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SCSP is a training-free framework that leverages semantic compression for informative token selection in long-context embedding and can be integrated into both zero-shot and fine-tuned models in a plug-and-play manner, consistently improving their performance.
This work proposes an inference-time, plug-and-play Self-Contrastive Steering (SCS) method that constructs unconditional general text embeddings and uses them to refine conditional text embeddings, making them more focused on the target condition.
A Dynamic Distribution-Aware Uncertainty Quantification framework (DDA-UQ) is proposed that shifts the paradigm from static mapping to a dynamic distribution-aware process and significantly outperforms state-of-the-art methods.
Step-Level On-Policy Distillation is proposed, which combines the long-horizon correction of supervised fine-tuning (SFT) with the on-policy advantage of OPD to provide step-level supervision over complete student-generated trajectories and substantially outperforms conventional SFT and OPD.
This work proposes a structured suffix modeling method that incorporates the decoding results from the previous step into the suffix token representations at the current step, allowing them to carry evolving denoising information across generation steps.
Class-wise Covariance Regularization is proposed, which aligns the predicted covariance structure of class confidences with the semantic correlations encoded in pretrained text embed-dings with the geometric consistency of the class space throughout fine-tuning, resulting in more stable and interpretable confidence dis...
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