MAVEN: Multi-Agent Verification-Elaboration Network with In-Step Epistemic Auditing
On the map: LLM reasoning → Reasoning for agents, planning and code
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Abstract (arXiv)
While explicit reasoning trajectories enhance model interpretability, existing paradigms often rely on monolithic chains that lack intermediate verification, allowing early errors to cascade unchecked. This lack of modularity impedes granular auditing and compromises the epistemic trust required for high-stakes applications. We propose MAVEN (Multi-Agent Verification-Elaboration Network with In-Step Epistemic Auditing), a blackboard-inspired framework designed to transform LLMs into deliberate reasoners through explicit role-decoupling. At its core, MAVEN operationalizes an adversarial Skeptic-Researcher-Judge loop, simulating expert deliberation by functionally separating logical defense from factual grounding. Experiments on OpenBookQA, TruthfulQA, HALUEVAL and StrategyQA benchmarks demonstrate that MAVEN delivers superior reasoning quality across four fine-grained metrics. Notably, MAVEN consistently outperforms latent reasoning models such as GEMINI-3.1-Pro and consensus-based baselines (e.g., ReConcile) by generating explicitly structured, modular, and verifiable deliberation trajectories, rather than relying on implicit internal states or post-hoc consensus. Moreover, comprehensive evaluations confirm that MAVEN is fully model-agnostic, serving as a strong and transferable reasoning booster that yields substantial performance improvements across diverse backbone models.
Key facts. Cited by 1 paper in the map (data through 7 Oct 2026). Placed in Reasoning for agents, planning and code, one of 17 directions in LLM reasoning. Builds on 16 of its 58 references that are in the map. First posted on arXiv 8 May 2026.
Builds on
16 of 58 references.
Closest ancestors · 2
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- 2025Talk Isn't Always Cheap: Understanding Failure Modes in Multi-Agent Debate5 Sep
- 2024Mixture-of-Agents Enhances Large Language Model Capabilities7 Jun
Other references · 14
- 2025DeepSeek-R1: Incentivizing Reasoning Capability in LLMs via Reinforcement Learningcited by 6,146
- 2022Language Models (Mostly) Know What They Knowcited by 1,783
- 2022Least-to-Most Prompting Enables Complex Reasoning in Large Language Modelscited by 1,397
- 2023ChatEval: Towards Better LLM-based Evaluators through Multi-Agent Debatecited by 724
- 2024DeepSeek-V3 Technical Reportcited by 724
- 2023Plan-and-Solve Prompting: Improving Zero-Shot Chain-of-Thought Reasoning by Large Language Modelscited by 550
- 2024Scaling Large Language Model-based Multi-Agent Collaborationcited by 235
- 2024LLMs Know More Than They Show: On the Intrinsic Representation of LLM Hallucinationscited by 230
- 2023LM vs LM: Detecting Factual Errors via Cross Examinationcited by 155
- 2025Multi-Agent Collaboration via Evolving Orchestrationcited by 102
- 2025Reasoning Models Hallucinate More: Factuality-Aware Reinforcement Learning for Large Reasoning Modelscited by 18
- 2025Incentivizing LLMs to Self-Verify Their Answerscited by 16
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Citing papers per year: 2026: 1.
1 paper.
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- 2026MAVEN: Improving Generalization in Agentic Tool Calling
- 2025On Generalization in Agentic Tool Calling: CoreThink Agentic Reasoner and MAVEN Dataset
- 2026Evidence-Grounded Verified Agentic Reasoning: A Path Toward Eliminating LLM Hallucination in Empirical Inference via Tool-Attested Kernel Proofs
- 2026Verify Before You Commit: Towards Faithful Reasoning in LLM Agents via Self-Auditing
- 2026VeriHarness: Scaling Agentic Verification for Long-Horizon Tasks
- 2026Auditing Multi-Agent LLM Reasoning Trees Outperforms Majority Vote and LLM-as-Judge
- 2026Project Ariadne: A Structural Causal Framework for Auditing Faithfulness in LLM Agents
- 2026Emergent Collaborative Deliberation in Multi-Model AI Systems: A BFT-Derived Protocol for Epistemic Synthesis