Token Parsimony

Token Parsimony: Toward Semantic Compression Substrates for Evidence-Bearing Artificial Intelligence introduces a formal architectural principle for reducing redundant token expenditure in AI systems. The paper argues that modern agentic architectures waste substantial computational resources repeatedly reacquiring already-known operational state through raw context ingestion. The work proposes Token Parsimony as a framework for preserving inferential continuity through compact semantic receipts, governed state transitions, replay-oriented operational artifacts, and bounded escalation policies. Rather than treating context windows as disposable bandwidth, the paper frames tokens as constrained thermodynamic resources within semantic systems. The paper develops a formal model for semantic receipts, inferential sufficiency, escalation governance, and replayable operational lineage. It explores implications for hyperscale AI infrastructure, constitutional computing, semantic operating systems, and continuity-centric agent architectures. Topics include: semantic receipts and transition vectors, inferential sufficiency, escalation hierarchies, governance-complete semantic systems, replay integrity, substrate liveness, semantic thermodynamics, and receipt-centric operating environments. The work argues that future scalable AI systems will increasingly reason through governed semantic continuity rather than repeated raw substrate observation.