ProxyFormer reduces transformer attention cost for long context and high-resolution generation
Source published ·Modelwire updated
Original coverage: arXiv cs.LG ↗·How Modelwire adds context
The development
ProxyFormer addresses a fundamental scaling constraint in modern transformers: the quadratic memory and compute cost of attention as context length and image resolution grow. The architecture compresses fine-grained features into proxy tokens at each layer, performs expensive global computations only on this compressed representation, then decompresses results back into the full feature stream. By preserving the local stream across layers, the method avoids irreversible information loss that plagues conventional compression schemes. This dual-stream design could unlock practical ultra-long-context models and high-resolution generation without the prohibitive KV cache overhead that currently limits deployment, making it strategically relevant for both language and vision model scaling.
Modelwire’s AI-generated summary of coverage from arXiv cs.LG.
Modelwire analysis
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The key innovation is that ProxyFormer preserves a full-resolution local stream alongside the compressed proxy stream at every layer, rather than discarding detail after compression. This reversibility is what prior work sacrificed.
This connects directly to the efficiency gains pursued in 'Provably adaptive sampling with uniform and remasking discrete diffusion models' (arXiv cs.LG, August 24), which also tackled a scaling bottleneck by proving that linear complexity with dimension isn't inherent to the forward process. Both papers attack the same class of problem: showing that what looks like a fundamental cost (quadratic attention here, linear sampling there) can be restructured without losing capability. ProxyFormer's dual-stream approach parallels the philosophy behind Prime Agent's separation of strategy from infrastructure; both recognize that you can compress expensive operations if you preserve the ability to recover full fidelity when needed.
If ProxyFormer's compression ratio and latency gains hold up when tested on the same long-context benchmarks used to evaluate recent open-source LLMs (like those in the 200k+ token range), that confirms the method scales beyond toy settings. If the decompression step introduces measurable quality loss on downstream tasks compared to full-attention baselines, the reversibility claim needs scrutiny.
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Coverage behind this analysis
These archive entries ground the connection in our analysis. They are ordered by source publication date, with links to our coverage and the original sources.
·arXiv cs.LG
New sampler breaks dimension scaling limits in discrete diffusion models
Researchers have solved a longstanding efficiency bottleneck in discrete diffusion models by proving that linear scaling with dimension is not inherent to the uniform forward process. A new leave-one-out denoiser sampler enables parallel coordinate updates while correcting mistakes during generation, potentially unlocking faster inference for non-autoregressive discrete generation. This addresses a core limitation that has…
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Modelwire summarizes, we don’t republish. arXiv cs.LG originally reported this story as “ProxyFormer: A Dual-Stream Proxy Architecture for Ultra-Long Context and High-Resolution Generation”. The full content lives on arxiv.org. If you’re a publisher and want a different summarization policy for your work, see our takedown page.