mlsanityData to 14 Oct 2026

The Arbiter Agent: Continually Monitoring Multi-Agent Conversations to Detect Emergent Misalignment

arXiv 2606.10747 ↗First posted 9 Jun 2026cs.AIcited by 0

On the map: LLM agents and tool use → General LLM agent frameworks and benchmarks

Abstract (arXiv)

As AI systems built from multiple language-model agents become more common, they are increasingly used to make decisions together: discussing, negotiating, and acting on shared tasks. While individual agents may appear well-aligned when tested on their own, problems can arise from how they interact with one another. We introduce the Arbiter, an agent designed to monitor multi-agent conversations in real time and identify which participants may be behaving in misaligned ways. The Arbiter operates under a limited "inspection budget", meaning it must decide carefully how to use its resources. As it observes a conversation step by step, it can choose to wait, question a participant, examine internal information such as system prompts or reasoning traces, or log concerning behavior. At the end, it produces a report identifying the likely source of misalignment. We evaluate the Arbiter across five conversation conditions, ranging from risky financial advice model organisms to evaluation-aware and colluding agents, we test five tool configurations of increasing capability and two backbone models. We find that the Arbiter reliably detects misaligned agents well before the end of the conversation, with active inspection tools improving both detection accuracy and speed. Weight-induced misalignment proves hardest to detect, while instruction-induced misalignment is identified reliably even under passive observation. The logging tool exhibits a dual effect, improving recall at the cost of precision. These results suggest that continual, budget-aware monitoring can effectively catch misalignment, and that overseeing multi-agent systems may require treating the auditor as an active participant in the process. The code is available at https://github.com/aisilab/arbiter.

Key facts. No citing papers in the map yet (data through 14 Oct 2026). Placed in General LLM agent frameworks and benchmarks, one of 16 directions in LLM agents and tool use. Builds on 20 of its 61 references that are in the map. First posted on arXiv 9 Jun 2026.

Builds on

20 of 61 references.

References · 20

  1. 2022ReAct: Synergizing Reasoning and Acting in Language Modelscited by 8,695
  2. 2023Judging LLM-as-a-Judge with MT-Bench and Chatbot Arenacited by 8,626
  3. 2023Language Models Don't Always Say What They Think: Unfaithful Explanations in Chain-of-Thought Promptingcited by 1,122
  4. 2022Red Teaming Language Models with Language Modelscited by 1,026
  5. 2024Sleeper Agents: Training Deceptive LLMs that Persist Through Safety Trainingcited by 529
  6. 2023Weak-to-Strong Generalization: Eliciting Strong Capabilities With Weak Supervisioncited by 467
  7. 2018AI safety via debatecited by 400
  8. 2024Frontier Models are Capable of In-context Schemingcited by 287
  9. 2026Scaling Monosemanticity: Extracting Interpretable Features from Claude 3 Sonnetcited by 191
  10. 2023Large Language Models can Strategically Deceive their Users when Put Under Pressurecited by 146
  11. 2018Supervising strong learners by amplifying weak expertscited by 145
  12. 2024AI Sandbagging: Language Models can Strategically Underperform on Evaluationscited by 145
8 more
  1. 2025Subliminal Learning: Language models transmit behavioral traits via hidden signals in datacited by 95
  2. 2025Model Organisms for Emergent Misalignmentcited by 91
  3. 2023How to Catch an AI Liar: Lie Detection in Black-Box LLMs by Asking Unrelated Questionscited by 83
  4. 2025Activation Oracles: Training and Evaluating LLMs as General-Purpose Activation Explainerscited by 52
  5. 2026AuditBench: Evaluating Alignment Auditing Techniques on Models with Hidden Behaviorscited by 38
  6. 2025Narrow Finetuning Leaves Clearly Readable Traces in Activation Differencescited by 28
  7. 2025The Coming Crisis of Multi-Agent Misalignment: AI Alignment Must Be a Dynamic and Social Processcited by 21
  8. 2025LLMs Can Covertly Sandbag on Capability Evaluations Against Chain-of-Thought Monitoringcited by 18

Built on by

Not cited yet.

Similar, not cited

Close in wording, no citation either way

  1. 2025A Desideratum for Conversational Agents: Capabilities, Challenges, and Future Directions
  2. 2025Agent-Testing Agent: A Meta-Agent for Automated Testing and Evaluation of Conversational AI Agents
  3. 2026LCAM: A Framework for Diagnosing Interactional Alignment Failures in Con-versational AI
  4. 2025The Social Laboratory: A Psychometric Framework for Multi-Agent LLM Evaluation
  5. 2023Of Models and Tin Men: A Behavioural Economics Study of Principal-Agent Problems in AI Alignment using Large-Language Models
  6. 2025Agent-Environment Alignment via Automated Interface Generation
  7. 2025Talk Isn't Always Cheap: Understanding Failure Modes in Multi-Agent Debate
  8. 2026ConflictBench: Evaluating Human-AI Conflict via Interactive and Visually Grounded Environments