6 citations · 6 across the 7 of their papers we have counts for
6 papers · 1 filter
A Tilted Bowl Is Not a Slippery Slope: Compressing Looped Models
Steven Kolawole, Pearse Jim, Opegbemi M. Busoye +2
Looped models reason by applying the same block of weights many times, so compressing that block saves memory traffic on every loop. Compressed looped models, however, often collap…
EpiKV: Epiphany-Aware KV Cache Eviction Without the Attention Matrix
Steven Kolawole, Virginia Smith
Reasoning models can generate chains of thought tens of thousands of tokens long, making the key--value (KV) cache that holds them a major bottleneck for inference throughput. Exis…
What Survives When You Compress a Recursive Reasoner for the Edge?
Pearse Jim, Steven Kolawole, Opegbemi Matthias Busoye +2
Recursive reasoning models solve hard structured tasks with a few million parameters by iterating a latent state, but deploying them on edge hardware means compressing them -- and…
PARALLELPROMPT: Extracting Parallelism from Large Language Model Queries
Steven Kolawole, Keshav Santhanam, Virginia Smith +1
LLM serving systems typically treat user prompts as monolithic inputs, optimizing inference through decoding tricks or inter-query batching. However, many real-world prompts contai…
Agreement-Based Cascading for Efficient Inference
Steven Kolawole, Don Dennis, Ameet Talwalkar +1
Adaptive inference schemes reduce the cost of machine learning inference by assigning smaller models to easier examples, attempting to avoid invocation of larger models when possib…
Everybody Prune Now: Structured Pruning of LLMs with only Forward Passes
Steven Kolawole, Lucio Dery, Jean-François Kagy +3
Structured pruning is a promising approach to create smaller, faster large language models. However, existing methods typically rely on computing the gradient via backward passes,…