🤖 Category

cs.LG

2315 papers

Operator Learning for Consolidation: An Architectural Comparison for DeepONet Variants

This study systematically evaluates and enhances DeepONet architectures for geotechnical consolidation problems, demonstrating that a physics-inspired, Fourier feature-enhanced model (Model 4) significantly outperforms standard configurations and achieves up to 1,000-fold computational speedups in 3D scenarios, thereby enabling efficient uncertainty quantification and advancing the integration of scientific machine learning in geotechnics.

Yongjin Choi, Chenying Liu, Jorge Macedo2026-03-11🤖 cs.LG

Multimodal LLM-assisted Evolutionary Search for Programmatic Control Policies

This paper introduces Multimodal Large Language Model-assisted Evolutionary Search (MLES), a novel framework that combines multimodal LLMs with evolutionary search and visual feedback to automatically generate transparent, verifiable, and human-aligned programmatic control policies that match the performance of deep reinforcement learning methods like PPO.

Qinglong Hu, Xialiang Tong, Mingxuan Yuan, Fei Liu, Zhichao Lu, Qingfu Zhang2026-03-11🤖 cs.LG

CTRL Your Shift: Clustered Transfer Residual Learning for Many Small Datasets

This paper introduces Clustered Transfer Residual Learning (CTRL), a meta-learning method that combines cross-domain residual learning with adaptive clustering to improve prediction accuracy and preserve source-level heterogeneity across numerous small datasets with distributional shifts, demonstrating superior performance over state-of-the-art benchmarks on five large-scale datasets including a Swiss asylum resettlement program.

Gauri Jain, Dominik Rothenhäusler, Kirk Bansak, Elisabeth Paulson2026-03-11🤖 cs.LG

Iterative In-Context Learning to Enhance LLMs Abstract Reasoning: The Case-Study of Algebraic Tasks

This paper proposes an iterative in-context learning methodology that optimizes few-shot example selection to significantly enhance large language models' systematic generalization and reasoning capabilities on algebraic tasks with non-standard rules, revealing that simpler examples can sometimes outperform complex ones.

Stefano Fioravanti, Matteo Zavatteri, Roberto Confalonieri, Kamyar Zeinalipour, Paolo Frazzetto, Alessandro Sperduti, Nicolò Navarin2026-03-11🤖 cs.LG

A Surrogate model for High Temperature Superconducting Magnets to Predict Current Distribution with Neural Network

This paper presents a fully connected residual neural network (FCRN) surrogate model trained on finite element method data to rapidly and accurately predict current density distributions and optimize the design of large-scale high-temperature superconducting magnets, overcoming the computational limitations of traditional simulations.

Mianjun Xiao, Peng Song, Yulong Liu, Cedric Korte, Ziyang Xu, Jiale Gao, Jiaqi Lu, Haoyang Nie, Qiantong Deng, Timing Qu2026-03-11🤖 cs.LG

Robot Control Stack: A Lean Ecosystem for Robot Learning at Scale

This paper introduces the Robot Control Stack (RCS), a lean and modular software ecosystem designed to bridge the gap between large-scale Vision-Language-Action model training and real-world robot deployment by unifying simulation and physical control, while validating its effectiveness through extensive evaluations of policies like Octo, OpenVLA, and Pi Zero.

Tobias Jülg, Pierre Krack, Seongjin Bien, Yannik Blei, Khaled Gamal, Ken Nakahara, Johannes Hechtl, Roberto Calandra, Wolfram Burgard, Florian Walter2026-03-11🤖 cs.LG

ZeroSiam: An Efficient Asymmetry for Test-Time Entropy Optimization without Collapse

This paper introduces ZeroSiam, an efficient asymmetric Siamese architecture that prevents model collapse during test-time entropy minimization by employing asymmetric divergence alignment, thereby enhancing adaptation and reasoning performance across diverse vision and language tasks with negligible overhead.

Guohao Chen, Shuaicheng Niu, Deyu Chen, Jiahao Yang, Zitian Zhang, Mingkui Tan, Pengcheng Wu, Zhiqi Shen2026-03-11🤖 cs.LG

Compose Your Policies! Improving Diffusion-based or Flow-based Robot Policies via Test-time Distribution-level Composition

This paper introduces General Policy Composition (GPC), a training-free method that enhances diffusion and flow-based robot policies by theoretically and empirically demonstrating that convexly combining the distributional scores of multiple pre-trained policies at test time yields superior performance and adaptability across diverse tasks.

Jiahang Cao, Yize Huang, Hanzhong Guo, Rui Zhang, Mu Nan, Weijian Mai, Jiaxu Wang, Hao Cheng, Jingkai Sun, Gang Han, Wen Zhao, Qiang Zhang, Yijie Guo, Qihao Zheng, Chunfeng Song, Xiao Li, Ping Luo, Andrew F. Luo2026-03-11🤖 cs.LG

Improved Robustness of Deep Reinforcement Learning for Control of Time-Varying Systems by Bounded Extremum Seeking

This paper proposes a hybrid control framework that combines Deep Reinforcement Learning (DRL) with robust model-independent bounded extremum seeking to enhance the stability and adaptability of controlling nonlinear time-varying systems, demonstrating its effectiveness through numerical simulations and the automatic tuning of a particle accelerator.

Shaifalee Saxena, Alan Williams, Rafael Fierro, Alexander Scheinker2026-03-11🤖 cs.LG

Latent Speech-Text Transformer

The Latent Speech-Text Transformer (LST) improves the efficiency and performance of auto-regressive speech-text models by aggregating speech tokens into latent patches, which aligns sequence granularity with text, reduces computational costs, and achieves significant accuracy gains across speech and text benchmarks.

Yen-Ju Lu, Yashesh Gaur, Wei Zhou, Benjamin Muller, Jesus Villalba, Najim Dehak, Luke Zettlemoyer, Gargi Ghosh, Mike Lewis, Srinivasan Iyer, Duc Le2026-03-11🤖 cs.AI

AlphaApollo: A System for Deep Agentic Reasoning

AlphaApollo is an agentic reasoning system that enhances foundation models' performance on complex, long-horizon tasks by orchestrating multi-turn agentic reasoning, turn-level reinforcement learning for tool-use optimization, and a propose-judge-update evolution loop with verification.

Zhanke Zhou, Chentao Cao, Xiao Feng, Xuan Li, Zongze Li, Xiangyu Lu, Jiangchao Yao, Weikai Huang, Tian Cheng, Jianghangfan Zhang, Tangyu Jiang, Linrui Xu, Yiming Zheng, Brando Miranda, Tongliang Liu, Sanmi Koyejo, Masashi Sugiyama, Bo Han2026-03-11🤖 cs.AI

Exploring Single Domain Generalization of LiDAR-based Semantic Segmentation under Imperfect Labels

This paper addresses the challenge of LiDAR-based 3D semantic segmentation under noisy labels and domain shifts by introducing the DGLSS-NL task, establishing a new benchmark, and proposing DuNe, a dual-view framework that achieves state-of-the-art robustness across multiple datasets.

Weitong Kong, Zichao Zeng, Di Wen, Jiale Wei, Kunyu Peng, June Moh Goo, Jan Boehm, Rainer Stiefelhagen2026-03-11🤖 cs.LG