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Matching papers

FuseReg: Regularizing Layer Fusion Mitigates the Reconstruction-Generation Gap in Representation Autoencoders

112 upvotes · 25 SEP 2026 · Hongyang Du, Yunfei Xie, Junjie Ye et al.

This paper improves the performance of representation autoencoders (RAEs) by training a decoder to work with different layers of a pre-trained visual encoder, rather than relying on a fixed layer fusion. A practitioner might care about this because it allows for more flexible and robust image generation.

representation learningautoencoderslayer fusionregularizationdiffusion models

LatentPress: Context Compression Beyond Text and Vision

109 upvotes · 1 SEP 2026 · Zhengze Zhou, Hejian Sang

This paper introduces LatentPress, a method to compress conversational histories and documents into a continuous memory token format that allows language models to directly read and process the context without text reconstruction. Practitioners might care about this because it could lead to faster and more efficient language model inference.

continual learningcontext compressionlanguage modelsmemory tokensrepresentation learning