Endogenous Reprompting: Self-Evolving Cognitive Alignment for Unified Multimodal Models

• URL: http://arxiv.org/abs/2601.20305v1
• Date: Wed, 28 Jan 2026 06:54:36 GMT
• Title: Endogenous Reprompting: Self-Evolving Cognitive Alignment for Unified Multimodal Models
• Authors: Zhenchen Tang, Songlin Yang, Zichuan Wang, Bo Peng, Yang Li, Beibei Dong, Jing Dong,
• Abstract summary: Endogenous Reprompting transforms the model's understanding into an explicit generative reasoning step.<n>We show that SEER consistently outperforms state-of-the-art baselines in evaluation accuracy, reprompting efficiency, and generation quality.
• Score: 23.128973540926552
• License: http://creativecommons.org/licenses/by/4.0/
• Abstract: Unified Multimodal Models (UMMs) exhibit strong understanding, yet this capability often fails to effectively guide generation. We identify this as a Cognitive Gap: the model lacks the understanding of how to enhance its own generation process. To bridge this gap, we propose Endogenous Reprompting, a mechanism that transforms the model's understanding from a passive encoding process into an explicit generative reasoning step by generating self-aligned descriptors during generation. To achieve this, we introduce SEER (Self-Evolving Evaluator and Reprompter), a training framework that establishes a two-stage endogenous loop using only 300 samples from a compact proxy task, Visual Instruction Elaboration. First, Reinforcement Learning with Verifiable Rewards (RLVR) activates the model's latent evaluation ability via curriculum learning, producing a high-fidelity endogenous reward signal. Second, Reinforcement Learning with Model-rewarded Thinking (RLMT) leverages this signal to optimize the generative reasoning policy. Experiments show that SEER consistently outperforms state-of-the-art baselines in evaluation accuracy, reprompting efficiency, and generation quality, without sacrificing general multimodal capabilities.

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