6 citations · 7 across the 20 of their papers we have counts for
6 papers · 1 filter
Entropy-Constrained Adaptive Stochastic Quantization
Ran Ben Basat, Yaniv Ben-Itzhak, Michael Mitzenmacher +1
Adaptive stochastic quantization (ASQ) is a recently introduced quantization approach that optimizes the Mean Squared Error (MSE) for a given input while preserving unbiasedness. I…
On Topology's Role in ML Training Performance
Sarah McClure, Tegan Wilson, Brad Karp +4
Modern machine learning training workloads run on large-scale networks of compute accelerators. The networks commonly deployed in these systems are typically variations of two basi…
Context Compaction Theory
Hayder Tirmazi, Sam Markelon, Allison Bishop +1
Large Language Models (LLMs) have a bounded context window. The context window is the maximum input size an LLM can consume for a single inference. AI agents rely on a process call…
SVR-MAD: A Bayesian-Inspired Framework for Posterior-Guided Multi-Agent Debate
Weifan Jiang, Rana Shahout, Minghao Li +4
Multi-Agent Debate (MAD) improves LLM-agent accuracy but suffers from rapid context growth, limiting scalability in larger multi-agent settings. Existing methods prune low-utility…
Orla: A Library for Serving LLM-Based Multi-Agent Systems
Rana Shahout, Hayder Tirmazi, Minlan Yu +1
We introduce Orla, a library for constructing and running LLM-based agentic systems. Modern agentic applications consist of workflows that combine multiple LLM inference steps, too…
DynamiQ: Accelerating Gradient Synchronization using Compressed Multi-hop All-reduce
Wenchen Han, Shay Vargaftik, Michael Mitzenmacher +1
Multi-hop all-reduce is the de facto backbone of large model training. As the training scale increases, the network often becomes a bottleneck, motivating the reduction of the volu…