ProxyFormer: A Dual-Stream Proxy Architecture for Ultra-Long Context and High-Resolution Generation
Score
75提出双流代理架构，显著扩展可训练上下文长度，信息增量高。
方法描述完整，实验覆盖语言与图像，但缺乏更广泛基准。
可在 16GB GPU 上训练超长序列，对资源受限研究有实际价值。
面向超长上下文和高效推理这一前沿热点，发布及时。
来自 arXiv 预印本，尚未经同行评审，可靠性中等偏上。
Base 83 × T2 Source weight ×0.9 = 75
Curation line 70Selected
Why selected
The method is novel with strong experimental coverage across text and image generation, though not yet peer-reviewed and requiring closer scrutiny.
AI commentary
「This paper stands out because it doesn't just patch attention; it moves global interactions into a compressed proxy space via a dual-stream design, tackling quadratic complexity at the architectural level. It's a must-read for researchers working on ultra-long text inference and efficient generation—even the dynamic compression ratio and proxy-only KV-cache inference alone could inspire new optimization directions.」
Quick overview
ProxyFormer proposes a dual-stream architecture based on proxy tokens. Fine-grained local features are compressed bottom-up into a small set of proxy states; expensive global interactions are performed only in the compressed proxy space; then the context-rich proxies are decompressed and injected top-down back into the local stream. Because the local stream persists across layers, information missed by one compression can be refined later, mitigating irreversible loss. With a compression ratio of 64 on a 16GB GPU, ProxyFormer extends the trainable sequence length from about 20K to about 0.7M tokens. A model trained with a 64K window retains 92%-95% retrieval accuracy on a 1,048,576-token multi-needle task, while an 8K-window model exceeds 94% accuracy extrapolated to 256K tokens. Preliminary image generation experiments confirm feasibility in both pixel-space and latent-space flow matching.
- Dual-stream proxy architecture keeps the local stream persistent and performs global interactions in compressed proxy space, reducing attention and KV cache overhead.
- Introduces factorized multi-level compression/decompression, layer-wise dynamic compression ratios, asymmetric dual embeddings, and proxy-only KV-cache inference.
- Extends trainable sequence length from ~20K to ~0.7M tokens with compression ratio 64 on a 16GB GPU.
- Trained with 64K window retains 92%-95% accuracy on 1M-token multi-needle retrieval; 8K-window model exceeds 94% at 256K extrapolation.
Full text
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