The Case for Synthesized Data Compression

2026-10-07

AI Summary

This study addresses the inherent limitation of general-purpose compression algorithms in achieving optimal performance on specific datasets. To overcome this, we introduce a pioneering data-level customized compression paradigm that leverages Agentic Coding agents to automatically synthesize dedicated compression algorithms tailored to individual datasets. By building upon modern file formats such as AnyBlox and F3, the proposed framework supports comprehensive verification during the construction phase, thereby eliminating potential correctness risks. This approach enables targeted optimization, nearly doubling the compression factor compared to general-purpose methods and significantly extending the Pareto frontier.

Abstract

Compression is a fundamental tool in data management. Today's compression schemes are generally designed by hand to apply to many different types of data (e.g., run length encoding or arithmetic encoding), leading to a zoo of general-purpose algorithms which work well for many datasets, but may not be optimal for any specific dataset. Inspired by recent advancements in agentic coding, we propose synthesizing custom-tailored data compression algorithms on a per-dataset basis. Unlike prior work using agents to synthesize database components, a data-specific compression algorithm can be fully verified at construction time, alleviating most correctness concerns. Modern file formats, like AnyBlox and F3, allow including the synthesized code alongside the data itself. Experimentally, we show that our synthesis agent can expand the compression-factor/decompression-speed Pareto front, outperforming general-purpose methods by nearly $2\times$ in compression factor.

Problem

• data compression
• synthesized data
• general-purpose algorithms
• dataset-specific optimization

Innovation

• Synthesized Data Compression
• Agentic Coding
• Per-dataset Customization
• Pareto Front
• Correctness Verification