arXiv Machine Learning
22h ago
Agentic ESOpt: Fine-Tuning Long-Horizon LLM Agents with Minimal GPU Requirements
arXiv:2608. 17310v1 Announce Type: new Abstract: Reinforcement Learning (RL) has been promising in single-turn LLM fine-tuning.
Agentic Stage-One Stellarator Optimization: Autonomous Multi-Objective Search for Finite-Beta Equilibria
arXiv:2608. 01344v2 Announce Type: replace Abstract: Stage-one stellarator design searches a high-dimensional family of three-dimensional plasma boundaries and fixed-boundary MHD equilibria for configurations that jointly meet requirements on confinement, field-line topology, force balance, stability proxies, and geometry.
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arXiv AI
1d ago
GoalEvolve: From Handcrafted Algorithm Priors to Goal-Driven Evolution of Physical Design Algorithms
arXiv:2608. 16733v1 Announce Type: cross Abstract: Physical design algorithms operate within tightly coupled, multi-stage optimization flows, where stage-local gains may vanish or induce downstream degradation.
arXiv AI
Aug 5
Can LLM design high-quality experiments? A Comprehensive and Systematic Benchmark on Autonomous Experimental Design
arXiv:2608. 03501v1 Announce Type: new Abstract: AI for Research (AI4Research) leverages AI to automate and improve scientific workflows.
arXiv AI
Jul 29
Matryoshka Agent: Unfolding Sub-Agents for Long-Horizon Machine Learning Engineering
arXiv:2607. 25090v1 Announce Type: new Abstract: Machine learning engineering (MLE) tasks require long-horizon decision making over iterative solution debugging and refinement, under expensive and feedback-driven environment interactions.
Hugging Face Trending Papers
Aug 4
Can LLM design high-quality experiments? A Comprehensive and Systematic Benchmark on Autonomous Experimental Design
AI for Research (AI4Research) leverages AI to automate and improve scientific workflows. While experimental design is a critical stage of the research process, prior work has focused primarily on code implementation and execution, overlooking the importance of this stage, and no benchmark exists to evaluate AI's ability to conduct systematic experiment design.
arXiv AI
Aug 6
Improving Auto-Design of Neural PDE Solvers with a Domain-Specific Language
arXiv:2608. 04384v1 Announce Type: new Abstract: Neural PDE solver auto-design is fundamentally a search-space representation problem.
arXiv AI
Aug 11
SimulCost: A Cost-Aware Benchmark and Toolkit for Automating Physics Simulations with LLMs
arXiv:2603. 20253v3 Announce Type: replace-cross Abstract: Evaluating LLM agents for scientific tasks has focused on token costs while ignoring tool-use costs like simulation time and experimental resources.
arXiv AI
Jul 7
AgenticPD: A Stage-Aware Agentic Framework for Physical Design QoR Optimization
arXiv:2607. 04758v1 Announce Type: new Abstract: Physical design quality-of-results~(QoR) optimization is hard and expensive.
arXiv AI
Jun 16
StarOR: Synergizing Tree Search and Test-Time Reinforcement Learning for Optimization Modeling
arXiv:2606. 15197v1 Announce Type: cross Abstract: Optimization modeling is inherently hierarchical, requiring a precise sequence of symbolic commitments.
arXiv AI
Jul 28
HELIOS: An LLM-Driven Autonomous Indirect Trajectory Optimization Agent
arXiv:2607. 24051v1 Announce Type: cross Abstract: Low-thrust trajectory optimization is a core technology in deep-space mission design.
arXiv AI
Aug 3
A Multi-Agent System for Motor Design Optimization via an FEA-AI Hybrid Approach
arXiv:2606. 09037v2 Announce Type: replace Abstract: This study presents a large language model (LLM)-based multi-agent framework for interior permanent magnet synchronous motor (IPMSM) design optimization that mitigates limitations of conventional workflows: expertise-dependent problem setup and data preparation, the prohibitive computational cost of finite element analysis (FEA), and the unreliability of AI surrogates in unexplored regions.
arXiv AI
Aug 6
Argus: A General-Purpose Agentic Runtime for Long-Horizon Reasoning
arXiv:2608. 05144v1 Announce Type: new Abstract: Long-horizon reasoning requires an agentic runtime that can persist when evidence supports its current approach and pivot when measurements reveal failure, hidden constraints, or a misspecified objective.