6 citations · 6 across the 6 of their papers we have counts for
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cs.LG2026
Adaptive-Depth Is a Finishing Stage
Andrei Piterbarg, Sean McLeish, Dhruv Pai +1
Looped transformers reuse the same layer multiple times, allowing inference compute to be adjusted without changing the model. Recent adaptive-depth methods train the model across…
cs.CL2026
End-to-End Context Compression at Scale
Ang Li, Sean McLeish, Haozhe Chen +12
Long-context language model inference is bottlenecked by memory, as the KV cache grows with context length. Recent techniques to compress the KV cache fall short: they either degra…
cs.CL2026
Do Language Models Need Sleep? Offline Recurrence for Improved Online Inference
Sangyun Lee, Sean McLeish, Tom Goldstein +1
Transformer-based large language models are increasingly used for long-horizon tasks; however, their attention mechanism scales poorly with context length. To handle this, we study…