mlsanityData to 7 Oct 2026

Aetheria: A multimodal interpretable content safety framework based on multi-agent debate and collaboration

arXiv 2512.02530 ↗First posted 2 Dec 2025cs.AIcited by 2

On the map: LLM agents and tool use → Agent threats, guardrails and governance

Abstract (arXiv)

The exponential growth of digital content presents significant challenges for content safety. Current moderation systems, often based on single models or fixed pipelines, exhibit limitations in identifying implicit risks and providing interpretable judgment processes. To address these issues, we propose Aetheria, a multimodal interpretable content safety framework based on multi-agent debate and collaboration.Employing a collaborative architecture of five core agents, Aetheria conducts in-depth analysis and adjudication of multimodal content through a dynamic, mutually persuasive debate mechanism, which is grounded by RAG-based knowledge retrieval.Comprehensive experiments on our proposed benchmark (AIR-Bench) validate that Aetheria not only generates detailed and traceable audit reports but also demonstrates significant advantages over baselines in overall content safety accuracy, especially in the identification of implicit risks. This framework establishes a transparent and interpretable paradigm, significantly advancing the field of trustworthy AI content moderation.

Key facts. Cited by 2 papers in the map (data through 7 Oct 2026). Placed in Agent threats, guardrails and governance, one of 16 directions in LLM agents and tool use. Builds on 35 of its 43 references that are in the map. First posted on arXiv 2 Dec 2025.

Builds on

35 of 43 references.

Closest ancestors · 2

Same direction, newest first

  1. 2025Efficient LLM Safety Evaluation through Multi-Agent Debate9 Nov
  2. 2025RADAR: A Risk-Aware Dynamic Multi-Agent Framework for LLM Safety Evaluation via Role-Specialized Collaboration28 Sep

Other references · 33

  1. 2022Chain-of-Thought Prompting Elicits Reasoning in Large Language Modelscited by 12,302
  2. 2023Judging LLM-as-a-Judge with MT-Bench and Chatbot Arenacited by 8,393
  3. 2021On the Opportunities and Risks of Foundation Modelscited by 3,961
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  6. 2023Improving Factuality and Reasoning in Language Models through Multiagent Debatecited by 1,739
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  8. 2023Language Models Don't Always Say What They Think: Unfaithful Explanations in Chain-of-Thought Promptingcited by 1,081
  9. 2023Encouraging Divergent Thinking in Large Language Models through Multi-Agent Debatecited by 995
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21 more
  1. 2017Automated Hate Speech Detection and the Problem of Offensive Languagecited by 724
  2. 2023Bias and Fairness in Large Language Models: A Surveycited by 626
  3. 2020The Hateful Memes Challenge: Detecting Hate Speech in Multimodal Memescited by 485
  4. 2024WildGuard: Open One-Stop Moderation Tools for Safety Risks, Jailbreaks, and Refusals of LLMscited by 460
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Cited by year (2026 to 7 Oct)

Citing papers per year: 2026: 2.

2 papers.

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Close in wording, no citation either way

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  3. 2024AEGIS: Online Adaptive AI Content Safety Moderation with Ensemble of LLM Experts
  4. 2025MV-Debate: Multi-view Agent Debate with Dynamic Reflection Gating for Multimodal Harmful Content Detection in Social Media
  5. 2025HarmTransform: Transforming Explicit Harmful Queries into Stealthy via Multi-Agent Debate
  6. 2025MAEBE: Multi-Agent Emergent Behavior Framework
  7. 2026Relevance as a Vulnerability: How Web Retrieval Degrades Safety Alignment in LLM Agents
  8. 2025A Different Approach to AI Safety: Proceedings from the Columbia Convening on Openness in Artificial Intelligence and AI Safety