# An Optimal, Universal and Agnostic Decoding Method for Message Reconstruction, Bio and Technosignature Detection

arXiv: https://arxiv.org/abs/2303.16045 · HTML: https://mlsanity.com/paper/2303.16045/

## Key facts

- Cited by 2 papers in the map (data through 14 Oct 2026).
- Builds on 6 of its 63 references that are in the map.
- First posted on arXiv 28 Mar 2023.

## Builds on (most cited first, 6 of 6)

- [Emergence and algorithmic information dynamics of systems and observers](https://mlsanity.com/paper/2105.14707/) (2021)
- [Minimal Algorithmic Information Loss Methods for Dimension Reduction, Feature Selection and Network Sparsification](https://mlsanity.com/paper/1802.05843/) (2018)
- [Algorithmic Complexity and Reprogrammability of Chemical Structure Networks](https://mlsanity.com/paper/1802.05856/) (2018)
- [An Algorithmic Information Distortion in Multidimensional Networks](https://mlsanity.com/paper/2009.05879/) (2020)
- [The Case for Technosignatures: Why They May Be Abundant, Long-lived, Highly Detectable, and Unambiguous](https://mlsanity.com/paper/2203.10899/) (2022)
- [Computable Model Discovery and High-Level-Programming Approximations to Algorithmic Complexity](https://mlsanity.com/paper/2112.12197/) (2021)

## Built on by (most cited first, 2 of 2)

- [Local Compositional Complexity: How to Detect a Human-readable Messsage](https://mlsanity.com/paper/2501.03664/) (2025)
- [Non-Random Data Encodes its Geometric and Topological Dimensions](https://mlsanity.com/paper/2405.07803/) (2024)

## Abstract (arXiv)

We present an agnostic signal reconstruction method for zero-knowledge one-way communication channels in which a receiver aims to interpret a message sent by an unknown source about which no prior knowledge is available and to which no return message can be sent. Our reconstruction method is agnostic vis-`a-vis the arbitrarily chosen encoding-decoding scheme and other observer-dependent characteristics, such as the arbitrarily chosen computational model, probability distributions, or underlying mathematical theory. We investigate how non-random messages encode information about their intended physical properties, such as dimension and length scales of the space in which a signal or message may have been originally encoded, embedded, or generated. We focus on image data as a first illustration of the capabilities of the new method. We argue that our results have applications to life and technosignature detection, and to coding theory in general.