6 papers
No More K-means: Single-Stage Sparse Coding for Efficient Multi-Vector Retrieval
Lixuan Guo, Yifei Wang, Tiansheng Wen +3
Multi-vector retrieval (MVR) models, exemplified by ColBERT, have established new benchmarks in retrieval accuracy by preserving fine-grained token-level interactions. However, thi…
Scaling Attention via Feature Sparsity
Yan Xie, Tiansheng Wen, Tangda Huang +4
Scaling Transformers to ultra-long contexts is bottlenecked by the cost of self-attention. Existing methods reduce this cost along the sequence axis through local window…
Route Experts by Sequence, not by Token
Tiansheng Wen, Yifei Wang, Aosong Feng +7
Mixture-of-Experts (MoE) architectures scale large language models (LLMs) by activating only a subset of experts per token, but the standard TopK routing assigns the same fixed num…
Martian World Model: Controllable Video Synthesis with Physically Accurate 3D Reconstructions
Longfei Li, Zhiwen Fan, Wenyan Cong +10
Synthesizing realistic Martian landscape videos is crucial for mission rehearsal and robotic simulation. However, this task poses unique challenges due to the scarcity of high-qual…
Beyond Matryoshka: Revisiting Sparse Coding for Adaptive Representation
Tiansheng Wen, Yifei Wang, Zequn Zeng +7
Many large-scale systems rely on high-quality deep representations (embeddings) to facilitate tasks like retrieval, search, and generative modeling. Matryoshka Representation Learn…
UniHGKR: Unified Instruction-aware Heterogeneous Knowledge Retrievers
Dehai Min, Zhiyang Xu, Guilin Qi +2
Existing information retrieval (IR) models often assume a homogeneous structure for knowledge sources and user queries, limiting their applicability in real-world settings where re…