PRIDE: Prediction Reconciliation via Internal Divergence Elicitation for Self-Evolving Vision-Language Models

Vision-language models can answer a visual attribute correctly in isolation yet fail on the same attribute inside a compound query. Because such disagreement may arise from prompt form or sampling noise, we first establish a controlled premise: a subset of atomic-joint disagreements persists under paraphrase, length/schema matching, and repeated rollouts, and is enriched for recoverable joint errors. We introduce PRIDE, a label-free self-evolution framework that filters this stable task-conditioned divergence and converts accepted output corrections into visual-parameter updates through a preservation-aware Jacobian/Gram solve. Under a controlled Qwen3.5-4B setting trained on the full ADOPD-Dataset-6K across twelve disjoint 500-image rounds, with MMAD reserved exclusively for evaluation, PRIDE establishes a new state of the art on the MMAD AD/DC subtasks, reaching 85.57/74.40 in 0-shot and 87.16/76.02 in 1-shot evaluation. The gains further transfer across Qwen3-VL-4B, LLaVA-OneVision-1.5-4B, and Gemma-3n-E4B, and extend beyond industrial anomaly reasoning to open-ended GQA and ChartQA. We additionally report the complete seven-task MMAD profile to verify that improvements on AD/DC do not come at the expense of localization and other non-target capabilities. These results support atomic–joint task-conditioned divergence as a reliable endogenous supervision signal for self-evolving vision-language models when signal stability, output coupling, and local update validity are explicitly controlled.