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]
- 确保已安装 OpenClaw(本地或 Docker 部署)
- 在对话框中输入安装命令:
/install coe-consensus - 安装完成后,直接呼叫该 Skill 的名称或使用
/coe-consensus触发 - 根据 Skill 的参数说明提供必要输入,即可获得结构化输出
COE Consensus 是什么?
COE Consensus Engine — Cross-Model Consensus Skill for Shared World State Formation. 它是一个面向 Claude Code / OpenClaw 的 AI Agent Skill 插件,目前累计下载 52 次。
如何安装 COE Consensus?
在 OpenClaw 或 Claude Code 对话框中运行命令「/install coe-consensus」即可一键安装,无需额外配置。
COE Consensus 是免费的吗?
是的,COE Consensus 完全免费,采用 MIT-0 许可证,可自由下载、安装和使用。
COE Consensus 支持哪些平台?
COE Consensus 跨平台运行,可在任意部署了 OpenClaw / Claude Code 的环境中使用(cross-platform)。
谁开发了 COE Consensus?
由 JEP (Judgment Event Protocol)(@schchit)开发并维护,当前版本 v1.0.0。