VerifiAgent: a Unified Verification Agent in Language Model Reasoning
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
67ee3e63e7defc1b8655c8f6
Jiuzhou Han
admin_assigned
2025-04-03T14:06:39.670Z
63b0e5a7f2eb87a4d695398a
/avatars/3a03fbb3edbb4aad848cd63c0bce6853.svg
Jiuzhou Han
user
Jiuzhouh
67ee3e63e7defc1b8655c8f7
Wray Buntine
admin_assigned
2025-04-03T14:06:46.133Z
65bf02eb4d1795fa8ede0d3f
/avatars/91bdd8eabdf19d61b55493193c222a73.svg
Wray Buntine
user
wbuntine
67ee3e63e7defc1b8655c8f8
Ehsan Shareghi
admin_assigned
2025-04-03T14:06:53.451Z
65f2a8220effe20744129714
/avatars/524681fa6dda88d6ae20cb68c1bbe6d8.svg
Ehsan Shareghi
user
eehsan
VerifiAgent: a Unified Verification Agent in Language Model Reasoning
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
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.
meta-verification tool-based adaptive verification VerifiAgent mathematical reasoning logical reasoning commonsense reasoning deductive verifier backward verifier inference scaling process reward models