mlsanityData to 7 Oct 2026

Delayed Verification Destabilizes Multi-Agent LLM Belief: Instability Thresholds and Optimal Corrector Placement

arXiv 2606.27409 ↗First posted 25 Jun 2026cs.MA cs.CL cs.LGcited by 4

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

Abstract (arXiv)

Multi-agent large language model (LLM) systems often rely on verifier and critic agents to suppress hallucinations, but verification is delayed. During this delay, false claims can propagate through the agent network. We model this process as delayed consensus on a graph with grounded corrector nodes. Spectral decomposition by the grounded Laplacian yields a closed-form stability threshold for the verification dose: correction that is too strong or too delayed can turn consensus into oscillation. The most unstable regime occurs when the communication and verification delays coincide; for delay two, the threshold is the inverse golden ratio. The same framework gives a supermodular placement objective and a greedy (1-1/e)-approximation rule for assigning a limited corrector budget to influential nodes. Experiments across five open models confirm the predicted dose-delay oscillations. By contrast, grounded factual answering makes truth an absorbing boundary and eliminates the effect, suggesting that the instability is specific to signed-belief tasks while grounded verification remains stabilizing

Key facts. Cited by 4 papers in the map (data through 7 Oct 2026). Placed in General LLM agent frameworks and benchmarks, one of 16 directions in LLM agents and tool use. Builds on 35 of its 56 references that are in the map. First posted on arXiv 25 Jun 2026.

Builds on

35 of 56 references.

Closest ancestors · 4

Same direction, newest first

  1. 2026Contagion Networks: Evaluator Preference Propagation in Multi-Agent LLM Systems18 Jun
  2. 2026Self-Correction as Feedback Control: Error Dynamics, Stability Thresholds, and Prompt Interventions in LLMs24 Apr
  3. 2024Improving Multi-Agent Debate with Sparse Communication Topology17 Jun
  4. 2023Multi-Agent Consensus Seeking via Large Language Models31 Oct

Other references · 31

  1. 2020Retrieval-Augmented Generation for Knowledge-Intensive NLP Taskscited by 9,160
  2. 2023Self-Refine: Iterative Refinement with Self-Feedbackcited by 3,317
  3. 2021TruthfulQA: Measuring How Models Mimic Human Falsehoodscited by 2,919
  4. 2023Improving Factuality and Reasoning in Language Models through Multiagent Debatecited by 1,739
  5. 2018FEVER: a large-scale dataset for Fact Extraction and VERificationcited by 1,362
  6. 2023How Language Model Hallucinations Can Snowballcited by 310
  7. 2023ReConcile: Round-Table Conference Improves Reasoning via Consensus among Diverse LLMscited by 297
  8. 2025Which Agent Causes Task Failures and When? On Automated Failure Attribution of LLM Multi-Agent Systemscited by 170
  9. 2025TRAIL: Trace Reasoning and Agentic Issue Localizationcited by 98
  10. 2025Talk Isn't Always Cheap: Understanding Failure Modes in Multi-Agent Debatecited by 63
  11. 2025GUARDIAN: Safeguarding LLM Multi-Agent Collaborations with Temporal Graph Modelingcited by 35
  12. 2026AgentHallu: Benchmarking Automated Hallucination Attribution of LLM-based Agentscited by 15
19 more
  1. 2025When Models Lie, We Learn: Multilingual Span-Level Hallucination Detection with PsiloQAcited by 13
  2. 2026From Spark to Fire: Modeling and Mitigating Error Cascades in LLM-Based Multi-Agent Collaborationcited by 13
  3. 2026Hallucination Cascade: Analyzing Error Propagation in Multi-Agent LLM Systemscited by 12
  4. 2025Sherlock: Reliable and Efficient Agentic Workflow Executioncited by 10
  5. 2026AgentForesight: Online Auditing for Early Failure Prediction in Multi-Agent Systemscited by 8
  6. 2026Opinion Consensus Formation Among Networked Large Language Modelscited by 5
  7. 2026MARCH: Multi-Agent Reinforced Self-Check for LLM Hallucinationcited by 5
  8. 2026Collective Hallucination in Multi-Agent LLMs:Modeling and Defensecited by 5
  9. 2026Unveiling the Entropy Dynamics of Chain-of-Thought Reasoningcited by 4
  10. 2026PropGuard: Safeguarding LLM-MAS via Propagation-Aware Exploration and Remediationcited by 3
  11. 2026CASPIAN: Online Detection and Attribution of Cascade Attacks in LLM Multi-Agent Systems via Cross-Channel Causal Monitoringcited by 3
  12. 2017Submodular Optimization for Consensus Networks with Noise-Corrupted Leaderscited by 2
  13. 2024Interacting Large Language Model Agents. Interpretable Models and Social Learningcited by 2
  14. 2026Hidden Anchors in Multi-Agent LLM Deliberationcited by 2
  15. 2013Minimizing Convergence Error in Multi-Agent Systems via Leader Selection: A Supermodular Optimization Approachcited by 1
  16. 2016Robustness of Leader-Follower Networked Dynamical Systemscited by 1
  17. 2025Convergence of time-delayed opinion dynamics with complex interaction typescited by 1
  18. 2026Delayed Repression and Emergent Instability in Adaptive Multi-Agent Systemscited by 1
  19. 2026Quickest Detection of Hallucination Onset: Delay Bounds and Learned CUSUM Statisticscited by 1

Built on by

Cited by year (2026 to 7 Oct)

Citing papers per year: 2026: 4.

4 papers.

  1. 2026Onnes: A Physics-Grounded Multi-Agent LLM Simulator for Cryogenic Fault Diagnosis in Quantum Computing Infrastructurecited by 0
  2. 2026How Much Does Correctness Cost? Budgeted Placement of Strong Correctors in a Weak Multi-Agent Swarmcited by 0
  3. 2026Uncertainty Quantification for LLM Agents: A Taxonomy, an Evaluation Protocol, and an Empirical Studycited by 0
  4. 2026Maat: Independent Deterministic Contract-Based Governance for Multi-Agent LLM Workflowscited by 0

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  4. 2026Verify Before You Commit: Towards Faithful Reasoning in LLM Agents via Self-Auditing
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  6. 2026Conformity Dynamics in LLM Multi-Agent Systems: The Roles of Topology and Self-Social Weighting
  7. 2026Stable Reasoning, Unstable Responses: Mitigating LLM Deception via Stability Asymmetry
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