A Multi-Agent LLM System for Automated Tuning of Specialized Neural Network Models
cs.AI, cs.CL, cs.LG, cs.MA, cs.NE
Submitted: 2026-10-09
Updated: 2026-10-09
Code: https://github.com/sag111/llm-auto-tuning-results
License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
Sources
- MLE-bench: Evaluating Machine Learning Agents on Machine Learning Engineering
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- Large Language Model based Multi-Agents: A Survey of Progress and Challenges
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- Understanding the planning of LLM agents: A survey
- Neural Architecture Search for Spiking Neural Networks
- The AI Scientist: Towards Fully Automated Open-Ended Scientific Discovery
- AutoSNN: Towards Energy-Efficient Spiking Neural Networks
- Agent Laboratory: Using LLM Agents as Research Assistants
- Reflexion: Language Agents with Verbal Reinforcement Learning
- Accelerated Inorganic Materials Design with Generative AI Agents
- Voyager: An Open-Ended Embodied Agent with Large Language Models
- Chain-of-Thought Prompting Elicits Reasoning in Large Language Models
- The Rise and Potential of Large Language Model Based Agents: A Survey
- The AI Scientist-v2: Workshop-Level Automated Scientific Discovery via Agentic Tree Search
- R&D-Agent: An LLM-Agent Framework Towards Autonomous Data Science
- Tree of Thoughts: Deliberate Problem Solving with Large Language Models
- ReAct: Synergizing Reasoning and Acting in Language Models
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