Evaluating Self-Correcting Vision Agents Through Quantitative and Qualitative Metrics

• URL: http://arxiv.org/abs/2601.11637v1
• Date: Wed, 14 Jan 2026 15:17:11 GMT
• Title: Evaluating Self-Correcting Vision Agents Through Quantitative and Qualitative Metrics
• Authors: Aradhya Dixit,
• Abstract summary: Vision-Language Agents (VLAs) can decompose complex visual tasks into executable tool-based plans.<n>Recent benchmarks have begun to evaluate iterative self-correction, but its quantitative limits and dominant reasoning bottlenecks remain poorly characterized.
• Score: 0.0
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: Recent progress in multimodal foundation models has enabled Vision-Language Agents (VLAs) to decompose complex visual tasks into executable tool-based plans. While recent benchmarks have begun to evaluate iterative self-correction, its quantitative limits and dominant reasoning bottlenecks remain poorly characterized. This work introduces a Diagnostic Micro-Benchmark. Our analysis decouples Task Success Rate (TSR = 62 percent) from Correction Success Rate (CSR = 25 to 33 percent), revealing that initial competence does not predict repair ability. We explicitly quantify the diminishing returns of correction, which saturates after three retries. Our Failure Taxonomy reveals a frequent factor is Semantic Drift (about 28 percent of failures), a loss of contextual state. By isolating this reasoning bottleneck, this benchmark defines a reproducible framework toward stateful, trustworthy multimodal agents.

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