🤖 Category

cs.AI

11553 papers

Don't Freeze, Don't Crash: Extending the Safe Operating Range of Neural Navigation in Dense Crowds

This paper proposes a reinforcement learning approach for dense crowd navigation that achieves zero-shot generalization to higher crowd densities by combining density-invariant observation encoding, density-randomized training, and physics-informed proxemic reward shaping, thereby significantly outperforming existing learning-based and analytical methods in success rate and collision avoidance without freezing.

Jiefu Zhang, Yang Xu, Vaneet Aggarwal2026-03-10🤖 cs.LG

Rank-Factorized Implicit Neural Bias: Scaling Super-Resolution Transformer with FlashAttention

This paper proposes Rank-factorized Implicit Neural Bias (RIB), a novel positional bias mechanism that enables the use of hardware-efficient FlashAttention in Super-Resolution Transformers, allowing for significantly larger window sizes and training patches that achieve state-of-the-art performance (35.63 dB PSNR) while reducing training and inference times by 2.1×\times and 2.9×\times, respectively.

Dongheon Lee, Seokju Yun, Jaegyun Im, Youngmin Ro2026-03-10🤖 cs.LG

Stabilizing Reinforcement Learning for Diffusion Language Models

This paper identifies that applying Group Relative Policy Optimization (GRPO) to diffusion language models causes reward collapse due to noisy importance ratio estimates and formulation mismatches, and proposes StableDRL, a reformulated algorithm featuring unconditional clipping and self-normalization to stabilize training and prevent policy drift.

Jianyuan Zhong, Kaibo Wang, Ding Ding, Zijin Feng, Haoli Bai, Yang Xiang, Jiacheng Sun, Qiang Xu2026-03-10🤖 cs.LG

Property-driven Protein Inverse Folding With Multi-Objective Preference Alignment

This paper introduces ProtAlign, a multi-objective preference alignment framework that fine-tunes pretrained inverse folding models to simultaneously optimize diverse developability properties like solubility and thermostability while preserving structural designability, resulting in the enhanced MoMPNN model for practical protein sequence design.

Xiaoyang Hou, Junqi Liu, Chence Shi, Xin Liu, Zhi Yang, Jian Tang2026-03-10🤖 cs.LG

Robotic Foundation Models for Industrial Control: A Comprehensive Survey and Readiness Assessment Framework

This paper surveys the landscape of robotic foundation models, identifies eleven key industrial implications to establish a 149-criteria assessment framework, and evaluates 324 models to reveal that current industrial readiness is limited and uneven, necessitating a shift from isolated benchmark successes to systematic integration of safety, real-time performance, and robust system deployment.

David Kube, Simon Hadwiger, Tobias Meisen2026-03-10💻 cs

Learning Unbiased Cluster Descriptors for Interpretable Imbalanced Concept Drift Detection

This paper proposes ICD3, an interpretable and robust approach for detecting concept drift in imbalanced streaming data by employing multi-distribution-granular search to identify small concepts and training independent One-Cluster Classifiers for each, thereby overcoming the masking effect of dominant large clusters.

Yiqun Zhang, Zhanpei Huang, Mingjie Zhao, Chuyao Zhang, Yang Lu, Yuzhu Ji, Fangqing Gu, An Zeng2026-03-10🤖 cs.LG

Gradient-based Nested Co-Design of Aerodynamic Shape and Control for Winged Robots

This paper introduces a general-purpose, gradient-based nested co-design framework that jointly optimizes the aerodynamic shape and motion planner of winged robots using neural surrogate models for complex flow conditions, demonstrating superior performance and efficiency over evolutionary baselines in tasks like perching and short landing.

Daniele Affinita, Mingda Xu, Benoît Valentin Gherardi, Pascal Fua2026-03-10💻 cs

Diversity-Aware Adaptive Collocation for Physics-Informed Neural Networks via Sparse QUBO Optimization and Hybrid Coresets

This paper proposes a diversity-aware adaptive collocation method for Physics-Informed Neural Networks that formulates point selection as a sparse QUBO optimization problem on a kNN graph to efficiently construct hybrid coreset subsets, thereby reducing training redundancy and overhead while improving accuracy on PDEs with shock formation.

Hadi Salloum, Maximilian Mifsud Bonici, Sinan Ibrahim, Pavel Osinenko, Alexei Kornaev2026-03-10🤖 cs.LG

Failure Detection in Chemical Processes using Symbolic Machine Learning: A Case Study on Ethylene Oxidation

This paper demonstrates that symbolic machine learning can effectively predict failures in chemical processes, such as ethylene oxidation, by generating interpretable, rule-based models that outperform traditional black-box methods while addressing the scarcity of real-world failure data through simulator-generated examples.

Julien Amblard, Niklas Groll, Matthew Tait, Mark Law, Gürkan Sin, Alessandra Russo2026-03-10🤖 cs.LG

HGT-Scheduler: Deep Reinforcement Learning for the Job Shop Scheduling Problem via Heterogeneous Graph Transformers

This paper proposes HGT-Scheduler, a deep reinforcement learning framework that utilizes Heterogeneous Graph Transformers to explicitly model the distinct edge semantics of the Job Shop Scheduling Problem, thereby outperforming homogeneous graph baselines on benchmark instances by capturing type-specific relational patterns through edge-type-dependent attention mechanisms.

Bulent Soykan2026-03-10🤖 cs.LG