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.
Adam Ableman Mazurk (Mon,) studied this question.
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