arXiv 2610.31305 · cs.CL · 2026-10-22
Cola2: Co-Adaptive Hierarchical Language Modeling with Continuous Semantic Compression and Balanced Discrete Generation
Hongcan Guo, Yian Zhao, Feng Wang, Shen Nie, Guoqiang Wei, Yan Zeng, Hengshuang Zhao
ByteDance Seed
In latest · #343 Added on 2026-10-26
One-line takeaway
Cola2 is a hierarchical language-modeling framework that compresses dynamically segmented semantic spans into continuous latents and realizes text with a block-causal masked diffusion decoder, with the encoder and decoder co-adapting.
Cola2 是分层语言生成框架:把文本动态切成语义片段压缩成连续潜变量,再用块因果掩码扩散解码器生成离散文本,并让编码器与解码器协同适配。
Read on arXiv PDF Hugging Face discussion Project page
Topics & disciplines
More on LLMs
- Jeva: System One Models from First Principles ▲371
- Post-Training Leaves Behavioral Shadows on Unrelated Decisions ▲273
- OmniEdu: Open Foundation Models for Learning and Teaching ▲240
- PAR: Progress-Aligned Recurrence for Length Generalization in State Tracking ▲163
- MemT-1.0: Towards Memory-Native Evolution ▲143
More on Efficient training & inference
- MJev-Spatial: System One Spatial Intelligence via Native Attention Matching 2026-10-15 · ▲556
- The Teacher Is a Direction, Not a Destination: Extrapolating RL-Induced Representation Residuals in On-Policy Distillation 2026-09-29 · ▲511
- VideoJev: System One Model for Video Understanding and Interaction 2026-10-13 · ▲375
- LEV: Thinking Fast and Deep with Looped Decision Models 2026-10-14 · ▲337
- Scaling Properties of Same-Family On-Policy Distillation 2026-09-26 · ▲327
More from ByteDance Seed
About this data
Metadata from the arXiv API (verified); upvotes from Hugging Face Daily Papers; citations from Semantic Scholar. Last refreshed 2026-10-26.
The takeaway is written by AIMap from the abstract; the abstract itself is not reproduced — read the original.