13 papers
LGNNIC: Acceleration of Large-Scale GNN Training using SmartNICs
Liad Gerstman, Aditya Dhakal, Dejan Milojicic +1
Graph Neural Networks (GNNs) are widely used across domains such as natural sciences, social network analysis, chip design, and recommendation systems. However, as graph sizes grow…
Linguistic Firewall: Geometry as Defense in Multi-Agent Systems Routing
Dvir Alsheich, Adar Peleg, Ben Hagag +3
The rapid integration of Large Language Models (LLMs) has driven the evolution of Multi-Agent Systems (MAS), where specialized agents collaborate to execute complex workflows. Effe…
You Had One Job: Per-Task Quantization Using LLMs' Hidden Representations
Amit LeVi, Raz Lapid, Rom Himelstein +3
Many LLM applications require only narrow capabilities, yet standard post-training quantization (PTQ) methods allocate precision without considering the target task. This can waste…
Breaking the Bubble: Asynchronous Pipeline Parallel Training with Bounded Weight Inconsistency
Itay Elam, Eliron Rahimi, Avi Mendelson +1
Pipeline parallelism is essential for training large neural networks, but existing schedules trade off throughput, memory, and optimization consistency. Synchronous pipelines prese…
Extracting Recurring Vulnerabilities from Black-Box LLM-Generated Software
Tomer Kordonsky, Amit LeVi, Maayan Yamin +2
LLMs are increasingly used for code generation, but their outputs often follow recurring templates that can induce predictable vulnerabilities. We study vulnerability persistence i…
Step-Wise Refusal Dynamics in Autoregressive and Diffusion Language Models
Eliron Rahimi, Elad Hirshel, Rom Himelstein +3
Diffusion language models (DLMs) have recently emerged as a competitive alternative to autoregressive (AR) models, offering parallel decoding, competitive generation quality, and i…