Notice: This website is continuously updated as new conference proceedings and journal volumes are published. Consequently, the figures presented here may differ from those reported in our publications.
A brief overview of our methodology is provided on the Methods page, along with references to publications documenting our approach and links to the code and data used in each study.
The reproducibility variables underlying each score are classified using an automated LLM-based pipeline, validated against a manually labeled dataset. LLM-based classification introduces uncertainty and potential bias; scores should be interpreted as estimates.
Revolutionizing Training-Free NAS: Towards Efficient Automatic Proxy Discovery via Large Language Models
Authors: Haidong Kang, Lihong Lin, Hanling Wang
NeurIPS 2025 | Venue PDF | LLM Run Details | Input Tokens: 40,858 Total number of tokens sent to the LLM as input for this paper's analysis. | Output Tokens: 4,737 Total number of tokens produced by the LLM (including reasoning/thinking tokens) for this paper's analysis.
| Reproducibility Variable | Result | LLM Response |
|---|---|---|
| Research Type | Experimental | We conduct extensive experiments on mainstream NAS benchmarks, demonstrating APD excels in both performance and efficiency. Besides, we firmly believe that our APD will dramatically benefit the deep learning community through providing novel paradigm of design algorithms via LLMs. |
| Researcher Affiliation | Academia | 1Northeastern University, China 2Pengcheng Laboratory, China |
| Pseudocode | Yes | Algorithm 1 Evolution Framework |
| Open Source Code | Yes | Question: Does the paper provide open access to the data and code, with sufficient instructions to faithfully reproduce the main experimental results, as described in supplemental material? Answer: [Yes] Justification: We provided runnable code in the supplementary material. |
| Open Datasets | Yes | We evaluate APD on 5 representative search spaces (e.g., NAS-Bench-201 Dong and Yang [2020], NAS-Bench-101 Ying et al. [2019], DARTS Liu et al. [2018], Trans NAS-Bench-101 Micro Duan et al. [2021], Oo D-Vi T-NAS Bai et al. [2021]) across 4 tasks: image recognition, autoencoding, scene classification, and self-supervised jigsaw puzzles. |
| Dataset Splits | Yes | We evaluate APD on 5 representative search spaces (e.g., NAS-Bench-201 Dong and Yang [2020], NAS-Bench-101 Ying et al. [2019], DARTS Liu et al. [2018], Trans NAS-Bench-101 Micro Duan et al. [2021], Oo D-Vi T-NAS Bai et al. [2021]) across 4 tasks: image recognition, autoencoding, scene classification, and self-supervised jigsaw puzzles. We transfer the zero-cost proxy identified on CIFAR-10 within the NAS-Bench-201 search space to all datasets in both NAS-Bench-201 and NAS-Bench-101. |
| Hardware Specification | Yes | Searches are conducted on a single RTX4090 GPU with a fixed random seed of 0. |
| Software Dependencies | No | Output: For each proxy, provide a Description paragraph and Code demo implementing evaluate(model, inputs, targets) in Py Torch. |
| Experiment Setup | Yes | Table 8: Hyper-parameters for Automatic Proxy Discovery. Parameter Value Episodes 10 Steps 100 History windows 5 Discount factor 0.9 Actor learning rate 1e-3 Critic learning rate 1e-2 Input batch size 16 Repeats 5 Hidden size 256 Number of layers 2 Population size 5 β 1 |