The Japanese manuscript is included as a companion translation.