FLORE achieves 1000x error reduction in linear sketching while being 100x faster than previous learning-based solutions.
March 17, 2026
Original Paper
On the (Generative) Linear Sketching Problem
arXiv · 2603.14474
The Takeaway
It solves the long-standing sketching dilemma of information loss by using generative priors that can be trained without ground-truth data. For practitioners dealing with high-velocity data streams, this provides a near-perfect recovery method with negligible computational overhead.
From the abstract
Sketch techniques have been extensively studied in recent years and are especially well-suited to data streaming scenarios, where the sketch summary is updated quickly and compactly. However, it is challenging to recover the current state from these summaries in a way that is accurate, fast, and real. In this paper, we seek a solution that reconciles this tension, aiming for near-perfect recovery with lightweight computational procedures. Focusing on linear sketching problems of the form $\bolds