DeCIR improves projection-based zero-shot composed image retrieval by decoupling endpoint and semantic transition alignment with separate low-rank adapters merged by LRDM, showing gains on CIRR, CIRCO, FashionIQ, and GeneCIS.
Model soups: aver- aging weights of multiple fine-tuned models improves accuracy without increasing inference time
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λ-Orthogonality regularization enables distribution-specific adaptation of representations via affine transformations while retaining original learned structures.
A training-free, sample-wise model merging method that rescales task vectors based on representation similarity outperforms several static merging baselines on vision and language benchmarks.
Step-Audio-AQAA, a 130B end-to-end audio language model using dual-codebook tokens, text-audio interleaving, masked DPO and weight merging, is claimed to outperform Kimi-Audio and Qwen-Omni on the authors' StepEval-Audio-360 benchmark.
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Decoupling Endpoint and Semantic Transition Learning for Zero-Shot Composed Image Retrieval
DeCIR improves projection-based zero-shot composed image retrieval by decoupling endpoint and semantic transition alignment with separate low-rank adapters merged by LRDM, showing gains on CIRR, CIRCO, FashionIQ, and GeneCIS.
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$\boldsymbol{\lambda}$-Orthogonality Regularization for Compatible Representation Learning
λ-Orthogonality regularization enables distribution-specific adaptation of representations via affine transformations while retaining original learned structures.
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SE-Merging: A Self-Enhanced Approach for Dynamic Model Merging
A training-free, sample-wise model merging method that rescales task vectors based on representation similarity outperforms several static merging baselines on vision and language benchmarks.
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Step-Audio-AQAA: a Fully End-to-End Expressive Large Audio Language Model
Step-Audio-AQAA, a 130B end-to-end audio language model using dual-codebook tokens, text-audio interleaving, masked DPO and weight merging, is claimed to outperform Kimi-Audio and Qwen-Omni on the authors' StepEval-Audio-360 benchmark.