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This work proposes Counterfactual Evidence Disentanglement (CED), a training-time evidence audit for VLM grounding, which outperforms prior RL-based post-training methods, with targeted analyses verifying its object-centric signal.
In-Context Forcing is introduced, a progressive autoregressive paradigm that utilizes contexts with decreasing noise levels that enables cross-frame parallel denoising, achieving substantial inference acceleration without sacrificing performance.
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