Robust Calibrated Compute Control for Machine-Native Intelligence
C3R is an open-core control plane that decides which computation is worth performing next. It evaluates tools, retrieval, local and frontier models, verification, placement, and stopping as typed candidates under one conservative value-of-computation policy.
The v0.1 vertical slice now includes bounded hierarchical candidate compilation, a revision-verified Laya fast path with calibration-gated abstention, and a reproducible DecisionMix v1 schema preview. It is a research alpha: the controller is runnable and tested; fine-tuned weights and empirical production calibration remain release gates, not implied claims.
C3R's default deliberative language model is
deepseek-ai/DeepSeek-V4.1-Flash
(MIT). The hosted default uses deepseek/deepseek-v4.1-flash through OpenRouter; the
official DeepSeek API alias is deepseek-flash. Laya remains the separate System-One
decision fast path, and DeepSeek recommendations remain subject to the same verifier and
trusted commit boundary as every other candidate.
The 763B-parameter checkpoint is not assumed to fit a single GPU merely because only 8B/16B parameters are active per token. A GCP deployment must pass storage, aggregate accelerator memory, runtime-version, and smoke-test gates before C3R labels it self-hosted; otherwise the GPU service uses the hosted provider profile and stores no model weights.
Most agent stacks decide what to say. C3R decides what computation should happen next—and keeps that recommendation separate from authority to act.
flowchart LR
S[Versioned state] --> C[Hierarchical candidate compiler]
C --> L[Laya fast path]
C --> D[Deliberative envelope]
L --> V[Robust CVoC]
D --> V
V --> F[Verifier Firewall]
F --> G[Trusted Commit Gateway]
G --> O[Outcome + trace + cost twin]
Hard masks run before candidate expansion. Missing calibration or an uncertain typed prediction abstains. Learned components cannot grant permissions, select their authoritative verifier, turn failed verification into success, or directly commit an external effect.
| Surface | Included now |
|---|---|
| Candidate Compiler | family → subgroup → operation → arguments → placement → verifier; hard masks, budget pruning, caps, progressive widening |
| Laya fast path | exact upstream revision and license verification, typed probabilities, slice calibration, confidence/margin abstention |
| DecisionMix v1 | validated records, immutable deterministic splits, source/license provenance, SHA-256 manifest, 144-record synthetic preview |
| Authority boundary | action-bound verifier attestations, expiring single-use approvals, atomic nonce claims |
| Runtime control | conservative CVoC selection, deterministic STOP, cost twin, deliberative contracts, evidence-grade traces |
| Operational controls | fail-closed feature flags, tested frontier/open-weight HTTP contracts, Colibri shadow recommendations, canonical trace hash chain |
This compiler is the reviewed vertical slice, not the directive's full Candidate Compiler Definition of Done. Rich typed value constraints, per-argument provenance, dominated-branch pruning, and mandatory production placement policy remain explicit roadmap gates.
Core tests require only Python 3.11+:
python -m unittest discover -s tests -vInstall the optional pinned Laya integration:
pip install -e ".[laya]"Build the identical DecisionMix schema preview and verify its hashes:
python scripts/build_decisionmix_preview.pyMinimal conservative selection:
from c3r.cvoc import RobustCvocController
from c3r.state_schema import ActionCandidate, ActionFamily, RiskClass, ValueEstimate
candidate = ActionCandidate(
id="retrieve",
family=ActionFamily.RETRIEVAL,
risk_class=RiskClass.READ_ONLY,
optimistic_utility=0.7,
)
decision = RobustCvocController().select(
(candidate,),
{"retrieve": ValueEstimate(0.8, 0.2, 0.0, 0.1)},
)If the conservative lower bound is not positive, decision.selected is None and the fallback
is STOP.
- C3R v0.1 Hugging Face collection — the model preview, DecisionMix preview, and attributed upstream Laya checkpoint in one release collection.
- C3R DecisionMix v1 Preview
— published synthetic schema dataset with immutable splits and content hashes; its source is
mirrored in
data/decisionmix-v1-preview. - C3R Decision Laya 421M v0.1 — integration-preview model card, exact base revision, both research papers, and machine-readable calibration/training/evaluation gates; no derived weights are claimed.
docs/architecture.md— trust boundaries and component contracts.docs/roadmap.md— what remains before production qualification.docs/directive-compliance.md— evidence-backed audit against every Definition of Done item in Execution Directive v2.docs/empirical-release-plan.md— gated path from synthetic preview to trained, calibrated, independently reproducible release.docs/launch-announcement.md— canonical launch copy plus LinkedIn, X, and Hacker News variants with a publication checklist.docs/prior-art.md— explicit attribution links and the canonical novelty boundary required by the execution directive.
The adapter pins convaiinnovations/laya at
1c5edc17a7acd8701df6fc341c0d179f1c62c982. Before loading, the backend resolves the Hub
metadata, verifies that exact SHA and the Apache-2.0 license, downloads that revision, and passes
the local snapshot to laya==0.3.5.
The fast path answers fixed typed questions only. Every decision slice is keyed by question type, action family, option-count bucket, language, and consequence class. A missing temperature, insufficient top probability, or insufficient top-two margin returns an abstention signal that the host orchestration must route to its deliberative envelope.
The published preview is intentionally synthetic. It validates the entire publication contract without presenting generated fixtures as real training evidence. The empirical corpus will ship only when provenance, licensing, held-out integrity, and calibration support are independently auditable.
Required empirical metrics include accuracy, Brier score, ECE, maximum calibration error, NLL, selective risk versus coverage, abstention, escalation, p50/p95 latency, throughput, calls avoided, and cost per completed task.
Production hosts must preserve independent control of:
C3R_ENABLED
C3R_SYSTEM_ONE
C3R_DELIBERATIVE
C3R_ROUTING
C3R_SPECULATION
C3R_MOE_CONTROL
C3R_ONLINE_LEARNING=false
Disabling the learned fast path must leave the host's normal deterministic fallback operational.
FeatureFlags.from_mapping enforces those switches fail-closed and rejects autonomous online
learning. The reference provider and Colibri shadow contracts are documented in
docs/runtime-integrations.md.
Not yet claimed: trained C3R-Decision-Laya-421M-v0.1 weights, empirical DecisionMix training
data, live provider qualification, MC-1 product integration, a Colibri shadow deployment, or
measured production calibration/latency/cost results. Tested adapter and shadow-control contracts
are included, but they are not represented as production runs. These remain documented gates.
The upstream base is Laya by Convai Innovations,
licensed Apache-2.0. C3R is a broader runtime architecture, not a fork or rebranding of Laya.
See NOTICE for the attribution boundary.
Do not report vulnerabilities in a public issue; follow SECURITY.md. Production
effects must be completely mediated by an independently configured commit gateway.
Apache License 2.0. See LICENSE. If you use C3R, cite CITATION.cff.