Token Merging

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Less Context, Better Geometry: Masked Geometric Encoder for Robust 3D Foundation Models
Oct 5, 2026Zhimin Shao, Xijun Liu, Zhaoliang Zhang +43D Foundation Models Token Merging
Department of Electrical and Computer Engineering Johns Hopkins University · Department of Data Science University of Virginia
ZIPBrain: Can EEG Foundation Models Be Faster, Locally Deployable, but Accurate?
Aug 7, 2026Lingwei Li, Yirong Kan, Peng Chen +3Efficient Transformer Inference Token Merging
Nara Institute of Science and Technology, Nara, Japan · RIKEN Center for Computational Science, Hyogo, Japan · University of Illinois Urbana-Champaign, USA +1
UDT: Reconciling U-Nets and Diffusion Transformers with Data-Adaptive Token Reduction
Aug 2, 2026Junno Yun, Yaşar Utku Alçalar, Mehmet AkçakayaToken Merging Diffusion Transformer
University of Minnesota
Token Merging for Multilingual Speech Recognition: A Systematic Study Across Model Scale and Fine-Tuning
Jul 7, 2026Dylan Luke HolyoakToken Merging Low-Resource Speech Recognition
Geffen Academy at UCLA
Do All Visual Tokens Matter Equally? Object-Evidence Preserving Token Merging for Vision-Language Retrieval
Jul 6, 2026Suhyeong Park, Junha Jung, Jungwoo Park +1Token Merging Multi-Vector Retrieval
The Catholic University of Korea · AIGEN Sciences Inc. · Korea University
The Wiola Architecture for Efficient Small Language Models
Jul 1, 2026Aryuemaan Kumar Chowdhury, Afreen Shaik, Yaparla Bhargavi +1Rotary Positional Embeddings Token Merging
Research and Development, Oscowl Ai IIT Hyderabad Hyderabad, India · Research and Development, Oscowl Ai Hyderabad, India
Fusion: A Framework for Unified Sequential Token AdaptatIon in VisiOn TraNsformers
Jul 1, 2026Aravind Pradeep, Samira Nazari, Mahdi Taheri +1Token Merging Efficient ViTs
Brandenburg University of Technology Cottbus-Senftenberg, Germany · University of Zanjan, Iran · Humboldt University, Berlin, Germany +1
Variable-Length Tokenization via Learnable Global Merging for Diffusion Transformers
Jun 18, 2026Dong Hoon Lee, Seunghoon HongToken Merging Diffusion-Based Image Generation
Kim Jaechul Graduate School of AI, KAIST, Daejeon, South Korea · School of Computing, KAIST, Daejeon, South Korea
MergeTok: Unified Continuous and Discrete Visual Tokenization via Token Merging
May 29, 2026Luyuan Zhang, Siyuan Li, Zedong Wang +7Variational Autoencoders Token Merging
1Tsinghua University · 2Westlake University · 3Zhejiang University +3
AdaMerge: Salience-Aware Adaptive Token Merging for Training-Free Acceleration of Vision Transformers
May 26, 2026Semi Lee, Hyejin Go, Hyesong ChoiToken Merging Self-Attention
Electronic Engineering · Soongsil University · Seoul, South Korea
Locality Matters for Training-Free Audio Token Compression in Audio-Language Models
May 24, 2026Jiale Luo, Xiaoyu Liang, Haoji HuToken Merging Audio Token Compression
1Zhejiang University
ORBIS: Output-Guided Token Reduction with Distribution-Aware Matching for Video Diffusion Acceleration
May 21, 2026Hangyeol Lee, Joo-Young KimToken Merging Video Diffusion Models
KAIST · Daejeon, South Korea
TTF: Temporal Token Fusion for Efficient Video-Language Model
May 8, 2026Simin Huo, Ning LIToken Merging Efficient VLM Inference
Shanghai Jiao Tong University
Compressing Sequences in the Latent Embedding Space: $K$ -Token Merging for Large Language Models
Apr 16, 2026Zihao Xu, John Harvill, Ziwei Fan +3Token Merging LLM Compression
♢Rutgers University · ♡Mistral AI · ♠AWS AI Labs +3
StructSAM: Structure- and Spectrum-Preserving Token Merging for Segment Anything Models
Mar 7, 2026Duy M. H. Nguyen, Tuan A. Tran, Duong Nguyen +17Token Merging Image Segmentation
University of Stuttgart · DFKI · Max Planck Research School +10

Oct 5, 2026, cs.CVJ/K move · Enter open · S save

Less Context, Better Geometry: Masked Geometric Encoder for Robust 3D Foundation Models

Zhimin Shao, Xijun Liu, Zhaoliang Zhang, Yutao Tang+3

Department of Electrical and Computer Engineering Johns Hopkins University · Department of Data Science University of Virginia

3D Foundation Models Token Merging Multi-View 3D Reconstruction Sparse-View 3D Reconstruction Geometric Representation Learning

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Recent progress in 3D foundation models has enabled rapid 3D reconstruction and camera calibration by leveraging learned 3D priors from vast amount of spatial data. However, the all-to-all global attention design leads to quadratic complexity and limits long-sequence inference; unconstrained cross-view interactions also can propagate unreliable evidence from occluded or visually similar but geometrically distant views. In this paper, We introduce a Masked Geometric Encoder (MGE), which promotes the learning of robust geometric representations under incomplete cross-view context. During training, MGE strategically drops frame tokens from global attention and distills from a pretrained full-context teacher model. This allows the model to learn an intrinsically richer per-frame representation while providing sufficient intermediate supervision to avoid performance degradation. Through extensive experiments, we show that MGE leads to much stronger performance under occlusion and doppelganger views while retaining high performance on standard benchmarks. Such a richer frame representation also leads to more effective token reduction during inference. To this end, we develop a novel Anchor-Guided Adaptive token merging technique that preserves representative anchor frames while jointly merging redundant tokens from the remaining views. Compared to other efficient inference approaches, we can achieve inference speedup while consistently maintaining higher reconstruction quality, particularly in limited-view settings.

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