Consensus-gated Multi-Agent Neural Architecture Search for Seismic Fault Segmentation

• URL: http://arxiv.org/abs/2608.13889v1
• Date: Fri, 14 Aug 2026 02:41:37 GMT
• Title: Consensus-gated Multi-Agent Neural Architecture Search for Seismic Fault Segmentation
• Abstract summary: Neural networks for seismic fault segmentation are often borrowed from computer vision and medical imaging domains.<n>Classical neural architecture search (NAS) is restricted to hand-crafted search spaces and large compute budgets.<n>We present an agentic NAS system in which a panel of three large language models (Claude, GPT-5.1, and Gemini2.5Pro) debates each candidate architecture to unanimous consensus.
• Score: 0.17188280334580194
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: Neural networks for seismic fault segmentation are often borrowed from computer vision and medical imaging domains where they train under relatively much larger labeled data resources. Optimizing their architecture under tight labeled data budgets as are common in geophysical applications is not a trivial problem. Manually designing data-optimal architectures is time-consuming while classical neural architecture search (NAS) is restricted to hand-crafted search spaces and large compute budgets. We present an agentic NAS system in which a panel of three large language models (Claude, GPT-5.1, and Gemini~2.5~Pro) debates each candidate architecture to unanimous consensus, authors the complete PyTorch implementation, cross-reviews it, and submits it to an automated validate-train-score loop with a hard 450K parameter budget, keep-or-revert lineage, and a memory of failed mechanisms. Operating on source code rather than a predefined operation menu, the search ran on a single consumer GPU and trained only eight candidates. It discovered \ours{}: a 425K-parameter encoder-decoder with a strip-pooling bottleneck, squeeze-and-excitation gating, an asymmetric one-conv decoder, and a feature-pyramid fusion neck. Trained under a protocol identical to all baselines on sections derived from the Thebe fault dataset, it attains the highest F1 (0.578) and IoU of all models tested while being the smallest, outperforming a published-capacity U-Net (31M parameters, F1 0.484), DeepLabV3-ResNet50 (39.6M, 0.516), an Attention U-Net(1.83M, 0.502). The search cost 101 LLM calls ($\sim$1.15M input / 0.39M output tokens) and roughly one GPU-day, making consensus-gated LLM panels a practical, low-cost route to domain-specific architecture discovery.

Related papers

• Transformer-Guided Swarm Intelligence for Frugal Neural Architecture Search [0.0]
  We propose a frugal and memetic Neural Architecture Search framework to democratize architecture design on consumer-grade hardware.<n>Our approach combines the global macro-search capabilities of an autoregressive Transformer controller, trained via Reinforcement Learning (RL), with the local micro-exploitation of an Artificial Bee Colony (ABC) algorithm.<n>We show that our framework can yield tailored, accessible, and highly parameter-efficient deep learning models suitable for edge deployment.
  arXiv  Detail & Related papers  (2026-07-13T17:18:24Z)
• Omni-SimpleMem: Autoresearch-Guided Discovery of Lifelong Multimodal Agent Memory [76.63021613850093]
  We deploy an autonomous research pipeline to discover Omni-SimpleMem, a unified multimodal memory framework for lifelong AI agents.<n>System achieves state-of-the-art on two benchmarks, improving F1 by +411% on LoCoMo and +214% on Mem-Gallery.<n>We provide a taxonomy of six discovery types and identify four properties that make multimodal memory particularly suited for autoresearch.
  arXiv  Detail & Related papers  (2026-04-01T15:06:23Z)
• Resource-Efficient Iterative LLM-Based NAS with Feedback Memory [49.44875022114861]
  Neural Architecture Search (NAS) automates network design, but conventional methods demand substantial computational resources.<n>We propose a closed-loop pipeline leveraging large language models (LLMs) to iteratively generate, evaluate, and refine convolutional neural network architectures.
  arXiv  Detail & Related papers  (2026-03-12T16:00:22Z)
• Regression Language Models for Code [13.53022663049873]
  We show that a single unified Regression Language Model (RLM) can simultaneously predict directly from text.<n>We show that the RLM can obtain the highest average Kendall-Tau of 0.46 on five classic NAS design spaces.
  arXiv  Detail & Related papers  (2025-09-30T16:25:23Z)
• DNA Family: Boosting Weight-Sharing NAS with Block-Wise Supervisions [121.05720140641189]
  We develop a family of models with the distilling neural architecture (DNA) techniques. Our proposed DNA models can rate all architecture candidates, as opposed to previous works that can only access a sub- search space using algorithms. Our models achieve state-of-the-art top-1 accuracy of 78.9% and 83.6% on ImageNet for a mobile convolutional network and a small vision transformer, respectively.
  arXiv  Detail & Related papers  (2024-03-02T22:16:47Z)
• EAutoDet: Efficient Architecture Search for Object Detection [110.99532343155073]
  EAutoDet framework can discover practical backbone and FPN architectures for object detection in 1.4 GPU-days. We propose a kernel reusing technique by sharing the weights of candidate operations on one edge and consolidating them into one convolution. In particular, the discovered architectures surpass state-of-the-art object detection NAS methods and achieve 40.1 mAP with 120 FPS and 49.2 mAP with 41.3 FPS on COCO test-dev set.
  arXiv  Detail & Related papers  (2022-03-21T05:56:12Z)
• OPANAS: One-Shot Path Aggregation Network Architecture Search for Object Detection [82.04372532783931]
  Recently, neural architecture search (NAS) has been exploited to design feature pyramid networks (FPNs) We propose a novel One-Shot Path Aggregation Network Architecture Search (OPANAS) algorithm, which significantly improves both searching efficiency and detection accuracy.
  arXiv  Detail & Related papers  (2021-03-08T01:48:53Z)
• Fine-Grained Stochastic Architecture Search [6.277767522867666]
  Fine-Grained Architecture Search (FiGS) is a differentiable search method that searches over a much larger set of candidate architectures. FiGS simultaneously selects and modifies operators in the search space by applying a structured sparse regularization penalty. We show results across 3 existing search spaces, matching or outperforming the original search algorithms.
  arXiv  Detail & Related papers  (2020-06-17T01:04:14Z)
• DC-NAS: Divide-and-Conquer Neural Architecture Search [108.57785531758076]
  We present a divide-and-conquer (DC) approach to effectively and efficiently search deep neural architectures. We achieve a $75.1%$ top-1 accuracy on the ImageNet dataset, which is higher than that of state-of-the-art methods using the same search space.
  arXiv  Detail & Related papers  (2020-05-29T09:02:16Z)
• A CNN-LSTM Quantifier for Single Access Point CSI Indoor Localization [9.601632184687787]
  This paper proposes a combined network structure between convolutional neural network (CNN) and long-short term memory (LSTM) quantifier for WiFi fingerprinting indoor localization. Using only a single WiFi router, our structure achieves an average localization error of 2.5m with $mathrm80%$ of the errors under 4m.
  arXiv  Detail & Related papers  (2020-05-13T16:54:31Z)

This list is automatically generated from the titles and abstracts of the papers in this site.

This site does not guarantee the quality of this site (including all information) and is not responsible for any consequences.