4 citations · 7 across the 10 of their papers we have counts for
4 papers · 1 filter
Mirror Speculative Decoding: Breaking the Serial Barrier in LLM Inference
Nikhil Bhendawade, Kumari Nishu, Arnav Kundu +3
Speculative decoding accelerates LLM inference by using a draft model to look ahead, but gains are capped by the cost of autoregressive draft generation: increasing draft size elev…
FS-DFM: Fast and Accurate Long Text Generation with Few-Step Diffusion Language Models
Amin Karimi Monsefi, Nikhil Bhendawade, Manuel Rafael Ciosici +3
Autoregressive language models (ARMs) deliver strong likelihoods, but are inherently serial: they generate one token per forward pass, which limits throughput and inflates latency…
Apple Intelligence Foundation Language Models: Tech Report 2025
Ethan Li, Anders Boesen Lindbo Larsen, Chen Zhang +395
We introduce two multilingual, multimodal foundation language models that power Apple Intelligence features across Apple devices and services: i a 3B-parameter on-device model opti…
M2R2: Mixture of Multi-Rate Residuals for Efficient Transformer Inference
Nikhil Bhendawade, Mahyar Najibi, Devang Naik +1
Residual transformations enhance the representational depth and expressive power of large language models (LLMs). However, applying static residual transformations across all token…