TextualVerifier: Verify TextGrad Step-by-Step

• URL: http://arxiv.org/abs/2511.03739v1
• Date: Wed, 29 Oct 2025 13:53:42 GMT
• Title: TextualVerifier: Verify TextGrad Step-by-Step
• Abstract summary: TextualVerifier implements chain-of-thought decomposition, variant generation, majority voting, and consensus aggregation.<n>It integrates non-invasively with TextGrad at both the loss function and optimization result verification stages.
• Score: 0.25489046505746704
• License: http://creativecommons.org/licenses/by/4.0/
• Abstract: TextGrad is a novel approach to text-based automatic differentiation that enables composite AI systems to perform optimization without explicit numerical equations. However, it currently lacks self-verification mechanisms that ensure reasoning validity in text-based decision making. This research introduces TextualVerifier, a verification framework that leverages chain-of-thought reasoning and majority voting with large language models to address this verification gap. TextualVerifier implements a four-stage workflow: chain-of-thought decomposition, variant generation, majority voting, and consensus aggregation. It integrates non-invasively with TextGrad at both the loss function and optimization result verification stages. Experimental evaluation using the Gemini 1.5 Pro model is conducted in two phases: (1) standalone evaluation on PRM800K, and (2) integrated evaluation with TextGrad on GPQA-Diamond, MMLU-ML, and MMLU-CP benchmarks. Results show statistically significant improvements (p < 0.001). In phase one, TextualVerifier improves the validity of reasoning steps by 29 percent. In phase two, integration into TextGrad loss function yields a 2.2 percentage point gain from 68.2 to 70.4 percent with a moderate overhead of 5.9 LLM calls on average. Further evaluations of TextualVerifier versioning yield 8.08, 10.71, and 3.92 percentage point improvements on GPQA, MMLU-ML, and MMLU-CP respectively. TextualVerifier thus presents the first self-verification framework for TextGrad through LLM-based techniques without requiring numerical gradients, enabling more reliable reasoning and opening new directions for verification in text-based optimization.

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