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cs.LG2025
Global Resolution: Optimal Multi-Draft Speculative Sampling via Convex Minimization
Rahul Krishna Thomas, Arka Pal
Speculative sampling reduces the latency of autoregressive decoding for target model LLMs without sacrificing inference quality, by using a cheap draft model to suggest a candidate…
cs.LG2025
Cascade: Token-Sharded Private LLM Inference
Rahul Thomas, Louai Zahran, Erica Choi +3
As LLMs continue to increase in parameter size, the computational resources required to run them are available to fewer parties. Therefore, third-party inference services -- where…
cs.CR2025
An Attack to Break Permutation-Based Private Third-Party Inference Schemes for LLMs
Rahul Thomas, Louai Zahran, Erica Choi +3
Recent advances in Large Language Models (LLMs) have led to the widespread adoption of third-party inference services, raising critical privacy concerns. Existing methods of perfor…