# Effective Communication: When to Pull Updates?

arXiv: https://arxiv.org/abs/2311.06432 · HTML: https://mlsanity.com/paper/2311.06432/

## Key facts

- Cited by 4 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 10 Nov 2023.

## Built on by (most cited first, 4 of 4)

- [Integrated Push-and-Pull Update Model for Goal-Oriented Effective Communication](https://mlsanity.com/paper/2407.14092/) (2024)
- [Pull-Based Query Scheduling for Goal-Oriented Semantic Communication](https://mlsanity.com/paper/2503.06725/) (2025)
- [Timeliness-Fidelity Tradeoff in 3D Scene Representations](https://mlsanity.com/paper/2407.16575/) (2024)
- [Task-Oriented Communications for 3D Scene Representation: Balancing Timeliness and Fidelity](https://mlsanity.com/paper/2509.17282/) (2025)

## Abstract (arXiv)

We study a pull-based communication system where a sensing agent updates an actuation agent using a query control policy, which is adjusted in the evolution of an observed information source and the usefulness of each update for achieving a specific goal. For that, a controller decides whether to pull an update at each slot, predicting what is probably occurring at the source and how much effective impact that update could have at the endpoint. Thus, temporal changes in the source evolution could modify the query arrivals so as to capture important updates. The amount of impact is determined by a grade of effectiveness (GoE) metric, which incorporates both freshness and usefulness attributes of the communicated updates. Applying an iterative algorithm, we derive query decisions that maximize the long-term average GoE for the communicated packets, subject to cost constraints. Our analytical and numerical results show that the proposed query policy exhibits higher effectiveness than existing periodic and probabilistic query policies for a wide range of query arrival rates.