2 citations · 2 across the 13 of their papers we have counts for
5 papers · 1 filter
Universal Test-Time Training
Zefan Cai, Qinzhe Hu, Ziqiao Ma +2
Recent Test-Time Training (TTT) architectures compress context into fast weights that are updated online and queried as memory. Existing TTT designs keep this memory private to eac…
Multi-Head Attention Residuals
Cheng Luo, Zefan Cai, Junjie Hu
Transformers propagate information across depth through a single additive residual stream: every sublayer reads only the most recent state. Attention residuals relax this by lettin…
Test-Time Training with Next-Token Prediction
Xuan Ouyang, Zefan Cai, Junjie Hu
Next-token prediction is the self-supervised signal that trains language models, and every observed prompt token provides the same signal at test time. We study whether this signal…
GRASP: Graph Agentic Search over Propositions for Multi-hop Question Answering
Stockton Jenkins, Ramya Korlakai Vinayak, Junjie Hu
Agentic retrieval improves multi-hop question answering by giving language models autonomy to iteratively gather evidence. Recent work augments these systems with knowledge graphs…
Delta Attention Residuals
Cheng Luo, Zefan Cai, Junjie Hu
Attention Residuals replace standard additive residual connections with learned softmax attention over previous layer outputs, enabling selective cross-layer routing. However, stan…