# Lossless Compression via Chained Lightweight Neural Predictors with Information Inheritance

arXiv: https://arxiv.org/abs/2604.15472 · HTML: https://mlsanity.com/paper/2604.15472/

## Key facts

- No citing papers in the map yet (data through 14 Oct 2026).
- Builds on 9 of its 30 references that are in the map.
- First posted on arXiv 16 Apr 2026.

## Builds on (most cited first, 9 of 9)

- [Attention Is All You Need](https://mlsanity.com/paper/1706.03762/) (2017)
- [Language Modeling Is Compression](https://mlsanity.com/paper/2309.10668/) (2023)
- [C3: High-performance and low-complexity neural compression from a single image or video](https://mlsanity.com/paper/2312.02753/) (2023)
- [COOL-CHIC: Coordinate-based Low Complexity Hierarchical Image Codec](https://mlsanity.com/paper/2212.05458/) (2022)
- [A Fast Transformer-based General-Purpose Lossless Compressor](https://mlsanity.com/paper/2203.16114/) (2022)
- [Zipf's law in 50 languages: its structural pattern, linguistic interpretation, and cognitive motivation](https://mlsanity.com/paper/1807.01855/) (2018)
- [DZip: improved general-purpose lossless compression based on novel neural network modeling](https://mlsanity.com/paper/1911.03572/) (2019)
- [ExCP: Extreme LLM Checkpoint Compression via Weight-Momentum Joint Shrinking](https://mlsanity.com/paper/2406.11257/) (2024)
- [EDPC: Accelerating Lossless Compression via Lightweight Probability Models and Decoupled Parallel Dataflow](https://mlsanity.com/paper/2507.18969/) (2025)

## Abstract (arXiv)

This paper is dedicated to lossless data compression with probability estimation using neural networks. First, we propose a probability estimation architecture based on a chain of neural predictors, so that each unit of the chain is defined as a neural network with the minimum possible number of weights, which is sufficient for efficient compression of data generated by Markov sources of a given order. We show that this architecture allows us to minimize the overall number of weights participating in the probability estimation process depending on the statistical properties of the input data. Second, in order to improve compression efficiency, we introduce an information inheritance mechanism, where the probability estimate obtained by a low-order unit is used at the next higher-order unit. Experimental results show that the proposed lossless data compressor equipped with the chained probability estimation architecture provides compression ratios close to the state-of-the-art PAC compressor. At the same time, it outperforms PAC by a factor of 1.2 to 6.3 in encoding throughput and by a factor of 2.8 to 12.3 in decoding throughput on a consumer GPU.