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yogsoth-ai/de-anthropocentric-research-engine/multiagent-debate

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PublishedAugust 9, 2026 at 02:21 PM

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version: "1.0.1" name: multiagent-debate description: 'Campaign: Multi-agent structured debate for adversarial validation. Core question: Can this artifact survive structured adversarial debate? Methods: Irving AI Safety via Debate, Du Society of Mind, Liang MAD, Toulmin Argumentation, D3 framework.' type: campaign produces: DebateVerdict artifact-types:

gap
hypothesis
research-question
idea
approach
experiment-design
claim

dependencies: strategies:

adversarial-escalation
courtroom-structured
critic-defender-judge
multi-perspective-panel
society-of-mind

tactics:

evidence-tournament
stress-test-dialectical-escalation
stress-test-perspective-rotation

sops:

context-checkpoint
context-init
debate-transcript-analysis
stress-test-saturation-detection
verdict-synthesis
weakness-classification

Multi-Agent Debate Campaign

Core question: Can this artifact survive structured adversarial debate?

Methodology Sources

Irving et al. (2018) — AI Safety via Debate
Du et al. (2023) — Society of Mind multi-agent sharing
Liang et al. (2023) — MAD (Multi-Agent Debate)
Toulmin (1958) — Argumentation model (claim, ground, warrant, backing, qualifier, rebuttal)
D3 framework — Deliberate, Debate, Decide

Strategy Routing

Artifact Type	Primary Strategy	Fallback Strategy
hypothesis, claim	critic-defender-judge	adversarial-escalation
research-question	multi-perspective-panel	society-of-mind
idea, approach	society-of-mind	courtroom-structured
experiment-design	courtroom-structured	critic-defender-judge
gap	multi-perspective-panel	adversarial-escalation

Budget Table

Parameter	S (Quick)	M (Standard)	L (Deep)
Debate rounds	4	8	12
Participating agents	3	5	8
Coverage dimensions	3	5	7
External evidence searches	2	5	10

Tactics

dialectical-escalation — Progressive pressure escalation based on confidence thresholds
perspective-rotation — Sequential perspective evaluation with divergence aggregation
evidence-tournament — Evidence gathering, cross-examination, and quality judgment

Context Management

Each subagent operates in isolated context. The debate-architect designs structure before execution. Transcripts are passed between rounds via structured markdown. Saturation detection terminates when novelty drops below threshold.

Output

Produces DebateVerdict containing: survival assessment, key vulnerabilities, confidence score, debate transcript summary, and recommended mitigations.

Available Strategies

Optional, no fixed order; the final leaf is always a sop.

Strategy	When to use
adversarial-escalation	Strategy: Progressive pressure escalation — starts with surface-level challenges and escalates to fundamental assumption attacks based on defender confidence decay.
courtroom-structured	Strategy: Legal adversarial structure — prosecution presents case, defense responds, evidence is cross-examined, judge delivers verdict. Emphasizes evidence quality and procedural rigor.
critic-defender-judge	Strategy: Classic triangular debate — Critic attacks, Defender responds, Judge adjudicates. Based on Irving AI Safety via Debate with Toulmin argumentation structure.
multi-perspective-panel	Strategy: Multi-stakeholder review panel — diverse expert perspectives evaluate artifact simultaneously, then synthesize through structured deliberation.
society-of-mind	Strategy: Multi-agent collaborative debate based on Du et al. Society of Mind. Agents share perspectives iteratively until convergence or divergence is detected.

Available Tactics

Optional, no fixed order; the final leaf is always a sop.

Tactic	When to use
evidence-tournament	Tactic: Evidence gathering, cross-examination, and quality judgment. External evidence is collected, presented, challenged, and scored for relevance and reliability.
stress-test-dialectical-escalation	Tactic: Progressive debate escalation based on confidence thresholds. Each round increases attack sophistication until defender collapses or proves resilient.
stress-test-perspective-rotation	Tactic: Sequential perspective evaluation with divergence aggregation. Each agent evaluates from a distinct viewpoint, then disagreements are surfaced and resolved.

Available SOPs

Optional, no fixed order; the final leaf is always a sop.

SOP	When to use
context-checkpoint	Append research process and results to the current Phase's context file. Covers both process and results with genuine substance. Use this skill at plan-designated checkpoint points — typically after each strategy completes or at key decision nodes within a research Phase.
context-init	Create a new context file for a research Phase. Called once at Phase start to initialize the file that subsequent context-checkpoint calls will append to. Use this skill whenever a new research Phase begins and a fresh context file is needed.
debate-transcript-analysis	Extracts key turning points, patterns, and insights from completed debate transcripts. Produces structured summary for verdict synthesis.
stress-test-saturation-detection	Determines whether validation has reached saturation — no new weaknesses or failure modes being discovered. Used by all 5 campaigns as termination signal.
verdict-synthesis	Synthesizes findings from a completed campaign into typed verdict reports. Produces DebateVerdict, RedTeamReport, FailureAnticipationReport, CounterfactualMap, or AdversarialStressReport depending on campaign. Also supports cross-campaign StressTestSummary.
weakness-classification	Classifies discovered weaknesses into severity tiers (fatal/major/minor/cosmetic) with structured justification and exploitability assessment.

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