arXiv:2608. 05773v1 Announce Type: new Abstract: A geometry-conditioned, neuro-symbolic closed-loop architecture is proposed for laser powder bed fusion, in which a standards-aligned ontology operates inside the control loop and couples symbolic reasoning with statistical learning to set the targets of a constraint-aware predictive controller.
The paper introduces OptTips, a knowledge base of 50 expert optimization modeling techniques, and OptDachshund, a multi‑agent framework that generates mathematical models and solver code from natural‑language problem descriptions. Using these tools, the authors create the EfficientOpt benchmark, comprising 561 expert‑reviewed tasks with paired reference implementations, to evaluate large language models (LLMs) on both correctness and computational efficiency. Their experiments with 11 LLMs show a consistent efficiency gap: even when LLMs produce correct solutions, the resulting programs often take longer to solve than expert‑crafted counterparts, highlighting the need to assess both accuracy and runtime performance in LLM‑based optimization modeling.
arXiv:2607. 23124v1 Announce Type: new Abstract: Large language model agents have advanced rapidly, yet progress remains fragmented across domains, capabilities, task difficulty, and interaction settings.
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.
MLExpert is a 35B Mixture-of-Experts agentic model designed to autonomously perform long‑horizon machine learning engineering (MLE) tasks. It uses a post‑training method that combines hierarchical agentic supervised fine‑tuning with Windowed Interaction RL to learn from intermediate outcomes in complex experimentation workflows. On the MLE‑Bench Lite benchmark, MLExpert raises the average medal rate from 37.9% to 72.7%, matching the performance of larger frontier models and demonstrating generalizable optimization capabilities across related tasks.
OptSkills is an archetype‑centric agent that learns and reasons about optimization problems using large language models. It clusters problems by underlying archetypes, explores diverse modeling and solver configurations within each cluster, and distills successful trajectories into reusable workflow‑level skills. The system achieves state‑of‑the‑art accuracy on multiple datasets, outperforming prior methods on challenging benchmarks such as MIPLIB‑NL and OOD NLCO.
EngiAI introduces a capability-based evaluation framework for tool-connected engineering agents, assessing workflow execution, retrieval-assisted parameter selection, HPC orchestration, and training-code authoring using execution traces and engineering artifacts. The framework was applied to four LLM backends on EngiBench Beams2D and Photonics2D, revealing that proprietary models outperform open-source ones in workflow completion and HPC orchestration, while indexed retrieval improves parameter selection. The study demonstrates that evaluating distinct skills separately provides clearer insight into failure mechanisms than end-to-end success rates alone.
MLExpert is a 35B Mixture-of-Experts agentic model designed to autonomously conduct long‑horizon machine learning engineering (MLE) tasks. It uses a post‑training approach that combines hierarchical supervised fine‑tuning with Windowed Interaction RL to learn from intermediate outcomes in complex experimentation workflows. On the MLE‑Bench Lite benchmark, MLExpert raises the average medal rate from 37.9% to 72.7%, matching the performance of larger frontier models and demonstrating transferable optimization capabilities to other benchmarks such as NatureBench Lite.
The paper introduces two lightweight inference harnesses, OPTICYCLE and OPTISTATE, designed to enable large language models to translate natural-language optimization problems into solver programs without additional training. Evaluated across nine benchmarks and 11 Qwen variants (0.6B–27B parameters), the harnesses consistently improve macro accuracy, with execution-guided revision boosting performance by up to 49.46 percentage points. Despite their simplicity, both harnesses outperform six adapted agentic workflows and four post-trained optimization models of comparable size, though the benefit of explicit formulation state varies by model.
arXiv:2606. 09118v1 Announce Type: new Abstract: As LLM capabilities advance rapidly, the evaluation methods used to assess them increasingly lag behind.
The paper investigates how the skill paradigm—using modular abilities within agentic frameworks—works with small to medium open‑source language models (270 M–80 B parameters) in industrial, resource‑constrained settings. A large‑scale empirical study on an insurance‑claim classification task and a set of Python programming problems shows that very small models often fail to retrieve or select correct skills, while larger 30 B–80 B models benefit significantly from skill augmentation. The authors also present a deployment‑oriented GPU profile, reporting VRAM time occupancy (GB·min) across models, to help characterize GPU‑memory costs for different model scales. "whyItMatters":"The study provides concrete evidence on how skill augmentation scales with model size, task complexity, and GPU budget, offering practical deployment insights for industrial applications using open‑source language models."
arXiv:2608. 16068v1 Announce Type: cross Abstract: Large language models (LLMs) are increasingly deployed as agents that rely on system prompts to use tools and complete tasks.