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Compact OCR-specific vision-language models achieve strong document parsing performance, but often rely on costly supervision and focus primarily on visual-text reconstruction. We introduce Xiaomi-OCR-0, a unified 0.8B model for document parsing and OCR-centric understanding. We build an approximately 170M-sample OCR-c...
This work identifies a systematic upstream/downstream structure in dLLM rollouts and proposes Informed Masking (IM), which derives a per-token priority score from the denoising trajectory at zero extra inference cost and biases mask sampling toward downstream tokens.
This work revisits catastrophic forgetting from an interpretability perspective and introduces correctness-guided layer alignment (CLA), which allows the new model to inherit multi-layer semantic consistency from its predecessor while learning from ground-truth supervision.
Switch-Reasoner is proposed, a GRPO-based framework that learns to adaptively select reasoning modes for MLLMs and introduces a dual-level regulation mechanism that balances the overall use of Thinking Mode and Direct Mode while providing sample-level supervision based on the relative benefit of the two choices.
DeltaV is proposed, a ULMM that replaces full-image generation with visual updates and introduces a temporal similarity (TSIM) Router, which stops allocating tokens once the marginal reconstruction gain falls below a threshold.
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