Stop Recomputing for AI/LLMs: Proof-Carrying Skills for Compute-Saving Inference Reuse

Stop Recomputing for AI/LLMs introduces Proof-Carrying Skills (PCS), an implementation-ready framework for reducing repeated inference cost (latency, energy, and operating cost) by reusing verified skill executions instead of recomputing full inference each time. PCS is built for a no-meta trust boundary: skill providers are untrusted, while acceptance depends only on a small deterministic checker, locally supplied inputs, and explicit observable anchors. The framework is specified as a lightweight layered stack: PCS-Core (deterministic checker, OPVM predicates, VTR/Glue receipts, replay-resistant invocation binding), with optional PCS-Blob (content-addressed chunks and Merkle inclusion proofs for large artifacts) and PCS-Registry (TTL, deduplication, coverage challenges, recertification automation). Key technical elements include a gas-metered, bounded Observable Predicate VM (OPVM), QJCS quantized canonicalization for stable numeric hashing, and fully specified receipt formats that keep verification bounded, reproducible, and fail-closed under adversarial conditions (forgery, replay, receipt-bloat/DoS, floating-point nondeterminism, and registry pollution). Repository URL: https://github.com/kadubon/Proof-Carrying-Skills--PCS-Core-