paperSeptember 2026Unreviewed

Beyond Training: A Feasibility Taxonomy for Inference-Time AI Governance

Samar Ansari

Abstract

Compute governance today is a governance of training: the thresholds, reporting requirements, and frontier-AI regimes now in force attach to training compute and treat the trained model as the regulatory unit. That picture is incomplete: capability increasingly migrates to the deployment stage through inference-time scaling, agentic scaffolding, and compression onto consumer hardware. This paper asks which mechanisms are available once the regulatory object shifts from the training run to the in

Cite

@misc{ansari2026beyond,
  title = {{Beyond Training: A Feasibility Taxonomy for Inference-Time AI Governance}},
  author = {Samar Ansari},
  year = {2026},
  month = sep,
  eprint = {2609.10105},
  archivePrefix = {arXiv},
  url = {https://arxiv.org/abs/2609.10105}
}