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Sprint projectJan 12, 2026Vancouver

Adversarial Dialectics: Mitigating AI Persuasion Risks through High-Fidelity Multi-Agent Debate

Dong Chen · Team Dong

Submitted to AI Manipulation Hackathon. Sprint projects are early-stage work by participants, not Apart Research publications.

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Report: Adversarial Dialectics: Mitigating AI Persuasion Risks through High-Fidelity Multi-Agent Debate

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This project builds an AI-driven debating platform to mitigate AI persuasion risks—especially epistemic weaponization, where a model manipulates beliefs by selectively presenting true facts and omitting context. The core idea is to replace one-way persuasion with a structured adversarial process: two capable debaters argue opposing stances under enforced cross-examination, while independent agents verify evidence and map the debate’s logical structure. Rather than treating “truth” as a single model output, the system treats it as a procedure that exposes where disagreements come from—empirical claims, causal assumptions, or underlying values.

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How much would this matter for AI safety if it worked? How innovative is it? For scores of 4-5: is this actually new to the field, or replicating recent work?

Scoring guide
  1. 1Negligible. No clear problem addressed, or no meaningful novelty.
  2. 2Limited. Addresses a real problem but with a generic or well-trodden approach. Incremental at best.
  3. 3Moderate. Clear problem with a reasonable approach; some novelty in framing or method beyond routine application of existing tools.
  4. 4Significant. Important problem with an original approach, or identifies a neglected problem area. A valuable contribution others could build on.
  5. 5Exceptional. Tackles a critical AI safety problem with a genuinely novel approach, or opens a new research direction. Clear theory of change. You'd be excited to share this with researchers in the area.

How sound are methodology, implementation, and findings?

Scoring guide
  1. 1Seriously flawed. Methodology broken, results uninterpretable, or implementation doesn't work.
  2. 2Weak. Approach has significant gaps: missing validation, flawed experimental design, or incomplete implementation.
  3. 3Competent. Technically solid given the short duration. Methodology makes sense, results are interpretable, limitations acknowledged, work builds toward clear conclusions.
  4. 4Strong. Thorough methodology with convincing validation. Results clearly support conclusions. Immediately useful for future work.
  5. 5Exceptional. Ambitious scope executed rigorously. Surprising findings, novel methods, or unusually robust validation.

How clearly are work, findings, and impact potential communicated?

Scoring guide
  1. 1Incomprehensible. Cannot determine what the project is actually claiming or doing.
  2. 2Hard to follow. Key information buried, missing, or diluted by excessive length. Significant effort to extract main points.
  3. 3Clear enough. Can understand the problem, approach, and results without undue effort. Core content clearly present: problem, method, findings, limitations.
  4. 4Well presented. Easy to follow, well-structured, appropriate level of detail. Target audience would get it quickly.
  5. 5Exceptionally clear. A pleasure to read. Complex ideas made accessible. Could serve as a model for how to present this type of work.

  1. This is a very thoughtful proposal that tackles 'epistemic weaponization' with nuance. The 'Bias Calibration' mechanism dynamically allocating context window budget to the minority viewpoint is a genuinely novel technical approach to breaking echo chambers. However, the reliance on Llama 3.1 to simulate the 'Voting Crowd' is a strong assumption; future work could prioritize validating these sentiment shifts against human baselines to prove the persuasion metrics are robust. I also felt that the latency analysis makes it hard to judge deployment viability given the complex four-agent loop. Overall, the 'Disagreement Frontier' mapping is a high-value output and the presentation was excellent.

  2. The framing in this project is really interesting. Manipulation via selective presentation is an underappreciated angle on AI manipulation, and the adversarial debate architecture is a really creative approach.

    I'd love to see this developed further with presentation of a few concrete examples and quantitative results. The write-up sadly doesn't really show the system in action or test whether adversarial debate actually reduces manipulation compared to single-agent interaction. A worked example walking through how the citation ledger and disagreement frontier evolve would make the contribution much more tangible.

Cite this project

@misc{chen2026adversarial,
  title = {{Adversarial Dialectics: Mitigating AI Persuasion Risks through High-Fidelity Multi-Agent Debate}},
  author = {Dong Chen},
  year = {2026},
  month = jan,
  note = {Submitted to AI Manipulation Hackathon, an Apart Research Sprint},
  howpublished = {\url{https://apartresearch.com/sprints/projects/adversarial-dialectics-mitigating-ai-persuasion-risks-through-highfidelity-multiagent-debate-c9vw}},
  url = {https://apartresearch.com/sprints/projects/adversarial-dialectics-mitigating-ai-persuasion-risks-through-highfidelity-multiagent-debate-c9vw}
}

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