Overview
Cheaper depth via reusable execution state
CLC provides a control-plane protocol that turns repeated prompt replay into stateful continuation. You keep inference in your own vLLM / sglang / HF stack. We provide policy enforcement and receipts.
The loop
Replace full prompt replay with a small delta against an authoritative spine reference.
Create a session and bootstrap a spine once (your stable context and schemas).
Each step sends only a leaf delta + spine_ref; no full prompt replays.
Inference runs in your infrastructure (vLLM / sglang / HF).
Receive receipts: lineage hashes, policy version, and avoided-recompute metrics.
Constraint profiles
One API, multiple hard policies. Start in EVAL. Promote to production when ready.
EVAL
Bounded depth and steps. Hash-only outputs by default. Deterministic receipts for benchmarking.
- —Max depth (e.g. 3)
- —Max steps (e.g. 25)
- —Short TTL + no cross-session persistence
LIMITED
Early production constraints with conservative invalidation and stronger safeguards.
- —Bounded persistence
- —Strict validation
- —Org-level quotas and audit trails
FULL
Uncapped workflows with enterprise governance and long-lived state (LK) under policy control.
- —Long-lived spines
- —Cross-session reuse
- —Custom governance + retention
Training gets cheaper too
The same spine/leaf structure can be applied to vertical training corpora to reduce tokens and variance.
Vertical Training Compiler
Compile vertical corpora into stable spines (schemas, policies, formats) and minimal leaf deltas. Output standard datasets and metrics without sending raw data to CLC Labs.
Security model
- —Customer-hosted inference (no GPU hosting)
- —Hash-only by default; no prompt logging
- —Policy-governed state retention and deletion