# Probabilistic Sensing: Intelligence in Data Sampling

arXiv: https://arxiv.org/abs/2601.19953 · HTML: https://mlsanity.com/paper/2601.19953/

## Key facts

- No citing papers in the map yet (data through 14 Oct 2026).
- Builds on 1 of its 35 references that are in the map.
- First posted on arXiv 27 Jan 2026.

## Builds on (most cited first, 1 of 1)

- [Adaptive Variation-Resilient Random Number Generator for Embedded Encryption](https://mlsanity.com/paper/2507.05523/) (2025)

## Abstract (arXiv)

Extending the intelligence of sensors to the data-acquisition process - deciding whether to sample or not - can result in transformative energy-efficiency gains. However, making such a decision in a deterministic manner involves risk of losing information. Here we present a sensing paradigm that enables making such a decision in a probabilistic manner. The paradigm takes inspiration from the autonomous nervous system and employs a probabilistic neuron (p-neuron) driven by an analog feature extraction circuit. The response time of the system is on the order of microseconds, over-coming the sub-sampling-rate response time limit and enabling real-time intelligent autonomous activation of data-sampling. Validation experiments on active seismic survey data demonstrate lossless probabilistic data acquisition, with a normalized mean squared error of 0.41%, and 93% saving in the active operation time of the system and the number of generated samples.