title: VerifiAgent: a Unified Verification Agent in Language Model Reasoning
summary: Large language models demonstrate remarkable reasoning capabilities but often
  produce unreliable or incorrect responses. Existing verification methods are
  typically model-specific or domain-restricted, requiring significant
  computational resources and lacking scalability across diverse reasoning tasks.
  To address these limitations, we propose VerifiAgent, a unified verification
  agent that integrates two levels of verification: meta-verification, which
  assesses completeness and consistency in model responses, and tool-based
  adaptive verification, where VerifiAgent autonomously selects appropriate
  verification tools based on the reasoning type, including mathematical,
  logical, or commonsense reasoning. This adaptive approach ensures both
  efficiency and robustness across different verification scenarios. Experimental
  results show that VerifiAgent outperforms baseline verification methods (e.g.,
  deductive verifier, backward verifier) among all reasoning tasks. Additionally,
  it can further enhance reasoning accuracy by leveraging feedback from
  verification results. VerifiAgent can also be effectively applied to inference
  scaling, achieving better results with fewer generated samples and costs
  compared to existing process reward models in the mathematical reasoning
  domain. Code is available at https://github.com/Jiuzhouh/VerifiAgent
paper_authors._id: 67ee3e63e7defc1b8655c8f6
paper_authors.name: Jiuzhou Han
paper_authors.status: admin_assigned
paper_authors.statusLastChangedAt: 2025-04-03T14:06:39.670Z
paper_authors._id: 63b0e5a7f2eb87a4d695398a
paper_authors.avatarUrl: /avatars/3a03fbb3edbb4aad848cd63c0bce6853.svg
paper_authors.fullname: Jiuzhou Han
paper_authors.type: user
paper_authors.user: Jiuzhouh
paper_authors._id: 67ee3e63e7defc1b8655c8f7
paper_authors.name: Wray Buntine
paper_authors.status: admin_assigned
paper_authors.statusLastChangedAt: 2025-04-03T14:06:46.133Z
paper_authors._id: 65bf02eb4d1795fa8ede0d3f
paper_authors.avatarUrl: /avatars/91bdd8eabdf19d61b55493193c222a73.svg
paper_authors.fullname: Wray Buntine
paper_authors.type: user
paper_authors.user: wbuntine
paper_authors._id: 67ee3e63e7defc1b8655c8f8
paper_authors.name: Ehsan Shareghi
paper_authors.status: admin_assigned
paper_authors.statusLastChangedAt: 2025-04-03T14:06:53.451Z
paper_authors._id: 65f2a8220effe20744129714
paper_authors.avatarUrl: /avatars/524681fa6dda88d6ae20cb68c1bbe6d8.svg
paper_authors.fullname: Ehsan Shareghi
paper_authors.type: user
paper_authors.user: eehsan
paper_title: VerifiAgent: a Unified Verification Agent in Language Model Reasoning
paper_summary: Large language models demonstrate remarkable reasoning capabilities but often
  produce unreliable or incorrect responses. Existing verification methods are
  typically model-specific or domain-restricted, requiring significant
  computational resources and lacking scalability across diverse reasoning tasks.
  To address these limitations, we propose VerifiAgent, a unified verification
  agent that integrates two levels of verification: meta-verification, which
  assesses completeness and consistency in model responses, and tool-based
  adaptive verification, where VerifiAgent autonomously selects appropriate
  verification tools based on the reasoning type, including mathematical,
  logical, or commonsense reasoning. This adaptive approach ensures both
  efficiency and robustness across different verification scenarios. Experimental
  results show that VerifiAgent outperforms baseline verification methods (e.g.,
  deductive verifier, backward verifier) among all reasoning tasks. Additionally,
  it can further enhance reasoning accuracy by leveraging feedback from
  verification results. VerifiAgent can also be effectively applied to inference
  scaling, achieving better results with fewer generated samples and costs
  compared to existing process reward models in the mathematical reasoning
  domain. Code is available at https://github.com/Jiuzhouh/VerifiAgent
paper_ai_summary: VerifiAgent is a unified verification agent that integrates meta-verification and tool-based adaptive verification to enhance the reliability and accuracy of reasoning tasks across different domains.
paper_ai_keywords: meta-verification tool-based adaptive verification VerifiAgent mathematical reasoning logical reasoning commonsense reasoning deductive verifier backward verifier inference scaling process reward models