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20242026
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cs.CL2026

Draft-KV: Learning Useful Latent Communication Between Language Models

Linquan Wu, Shichang Meng, Tianxiang Jiang +7

Latent communication passes internal states between language models instead of decoded text, but higher receiver accuracy does not show that the receiver used the message content.…

cs.LG2026

Temporal-Spectral Alignment with Frequency Adaptation for Source-Free Time-Series Adaptation

Shichang Meng, Linquan Wu, Xuan Ai +1

The goal of source-free domain adaptation (SFDA) for time-series data is to transfer knowledge from a pre-trained source model to an unlabeled target domain without requiring acces…

cs.AI2026

Mind-Studio: Executable World Models with Lookahead Evaluation for Partially Observable Games

Yifei Dong, Mingen Zheng, Linquan Wu +2

World-model synthesis aims to turn interaction experience into an internal model of environment dynamics. Existing symbolic approaches often fit observed transitions or mixtures of…

cs.CV2026

Imagine Before You Predict: Interleaved Latent Visual Reasoning for Video Event Prediction

Tianxiang Jiang, Linquan Wu, Sheng Xia +5

Video event prediction (VEP) requires models to infer unobserved future states from partial video evidence. Existing video MLLMs usually verbalize intermediate future reasoning in…

cs.CV2026

LaViT: Aligning Latent Visual Thoughts for Multi-modal Reasoning

Linquan Wu, Tianxiang Jiang, Yifei Dong +6

Current multimodal latent reasoning often relies on external supervision (e.g., auxiliary images), ignoring intrinsic visual attention dynamics. In this work, we identify a critica…