The paper initially received mixed ratings, but the reviewers were convinced by the rebuttal. Overall, their assessment is positive; the area chair agrees with their assessment and recommends an accept.

In Distributed Manipulator Systems (DMS), decentralization is a highly desirable property as it promotes robustness and facilitates scalability by distributing computational burden and eliminating singular points of failure. However, current DMS typically utilize a centralized approach to sensing, such as single-camera computer vision systems. This centralization poses a risk to system reliability and offers a significant limiting factor to system size. In this work, we introduce a decentralized approach for sensing and in a Distributed Manipulator Systems using Neural Cellular Automata (NCA). Demonstrating a decentralized sensing in a hardware implementation, we present a novel inductive sensor board designed for distributed sensing and evaluate its ability to estimate global object properties, such as the geometric center, through local interactions and computations. Experiments demonstrate that NCA-based sensing networks accurately estimate object position at 0.24 times the inter sensor distance. They maintain resilience under sensor faults and noise, and scale seamlessly across varying network sizes. These findings underscore the potential of local, decentralized computations to enable scalable, fault-tolerant, and noise-resilient object property estimation in DMS

Weaknesses: The paper is not very novel or significant in its contribution. It compiles two regularization methods to mitigate two long-standing problems in differentiable NAS, however, the proposed methods are not very novel. NAS-Bench is not a very well established benchmark that not many people are very familiar with. It is not fair to compare with existing work on NAS-bench, as most of them were not optimized on NAS-Bench. For instance, the DARTS work may work equally well with proper hyperparameter tuning and regularization. With the existing DARTS hyperparmeters, search on NAS-bench converges to networks with only identity/skip operation.

The reviewers generally found this paper to be a good contribution to the NAO/NAS field, with a good motivation and strong results. There were concerns on the novelty of the work, but after considering the author's response, particularly in relation to EWC, I think the work is sufficiently novel, especially given the relatively new domain. I would encourage the authors to include the clarifications and comparison to related work from the rebuttal in the main paper. The biggest issue that still lingers is the fact that NAS-Bench-201 is a very small benchmark. The most positive reviewer strongly encourages the authors to apply their technique to a larger benchmark such as NAS-Bench-1Shot1.

This paper proposes a novel search space for neural architecture post-processing that seeks to reduce resource consumption of trained models without sacrificing performance. Following the author feedback, all reviewers scored this paper above the threshold. They also continue to highlight crucial improvements, that I hope the authors will address for the camera-ready version.

This paper proposes a strategy for an efficient deep network architecture search (here for image classification, but a the general idea would apply for other tasks as well). The proposed strategy is will motivated and involves a data sampling stage at each step. Here, an active querying strategy can be employed and the authors evaluate their strategy with three different active sampling strategies. They show that their strategy improves over active learning (with the same active query strategies) with a fixed architecture. However, the reviewers have rightly pointed out that a comparison with other architecture search strategies would also have been in place.

Weaknesses: Similar to this work, many recent NAS approaches perform architecture search in the continuous representation space, and gradient descent (GD) is one of the most commonly used approaches for architecture search in the continuous space. However, this work only considers RL and BO as the search algorithms. I am curious to know how competitive GD is compared to RL and BO. In Figure 4, I am not sure why the right plot has more blank space compared to the left plot. An explanation is needed to help the readers understand the insights behind those plots.

In the paper authors created an unsupervised learning method to embeds architectures in latent space and showed through experiments that the representations formed result in improved downstream performance compared to training with supervised objective jointly. The idea is important and the analysis is sound. The paper could be improved by analysing more diverse space of architectures than ResNet like blocks, as well as other suggestions given by the reviewers.

The authors showed results on a larger search space (with learnable stage ratios), which worked reasonable well (of course at the cost of much longer training time). While still some other design choices could be optimized, I do think this is an interesting and novel approach that could open up many future research and advance the field of NAS. Thus I think this paper should be accepted and I'm keeping my rating.

This paper initially got a borderline recommendation (6,5,7). The reviewers agree that this paper gives interesting findings and the idea is new -- it targets at how to generate search space. However, reviewers have some questions on the experiment results. The authors give good response and address these questions well. The ratings are increased to 7,7,6.