HOMIE proposes a better MLLM integration strategy to extract knowledge of reference-level relationships without compromising the controllability of text encoders or incurring costly re-alignment, and introduces global multimodal guidance within self-attention to better align MLLM-derived semantic features with VAE toke...
Yiyang Cai, Nan Chen, Rongchang Xie et al.· arXiv.org· 0 citations
This work proposes ReBind, a systematic framework that introduces semantic instructions with embedded reference tokens as the intermediate representation for multi-reference image-conditioned video editing and develops ReBind-Instruct, a specialized MLLM that learns to establish explicit bindings between visual attribu...
Xin-Yu Liu, Shi-Hao Li, Weihong Lin et al.· arXiv.org· 2 citations
Experimental results indicate that fully automated data curation combined with imbalance-aware training yields non-trivial improvements, but models still struggle to capture fine-grained acoustic features, indicating a gap between surface-level alignment and deep musical comprehension.
Zi-Ya Zhou, Shangda Wu, Shenyang Xu et al.· 0 citations
FISA is proposed, a framework for MLLM self-improvement that constructs augmented images from the model's own failure cases that generates visually challenging yet answer-preserving image complications, verifies their utility through self-examination, and applies dual fidelity filtering to avoid semantic distortion.
Chun-Yang Jiang, Pingping Zhang, Yuzhi Zhao et al.· 0 citations
Test-Time Harness Evolution is introduced, which treats the executable harness as the state of test-time adaptation for LLM agents as evolution over executable control programs and identifies execution-derived proxy reliability as a central challenge for robust unsupervised agent improvement.
Jun Nie, Yonggang Zhang, Jun Song et al.· arXiv.org· 3 citations
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