## 1 Introduction
Some authors have also combined FBL with various communication technologies, such as non-orthogonal multiple access and retransmission, to analyze the impact on system performance [18; 19; 20; 21]. The authors in [18] provide a design that maximizes the effective capacity via FBL transmission. In [19], a joint optimization problem is formulated by determining the user grouping and allocating blocklength. In [20], the authors propose energy-efficient retransmission schemes for MEC networks with a hybrid automatic repeat request, optimizing the number of retries and transmission parameters under FBL constraints to minimize energy consumption for latency-critical tasks. In [21], the authors allocate blocklength to improve the average end-to-end reliability through a deep reinforcement learning approach. However, existing studies often focus on the transmission delay under the FBL regime. They tend to overlook the queuing delay, especially when tasks involve multiple related data packets. Since transmission delay does not fully capture the total time taken for transmitting packets, especially ignoring the waiting time of packets at the source, some researchers have introduced the Age of Information (AoI) [22]. The AoI concept, which includes the waiting time of packets at the source, offers a more accurate assessment of packet freshness. Recently, a set of studies have analyzed the AoI performance of networks operating with FBL codes. The authors in [23; 24] present a novel study for downlink cellular networks and orthogonal frequency division multiple access systems to minimize AoI, considering the impact of FBL. In [25], authors optimize the tail distribution of AoI in vehicular networks by employing extreme value theory. The work in [26] investigates the correlation and joint optimization of AoI and peak AoI in a last-come first-served system with retransmission and non-preemption policies. Some other recent studies have analyzed the AoI within the MEC networks. In [27], the authors investigate the average AoI in a wireless-powered MEC system and derive a closed-form expression for it. In [28], the problem of minimizing AoI in an MEC system for computation-intensive status updates is addressed with the no-wait policy and interval-wait policy. The authors of [29] present an AoI-based optimization strategy for computation offloading and transmission scheduling in MEC-enabled IoT networks. In [30], the authors analyze the average AoI and the average peak AoI in a multi-user MEC system, where a base station transmits computation-intensive packetsto UEs. However, in some scenarios, data packets are used for decision-making after transmission. The freshness of the data at the time of decision is more critical than at the moment transmission is completed. The AoI does not capture the packets' freshness for decision-making purposes. More recently, the concept of Age upon Decision (AuD) has been introduced in [31]. The AuD measures how old the information is when it is used to make a decision, whereas AoI measures the information received. The authors investigate the impact of scheduling update arrival and decision-making processes on the average AuD in [32; 33]. Then, the authors of [34] explored the AuD in IoT-based wireless networks with truncated hybrid automatic repeat requests, focusing on the impact of FBL and multiple sources. Although much of the research has looked at the AuD for data packets, the task AuD that involves multiple data packets still requires further investigation. To the best of our knowledge, there is also a missing analysis and optimization of a task's AuD with multiple related data packets in low-latency MEC networks under the FBL regime. In this paper, we consider task transmission and computation in a low-latency MEC network with multiple UEs and one MEC edge server. To minimize the average task AuD, we allocate finite blocklength resources to UEs and decide the transmission moment.