mlsanityData to 14 Oct 2026

The Illusion of Independent Quorums: Epistemic Fault Domains and Correlated Cognitive Failures in Agentic Quorums

arXiv 2609.02925 ↗First posted 24 Aug 2026cs.DC cs.MA cs.SEcited by 2

On the map: LLM agents and tool use → Agent memory systems

Abstract (arXiv)

Multi-agent quorums are widely used to authorize high-stakes infrastructure and policy mutations, yet distinct reviewers often share upstream telemetry, documents, or tool backends. When upstream inputs fail, multiple votes collapse onto a single corrupted cause: replication does not imply epistemic redundancy. We introduce Epistemic Fault Domains (EFDs) and the Structural Epistemic Cut kappaE, which quantifies the minimum number of modeled root faults whose exposure covers an authorizing coalition relative to an explicit Epistemic Fault Basis. Under closed causal accounting, conservative exposure, and authorization alignment, kappaE lower-bounds the number of roots required for semantic compromise (kappaS). We prove that arbitrarily large quorums can retain kappaE=1, that recognizing shared ancestry never increases credited resilience, and that adding voters at a fixed threshold cannot increase the cut under compatible exposure extensions. Finally, we design the Dependency-Aware Quorum Controller (DAQC) to enforce structural cuts at runtime admission, evaluate its mechanics via analytical derivations and simulations, and provide a frozen 120-task external benchmark suite.

Key facts. Cited by 2 papers in the map (data through 14 Oct 2026). Placed in Agent memory systems, one of 16 directions in LLM agents and tool use. Builds on 14 of its 30 references that are in the map. First posted on arXiv 24 Aug 2026.

Builds on

14 of 30 references.

Closest ancestors · 1

Same direction, newest first

  1. 2026The Honest Quorum Problem: Epistemic Byzantine Fault Tolerance for Agentic Infrastructure17 Jul

Other references · 13

  1. 2020Retrieval-Augmented Generation for Knowledge-Intensive NLP Taskscited by 9,297
  2. 2022Self-Consistency Improves Chain of Thought Reasoning in Language Modelscited by 5,708
  3. 2023Toolformer: Language Models Can Teach Themselves to Use Toolscited by 4,182
  4. 2023AutoGen: Enabling Next-Gen LLM Applications via Multi-Agent Conversationcited by 1,988
  5. 2023MetaGPT: Meta Programming for A Multi-Agent Collaborative Frameworkcited by 1,808
  6. 2023Improving Factuality and Reasoning in Language Models through Multiagent Debatecited by 1,784
  7. 2023Gorilla: Large Language Model Connected with Massive APIscited by 1,332
  8. 2023Encouraging Divergent Thinking in Large Language Models through Multi-Agent Debatecited by 1,011
  9. 2024Mixture-of-Agents Enhances Large Language Model Capabilitiescited by 438
  10. 2024Seven Failure Points When Engineering a Retrieval Augmented Generation Systemcited by 139
  11. 2026Temporary Authority, Permanent Effects: Commit-Time Authorization for LLM Agentscited by 18
  12. 2026Mnemosyne: Agentic Transaction Processing for Validating and Repairing AI-generated Workflowscited by 8
1 more
  1. 2026When AI Agents Commit: Cognitive Serializability Across Data, Evidence, Policy, and Authoritycited by 2

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Cited by year (2026 to 14 Oct)

Citing papers per year: 2026: 2.

2 papers.

Similar, not cited

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  1. 2026Replicating Belief, Not Bits: Epistemic State Replication for Agentic Systems
  2. 2026Semantic Quorum Assurance: Collective Certification for Non-Deterministic AI Infrastructure
  3. 2026Emergent Collaborative Deliberation in Multi-Model AI Systems: A BFT-Derived Protocol for Epistemic Synthesis
  4. 2026The Cost of Consensus: Isolated Self-Correction Prevails Over Unguided Homogeneous Multi-Agent Debate
  5. 2026Epistemic Sybil Resistance: Multiplying AI Agents Without Multiplying Evidence
  6. 2026When Truth Is Distributed: Misinformation Derails Collective Fact Recovery in LLM-Based Multi-Agent Systems
  7. 2026Not Just RLHF: Why Alignment Alone Won't Fix Multi-Agent Sycophancy
  8. 2023Towards CausalGPT: A Multi-Agent Approach for Faithful Knowledge Reasoning via Promoting Causal Consistency in LLMs