3 sources
A strong person to study for the modern NLP stack because his work spans denoising pretraining, retrieval-augmented generation, and later long-context inference tricks rather than only one phase of the language-model pipeline.
Researcher Profile
Editor reviewedGuangxuan Xiao
Streaming + long-context stability (attention sinks)
Researcher at MIT working on efficient long-context and multimodal inference
A strong systems page because his work repeatedly shows up where inference efficiency meets usable long context, especially in attention sinks, StreamingLLM, post-training quantization, and later long-context head designs.
Organizations
Massachusetts Institute of Technology
About This Page
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Known For
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01
Attention sinks and StreamingLLM
02
Long-context inference efficiency
03
Quantization and efficient deployment
04
Streaming + long-context stability (attention sinks)
05
Efficient Streaming Language Models with Attention Sinks
06
Long context
Start Here
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Signature Works
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