COE Consensus
/install coe-consensus
COE Consensus Skill
Cross-Model Consensus Engine
Algorithm implementation based on the COE (Cognition-Oriented Emergence) Protocol (Wang, 2026).
Core Problem
When multiple agents (humans, AI models, robots) observe the same physical space, how do they reach a verifiable consensus on "what the world is"?
Consensus Policies
| Policy | Use Case | Rule |
|---|---|---|
| Simple Majority | Small equal-trust groups | Confirmations exceed 50% of all verifications received |
| Weighted Trust | Heterogeneous agents with different reliability | Sum of (trust_weight * confidence) exceeds threshold |
| BFT | High-security with potential malicious agents | More than f+1 confirmations out of at least 2f+1 total verifications |
Shared World State (SWS)
Whenever consensus is reached, the engine produces an SWS record containing:
subject/predicate/value— the agreed-upon factconfidence— aggregated confidence scorebased_on— event IDs of the underlying J/V eventsconsensus_policy— policy used to reach agreementconfirmations— number of confirming verifications
Usage Example
Request
{
"session_id": "warehouse-001",
"target": "warehouse-zone-3",
"policy": "weighted_trust",
"events": [
{
"event_id": "evt-1",
"primitive": "J",
"issuer": "robot-A",
"timestamp": "2026-04-19T10:30:00Z",
"target": "warehouse-zone-3",
"assertion": {"subject": "door_01", "predicate": "status", "value": "open"},
"confidence": 0.95
},
{
"event_id": "evt-2",
"primitive": "V",
"issuer": "robot-B",
"timestamp": "2026-04-19T10:30:05Z",
"target": "warehouse-zone-3",
"verify_of": ["evt-1"],
"verification_result": "confirmed",
"confidence": 0.9
}
],
"trust_weights": {"robot-A": 0.9, "robot-B": 0.8},
"weighted_threshold": 1.5
}
Response
{
"session_id": "warehouse-001",
"resolved": true,
"policy": "weighted_trust",
"sws": {
"sws_id": "...",
"target": "warehouse-zone-3",
"timestamp": "2026-04-19T10:30:05Z",
"assertions": [
{
"subject": "door_01",
"predicate": "status",
"value": "open",
"confidence": 1.0,
"based_on": ["evt-1"],
"consensus_policy": "weighted_trust",
"confirmations": 1
}
]
},
"conflicts": [],
"message": "Consensus complete. 1 assertions resolved, 0 conflicts remain.",
"events_processed": 2,
"events_by_issuer": {"robot-A": 1, "robot-B": 1}
}
Relationship with JEP
- COE answers "what the world is" — cognitive consensus, ex-ante / in-situ collaboration.
- JEP answers "who is responsible" — accountability tracing, post-hoc audit.
- COE events may be referenced by JEP as evidence. Together they form a complete cognition-accountability dual-loop.
Cognitive Emergence Lab
[email protected]
- Make sure OpenClaw is installed (local or Docker)
- Run the install command in chat:
/install coe-consensus - After installation, invoke the skill by name or use
/coe-consensus - Provide required inputs per the skill's parameter spec and get structured output
What is COE Consensus?
COE Consensus Engine — Cross-Model Consensus Skill for Shared World State Formation. It is an AI Agent Skill for Claude Code / OpenClaw, with 52 downloads so far.
How do I install COE Consensus?
Run "/install coe-consensus" in the OpenClaw or Claude Code chat to install it in one step — no extra setup required.
Is COE Consensus free?
Yes, COE Consensus is completely free, licensed under MIT-0. You can download, install and use it at no cost.
Which platforms does COE Consensus support?
COE Consensus is cross-platform and runs anywhere OpenClaw / Claude Code is available (cross-platform).
Who created COE Consensus?
It is built and maintained by JEP (Judgment Event Protocol) (@schchit); the current version is v1.0.0.