六大多Agent推理验证引擎实测拆解：从辩论到聚合的推理质量跃迁

class MultiAgentDebate:
    def __init__(self, agents: list[Agent], judge: Agent, rounds: int = 3):
        self.agents = agents
        self.judge = judge
        self.rounds = rounds

    async def debate(self, question: str) -> DebateResult:
        # Phase 1: Independent generation
        positions = {}
        for agent in self.agents:
            positions[agent.id] = await agent.generate(question)

        # Phase 2-3: Iterative debate rounds
        for round_num in range(self.rounds):
            critiques = {}
            for i, agent_a in enumerate(self.agents):
                # Each agent critiques others' positions
                others = {k: v for k, v in positions.items() if k != agent_a.id}
                critique = await agent_a.critique(question, others)
                critiques[agent_a.id] = critique

            # Agents revise based on critiques received
            for agent in self.agents:
                received_critiques = {k: v for k, v in critiques.items() if k != agent.id}
                positions[agent.id] = await agent.revise(
                    question, positions[agent.id], received_critiques
                )

        # Phase 4: Judge arbitration
        final_verdict = await self.judge.arbitrate(question, positions)
        return DebateResult(
            verdict=final_verdict,
            debate_log=critiques,
            confidence=self.judge.confidence_score
        )

class MixtureOfAgents:
    def __init__(self, proposers: list[LLM], aggregator: LLM, layers: int = 2):
        self.proposers = proposers
        self.aggregator = aggregator
        self.layers = layers

    async def generate(self, prompt: str) -> str:
        current_inputs = [prompt]

        for layer in range(self.layers):
            # Each proposer generates in parallel
            proposer_outputs = await asyncio.gather(*[
                p.generate(
                    f"{current_inputs[0]}\n\n"
                    f"Reference answers from other models:\n"
                    + "\n---\n".join(current_inputs[1:])
                )
                for p in self.proposers
            ])
            # Aggregator synthesizes all proposals
            aggregated = await self.aggregator.aggregate(
                prompt=prompt,
                proposals=proposer_outputs
            )
            current_inputs = [prompt] + proposer_outputs + [aggregated]

        return current_inputs[-1]

class PROClaimFramework:
    def __init__(self, config: ProclaimConfig):
        self.prosecutor = RoleAgent(role="prosecutor", llm=config.llm)
        self.defense = RoleAgent(role="defense_attorney", llm=config.llm)
        self.witness = RoleAgent(role="expert_witness", llm=config.llm)
        self.judge = JudgeAgent(llm=config.llm)
        self.rag = ProgressiveRAG(retriever=config.retriever)

    async def verify_claim(self, claim: str) -> Verdict:
        # Round 1: Opening arguments with initial evidence
        prosecution_arg = await self.prosecutor.opening(
            claim, evidence=self.rag.retrieve(claim, round=1)
        )
        defense_arg = await self.defense.opening(
            claim, evidence=self.rag.retrieve(
                claim, context=prosecution_arg, round=1
            )
        )

        # Rounds 2-N: Progressive debate with targeted retrieval
        debate_history = [prosecution_arg, defense_arg]
        for round_num in range(2, self.max_rounds + 1):
            # Witness summoned based on debate gaps
            witness_testimony = await self.witness.testify(
                claim, debate_history, 
                expertise=self.rag.retrieve(
                    claim, context=debate_history, round=round_num,
                    strategy="gap_filling"  # Target missing evidence
                )
            )
            # Prosecution cross-examination
            prosecution_rebuttal = await self.prosecutor.rebut(
                defense_arg, witness_testimony
            )
            defense_rebuttal = await self.defense.rebut(
                prosecution_rebuttal, witness_testimony
            )
            debate_history.extend([witness_testimony, prosecution_rebuttal, defense_rebuttal])

        # Final verdict with structured ruling
        return await self.judge.deliver_verdict(
            claim, debate_history, format="structured_ruling"
        )

class TRUSTAgents:
    def __init__(self):
        self.decomposer = ClaimDecomposerAgent()
        self.evidence_gatherer = EvidenceGathererAgent(
            sources=["scholar", "clinical_trials", "who", "pubmed"]
        )
        self.verifier = ReasoningVerifierAgent()
        self.explainer = ExplanationGeneratorAgent()

    async def verify(self, claim: str) -> VerificationReport:
        # Stage 1: Decompose
        sub_claims = await self.decomposer.decompose(claim)

        # Stage 2: Gather evidence (parallel per sub-claim)
        evidence_map = {}
        for sc in sub_claims:
            evidence_map[sc.id] = await self.evidence_gatherer.gather(
                sc.text, min_sources=3, cross_validate=True
            )

        # Stage 3: Verify reasoning chains
        verification_results = {}
        for sc in sub_claims:
            verification_results[sc.id] = await self.verifier.verify(
                sub_claim=sc,
                evidence=evidence_map[sc.id],
                check_logical_consistency=True,
                detect_cherry_picking=True
            )

        # Stage 4: Generate explainable report
        report = await self.explainer.generate(
            original_claim=claim,
            sub_claims=sub_claims,
            evidence=evidence_map,
            verifications=verification_results
        )
        return report  # Structured: claim, evidence, reasoning, verdict, confidence

graph TB
    subgraph "输入层"
        Q[用户查询/待验证主张]
    end

    subgraph "路由层"
        R{推理验证路由器}
        R -->|通用增强| MOA_PATH[MoA路径]
        R -->|事实核查| FACT_PATH[法庭辩论路径]
        R -->|决策分析| DEBATE_PATH[对抗辩论路径]
        R -->|多媒体验证| ARENA_PATH[竞技场路径]
    end

    subgraph "MoA并行路径"
        MOA_PATH --> P1[Proposer 1<br/>Qwen2.5-72B]
        MOA_PATH --> P2[Proposer 2<br/>Llama-3.1-70B]
        MOA_PATH --> P3[Proposer 3<br/>Mixtral-8x22B]
        MOA_PATH --> P4[Proposer 4<br/>DeepSeek-V2]
        P1 & P2 & P3 & P4 --> ATT[Attention聚合器<br/>语义注意力加权]
    end

    subgraph "法庭辩论路径"
        FACT_PATH --> PROC[检察官Agent]
        FACT_PATH --> DEF[辩护律师Agent]
        PROC & DEF --> WIT[专家证人Agent<br/>+ 渐进RAG]
        WIT --> JDG[法官Agent]
    end

    subgraph "对抗辩论路径"
        DEBATE_PATH --> D1[保守型Agent]
        DEBATE_PATH --> D2[激进型Agent]
        DEBATE_PATH --> D3[质疑型Agent]
        D1 & D2 & D3 --> |3轮辩论| JUDGE[仲裁Agent]
    end

    subgraph "竞技场路径"
        ARENA_PATH --> A1[论证Agent α]
        ARENA_PATH --> A2[论证Agent β]
        ARENA_PATH --> A3[论证Agent γ]
        A1 & A2 & A3 --> AQBAF[A-QBAF论证图<br/>量化攻击/防御]
    end

    ATT --> CONF[置信度评估]
    JDG --> CONF
    JUDGE --> CONF
    AQBAF --> CONF

    CONF --> OUT{置信度≥阈值?}
    OUT -->|是| FINAL[输出最终结论<br/>+ 推理链/判决书]
    OUT -->|否| HUMAN[人工审核介入<br/>调整论证权重]
    HUMAN --> FINAL