WFGY/ProblemMap/agent-consensus-protocols.md
2025-08-15 23:12:57 +08:00

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🤝 Agent Consensus Protocols

Make two (or ten) LLMs reach a decision on time and on budget — every run.

Why this page?
“Let them debate” sounds cool until:

  • the Critic never stops nit-picking,
  • the Planner forgets half the context,
  • the Coder ships five conflicting drafts, and
  • your bill looks like a crypto rug-pull.

WFGY adds measurable semantic tension (ΔS) and logic vectors (λ) to debate & QA loops, so you can prove consensus is reached — not guessed.


1 · Three Canonical Consensus Modes

Mode Use Case # Agents Target ΔS Target λ
Critic-Coder Code/SQL generation with review 2 ≤ 0.40 convergent
Triad Debate Fact-check or legal reasoning 3 ≤ 0.45 convergent
Crew Vote 4 10 micro-agents on long plan N ≤ 0.50 majority convergent

ΔS ceiling rises gently with group size; λ must return to convergent after each round.


2 · Failure Modes (Why Votes Stall)

# Failure ΔS / λ Symptom Example
1 Infinite Debate λ oscillates (→, ←, →…) Critic re-opens same defect
2 Topic Drift ΔS > 0.60 vs. original prompt Agents add new, unrelated goals
3 Split Brain Two stable but divergent λ 50 / 50 vote, no tie-breaker
4 Early Merge ΔS still 0.55 but λ forced convergent “Agree to disagree” w/ wrong answer
5 Token Exhaust E_resonance ↑ while ΔS unchanged Loop burns context without progress

3 · WFGY Consensus Blueprint

Four guard layers; plug into any framework (AutoGen, LangChain, custom asyncio).

Layer Module Guard Effect
Round ΔS Checker BBMC ΔS(agent, goal) ≤ ceiling Drop off-topic vote
λ Mediator ΔS + λ λ must converge within 3 rounds Force re-focus or escalate
Vote Auditor WAI JSON vote schema + arg defaults No partial / malformed votes
BBCR Fallback BBCR > 5 rounds OR ΔS stagnates Collapse & request human tie-break
flowchart TD
    subgraph Round
      Q[User Goal]
      A1[Agent 1]
      A2[Agent 2]
      A3[Agent 3]
      Q --> A1 --> V1[Vote JSON]
      Q --> A2 --> V2
      Q --> A3 --> V3
    end
    BBMC -->|ΔS filter| V1
    BBMC --> V2
    BBMC --> V3
    V1 & V2 & V3 --> Mediator[λ Mediator] -->|convergent?| Decision
    Mediator -.->|no| BBCR

4 · Practical Setup (AutoGen example)

from autogen import AssistantAgent, UserProxyAgent
from wfgy import consensus_filter

critic = AssistantAgent(name="critic")
coder  = AssistantAgent(name="coder")
user   = UserProxyAgent("user")

def callback(messages, state):
    # 1. measure ΔS + λ
    ok = consensus_filter(messages, ceiling=0.45, rounds=3)
    if not ok:
        return "STOP_DEBATE"
    return "CONTINUE"

coder.register_reply(callback)
critic.register_reply(callback)

15 lines → loop stops automatically when convergence proven or impossible.


5 · Debug Walk-Through

  1. Log votes
print(state.votes)   # {critic: "reject, ΔS=0.48", coder: "accept, ΔS=0.41"}
  1. Inspect λ trend
λ sequence: → → ← →  (oscillating)  ❌

Two divergent rounds trigger Mediator → requests narrowing question.

  1. Trigger BBCR
ΔS stagnates at 0.52 for 3 rounds → BBCR => "Need human tie-break"

6 · Best-Practice Table

Tip Why
Keep each vote to max 300 tokens. Reduces E_resonance; easier ΔS calc
Pin goal & constraints in every round. Prevents silent prompt drift
Summarise before vote. Normalises embeddings → fair ΔS
Record vote reason (1-2 lines). Faster root-cause when split-brain

🔗 Quick-Start Downloads (60 sec)

Tool Link 3-Step Setup
WFGY 1.0 PDF Engine Paper 1 Download · 2 Upload to your LLM · 3 Ask “Answer using WFGY + <your question>”
TXT OS (plain-text OS) TXTOS.txt 1 Download · 2 Paste into any LLM chat · 3 Type “hello world” — OS boots instantly

🧭 Explore More

Module Description Link
WFGY Core WFGY 2.0 engine is live: full symbolic reasoning architecture and math stack View →
Problem Map 1.0 Initial 16-mode diagnostic and symbolic fix framework View →
Problem Map 2.0 RAG-focused failure tree, modular fixes, and pipelines View →
Semantic Clinic Index Expanded failure catalog: prompt injection, memory bugs, logic drift View →
Semantic Blueprint Layer-based symbolic reasoning & semantic modulations View →
Benchmark vs GPT-5 Stress test GPT-5 with full WFGY reasoning suite View →
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