# The Silence that Speaks: Neural Estimation via Communication Gaps

arXiv: https://arxiv.org/abs/2512.01056 · HTML: https://mlsanity.com/paper/2512.01056/

## Key facts

- No citing papers in the map yet (data through 14 Oct 2026).
- Builds on 13 of its 28 references that are in the map.
- First posted on arXiv 30 Nov 2025.

## Builds on (most cited first, 13 of 13)

- [Proximal Policy Optimization Algorithms](https://mlsanity.com/paper/1707.06347/) (2017)
- [Trust Region Policy Optimization](https://mlsanity.com/paper/1502.05477/) (2015)
- [Neural Lyapunov Control](https://mlsanity.com/paper/2005.00611/) (2020)
- [Neural Lyapunov Control of Unknown Nonlinear Systems with Stability Guarantees](https://mlsanity.com/paper/2206.01913/) (2022)
- [Neural Lyapunov Control for Discrete-Time Systems](https://mlsanity.com/paper/2305.06547/) (2023)
- [Value of Information in Feedback Control: Quantification](https://mlsanity.com/paper/1812.07534/) (2018)
- [Bayesian Intention for Enhanced Human Robot Collaboration](https://mlsanity.com/paper/2410.00302/) (2024)
- [Value of Information in Feedback Control: Global Optimality](https://mlsanity.com/paper/2103.14012/) (2021)
- [Communication-Control Co-design in Wireless Edge Industrial Systems](https://mlsanity.com/paper/2202.03976/) (2022)
- [QSID-MPC: Model Predictive Control with System Identification from Quantized Data](https://mlsanity.com/paper/2503.19102/) (2025)
- [InterQ: A DQN Framework for Optimal Intermittent Control](https://mlsanity.com/paper/2504.09035/) (2025)
- [Infinite-horizon optimal scheduling for feedback control](https://mlsanity.com/paper/2402.08819/) (2024)
- [Current trends and future directions in event-based control](https://mlsanity.com/paper/2505.22378/) (2025)

## Abstract (arXiv)

Accurate remote state estimation is a fundamental component of many autonomous and networked dynamical systems, where multiple decision-making agents interact and communicate over shared, bandwidth-constrained channels. These communication constraints introduce an additional layer of complexity, namely, the decision of when to communicate. This results in a fundamental trade-off between estimation accuracy and communication resource usage. Traditional extensions of classical estimation algorithms (e.g., the Kalman filter) treat the absence of communication as 'missing' information. However, silence itself can carry implicit information about the system's state, which, if properly interpreted, can enhance the estimation quality even in the absence of explicit communication. Leveraging this implicit structure, however, poses significant analytical challenges, even in relatively simple systems. In this paper, we propose CALM (Communication-Aware Learning and Monitoring), a novel learning-based framework that jointly addresses the dual challenges of communication scheduling and estimator design. Our approach entails learning not only when to communicate but also how to infer useful information from periods of communication silence. We perform comparative case studies on multiple benchmarks to demonstrate that CALM is able to decode the implicit coordination between the estimator and the scheduler to extract information from the instances of 'silence' and enhance the estimation accuracy.