Abstract
Task-Aware Quantization uses hidden representations and output sensitivity to allocate mixed precision across transformer layers under a fixed bit budget. Calibration uses a small set of task prompts, with no weight training, and the study measures accuracy, memory use, throughput, and latency.
Type
Publication
ICML 2026 Workshop on AdaptFM: Resource-Adaptive Foundation Model Inference
The workshop version is identified in the arXiv record. The original preprint was released in November 2025; the latest revision was submitted in June 2026.
Ravid Shwartz-Ziv
AI Researcher
AI researcher at Meta MSL with a background in information theory and computational neuroscience, working on world models, memory, and compression. Former Assistant Professor and Faculty Fellow at NYU, collaborating on research across academia and industry.