ArchPilot: A Proxy-Guided Multi-Agent Approach for Machine Learning Engineering

• URL: http://arxiv.org/abs/2511.03985v1
• Date: Thu, 06 Nov 2025 02:14:59 GMT
• Title: ArchPilot: A Proxy-Guided Multi-Agent Approach for Machine Learning Engineering
• Abstract summary: ArchPilot is a multi-agent system that integrates architecture generation, proxy-based evaluation, and adaptive search into a unified framework.<n>It prioritizes high-potential candidates with minimal reliance on expensive full training runs, facilitating efficient ML engineering under limited budgets.
• Score: 18.83090180326138
• License: http://creativecommons.org/licenses/by/4.0/
• Abstract: Recent LLM-based agents have demonstrated strong capabilities in automated ML engineering. However, they heavily rely on repeated full training runs to evaluate candidate solutions, resulting in significant computational overhead, limited scalability to large search spaces, and slow iteration cycles. To address these challenges, we introduce ArchPilot, a multi-agent system that integrates architecture generation, proxy-based evaluation, and adaptive search into a unified framework. ArchPilot consists of three specialized agents: an orchestration agent that coordinates the search process using a Monte Carlo Tree Search (MCTS)-inspired novel algorithm with a restart mechanism and manages memory of previous candidates; a generation agent that iteratively generates, improves, and debugs candidate architectures; and an evaluation agent that executes proxy training runs, generates and optimizes proxy functions, and aggregates the proxy scores into a fidelity-aware performance metric. This multi-agent collaboration allows ArchPilot to prioritize high-potential candidates with minimal reliance on expensive full training runs, facilitating efficient ML engineering under limited budgets. Experiments on MLE-Bench demonstrate that ArchPilot outperforms SOTA baselines such as AIDE and ML-Master, validating the effectiveness of our multi-agent system.

Related papers

• Multi-Agent Orchestration for High-Throughput Materials Screening on a Leadership-Class System [1.4669344809969722]
  Large language models (LLMs) play a critical role in autonomous decision-making.<n>We present a scalable, hierarchical multi-agent framework for orchestrating high- throughput screening campaigns.<n>Results demonstrate that the proposed agentic framework enables efficient and scalable execution on the Aurora supercomputer.
  arXiv  Detail & Related papers  (2026-04-09T01:01:11Z)
• AgentArk: Distilling Multi-Agent Intelligence into a Single LLM Agent [57.10083973844841]
  AgentArk is a novel framework to distill multi-agent dynamics into the weights of a single model.<n>We investigate three hierarchical distillation strategies across various models, tasks, scaling, and scenarios.<n>By shifting the burden of computation from inference to training, the distilled models preserve the efficiency of one agent while exhibiting strong reasoning and self-correction performance of multiple agents.
  arXiv  Detail & Related papers  (2026-02-03T19:18:28Z)
• ComAgent: Multi-LLM based Agentic AI Empowered Intelligent Wireless Networks [62.031889234230725]
  6G networks rely on complex cross-layer optimization.<n> manually translating high-level intents into mathematical formulations remains a bottleneck.<n>We present ComAgent, a multi-LLM agentic AI framework.
  arXiv  Detail & Related papers  (2026-01-27T13:43:59Z)
• Multi-Agent Deep Research: Training Multi-Agent Systems with M-GRPO [24.532870400949424]
  Current training methods train a unified large language model for all agents in the system.<n>This may limit the performances due to different underlying distributions for different agents.<n>We propose M-GRPO, a hierarchical extension of Group Relative Policy Optimization for vertical Multi-agent systems.
  arXiv  Detail & Related papers  (2025-11-17T12:06:30Z)
• AgentRouter: A Knowledge-Graph-Guided LLM Router for Collaborative Multi-Agent Question Answering [51.07491603393163]
  tAgent is a framework that formulates multi-agent QA as a knowledge-graph-guided routing problem supervised by empirical performance signals.<n>By leveraging soft supervision and weighted aggregation of agent outputs, Agent learns principled collaboration schemes that capture the complementary strengths of diverse agents.
  arXiv  Detail & Related papers  (2025-10-06T23:20:49Z)
• Multi-Agent Tool-Integrated Policy Optimization [67.12841355267678]
  Large language models (LLMs) increasingly rely on multi-turn tool-integrated planning for knowledge-intensive and complex reasoning tasks.<n>Existing implementations typically rely on a single agent, but they suffer from limited context length and noisy tool responses.<n>No existing methods support effective reinforcement learning post-training of tool-integrated multi-agent frameworks.
  arXiv  Detail & Related papers  (2025-10-06T10:44:04Z)
• InfiAgent: Self-Evolving Pyramid Agent Framework for Infinite Scenarios [28.65914611521654]
  InfiAgent is a Pyramid-like DAG-based Multi-Agent Framework that can be applied to textbfinfinite scenarios.<n>InfiAgent achieves 9.9% higher performance compared to ADAS (similar auto-generated agent framework)
  arXiv  Detail & Related papers  (2025-09-26T15:44:09Z)
• AgentOrchestra: Orchestrating Hierarchical Multi-Agent Intelligence with the Tool-Environment-Agent(TEA) Protocol [22.406849007798858]
  We propose the Tool-Environment-Agent Protocol to integrate environments, agents, and tools into an unified system.<n>We introduce AgentOrchestra, a hierarchical multi-agent framework with a central planning agent that decomposes complex objectives and coordinates specialized agents.
  arXiv  Detail & Related papers  (2025-06-14T13:45:37Z)
• AgentSwift: Efficient LLM Agent Design via Value-guided Hierarchical Search [58.98450205734779]
  Large language model (LLM) agents have demonstrated strong capabilities across diverse domains.<n>Existing agent search methods suffer from three major limitations.<n>We introduce a comprehensive framework to address these challenges.
  arXiv  Detail & Related papers  (2025-06-06T12:07:23Z)
• R&D-Agent: An LLM-Agent Framework Towards Autonomous Data Science [70.1638335489284]
  High-level machine learning engineering tasks remain labor-intensive and iterative.<n>We introduce R&D-Agent, a comprehensive, decoupled, and framework that formalizes the machine learning process.<n>R&D-Agent defines the MLE into two phases and six components, turning agent design for MLE into a principled, testable process.
  arXiv  Detail & Related papers  (2025-05-20T06:07:00Z)
• A Dynamic LLM-Powered Agent Network for Task-Oriented Agent Collaboration [55.35849138235116]
  We propose automatically selecting a team of agents from candidates to collaborate in a dynamic communication structure toward different tasks and domains. Specifically, we build a framework named Dynamic LLM-Powered Agent Network ($textDyLAN$) for LLM-powered agent collaboration. We demonstrate that DyLAN outperforms strong baselines in code generation, decision-making, general reasoning, and arithmetic reasoning tasks with moderate computational cost.
  arXiv  Detail & Related papers  (2023-10-03T16:05:48Z)
• Multi-Agent Reinforcement Learning for Microprocessor Design Space Exploration [71.95914457415624]
  Microprocessor architects are increasingly resorting to domain-specific customization in the quest for high-performance and energy-efficiency. We propose an alternative formulation that leverages Multi-Agent RL (MARL) to tackle this problem. Our evaluation shows that the MARL formulation consistently outperforms single-agent RL baselines.
  arXiv  Detail & Related papers  (2022-11-29T17:10:24Z)

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.