arXiv AI
Jun 30
Mechanistically Eliciting Latent Behaviors in Language Models
arXiv:2606. 29604v1 Announce Type: cross Abstract: We aim to discover diverse, generalizable perturbations of LLM internals that can surface hidden behavioral modes.
A Multi-Agent LLM System for Automated Tuning of Specialized Neural Network Models
The paper introduces a multi‑agent large language model framework that automatically tunes key components of specialized neural network models without altering their overall architecture. Three agents—analyst, coder, and recorder—operate within a sandboxed environment to generate code for specified components, enabling efficient optimization across diverse tasks. Experiments on spiking neural networks for classification and Physics‑Informed Neural Networks for solving high‑order differential equations demonstrate significant performance gains, such as 13‑point accuracy improvements on MNIST and substantial reductions in error metrics for nonlinear Schrödinger and Kuramoto–Sivashinsky equations.
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arXiv AI
Oct 16
MLExpert: Towards a Generalist Agentic Model for Autonomous Machine Learning Engineering
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.
arXiv AI
Oct 1
Right Answers, Costly Models: The Efficiency Gap in LLM-based Optimization Modeling
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 AI
Sep 3
OptSkills: Learning Generalizable Optimization Skills from Problem Archetypes via Cluster-Based Distillation
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.
arXiv AI
Jul 10
Agentic Neural Architecture Search
arXiv:2607. 07984v1 Announce Type: new Abstract: Neural architecture search (NAS) methods have grown increasingly efficient, yet they remain bounded by manually engineered search spaces that require substantial domain expertise and must be rebuilt for every new task.
Hugging Face Trending Papers
Oct 15
MLExpert: Towards a Generalist Agentic Model for Autonomous Machine Learning Engineering
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.
arXiv Machine Learning
Jul 27
Layer-wise LoRA fine-tuning: a similarity metric approach
arXiv:2602. 05988v2 Announce Type: replace Abstract: Pre-training Large Language Models (LLMs) on web-scale datasets becomes fundamental for advancing general-purpose AI.
arXiv Machine Learning
Jul 30
EvoPINN: Agentic Discovery of Executable Algorithms for Physics-Informed Neural Networks
arXiv:2607. 26490v1 Announce Type: cross Abstract: Physics-informed neural networks (PINNs) have emerged as a powerful paradigm for solving partial differential equations (PDEs), yet their performance heavily relies on the manual, trial-and-error engineering of neural representations, loss formulations, and optimization dynamics.
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.
arXiv Machine Learning
Jul 31
The MiniMax-M2 Series: Mini Activations Unleashing Max Real-World Intelligence
arXiv:2605. 26494v2 Announce Type: replace-cross Abstract: We introduce the MiniMax-M2 series, a family of Mixture-of-Experts language models built around the principle that mini activations can unleash maximum real-world intelligence.
arXiv Machine Learning
Sep 16
Agentic Search Spaces for Tabular Machine Learning
arXiv:2609.16309v1 Announce Type: new Abstract: Despite the rapid progress of LLM-based agents for planning, code generation, and debugging, their practical value for tabular machine learning remains...
arXiv AI
Oct 21
MaD-RSI: Model-as-Data for Recursive Self-Improvement of Agent Harnesses
arXiv:2610.28201v1 Announce Type: new Abstract: Recursive self-improvement (RSI) of agent harnesses has emerged as an effective approach to enhancing the capabilities of large language model (LLM) ag...