AI system learns to keep warehouse robot traffic running smoothly
This new approach adapts to decide which robots should get the right of way at every moment, avoiding congestion and increasing throughput.
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Recurrent neural network-based robust control systems with regional properties and application to MPC design
arXiv:2506.20334v4 Announce Type: replace-cross Abstract: This paper investigates the design of output-feedback schemes for systems described by a class of recurrent neural networks. We propose a procedure based on linear matrix inequalities for designing an observer and a static state-feedback…
Efficient Continual Learning in Language Models via Thalamically Routed Cortical Columns
arXiv:2602.22479v4 Announce Type: replace Abstract: Large language models deployed in the wild must adapt to evolving data, user behavior, and task mixtures without erasing previously acquired capabilities. In practice, this remains difficult: sequential updates induce catastrophic forgetting, while many stabilization…
A Comprehensive Survey on Enterprise Financial Risk Analysis from Big Data and LLMs Perspective
arXiv:2211.14997v5 Announce Type: replace-cross Abstract: Enterprise financial risk analysis aims at predicting the future financial risk of enterprises. Due to its wide and significant application, enterprise financial risk analysis has always been the core research topic in the fields of…
QUARK: Quantization-Enabled Circuit Sharing for Transformer Acceleration by Exploiting Common Patterns in Nonlinear Operations
arXiv:2511.06767v2 Announce Type: replace Abstract: Transformer-based models have revolutionized computer vision (CV) and natural language processing (NLP) by achieving state-of-the-art performance across a range of benchmarks. However, nonlinear operations in models significantly contribute to inference latency, presenting unique challenges for…
CGRL: Causal-Guided Representation Learning for Graph Out-of-Distribution Generalization
arXiv:2603.24304v1 Announce Type: cross Abstract: Graph Neural Networks (GNNs) have achieved impressive performance in graph-related tasks. However, they suffer from poor generalization on out-of-distribution (OOD) data, as they tend to learn spurious correlations. Such correlations present a phenomenon that GNNs…
Moonwalk: Inverse-Forward Differentiation
arXiv:2402.14212v2 Announce Type: replace Abstract: Backpropagation’s main limitation is its need to store intermediate activations (residuals) during the forward pass, which restricts the depth of trainable networks. This raises a fundamental question: can we avoid storing these activations? We address…
StateLinFormer: Stateful Training Enhancing Long-term Memory in Navigation
arXiv:2603.23571v1 Announce Type: new Abstract: Effective navigation intelligence relies on long-term memory to support both immediate generalization and sustained adaptation. However, existing approaches face a dilemma: modular systems rely on explicit mapping but lack flexibility, while Transformer-based end-to-end models are…
Dual-Criterion Curriculum Learning: Application to Temporal Data
arXiv:2603.23573v1 Announce Type: new Abstract: Curriculum Learning (CL) is a meta-learning paradigm that trains a model by feeding the data instances incrementally according to a schedule, which is based on difficulty progression. Defining meaningful difficulty assessment measures is crucial and…
AscendOptimizer: Episodic Agent for Ascend NPU Operator Optimization
arXiv:2603.23566v1 Announce Type: new Abstract: AscendC (Ascend C) operator optimization on Huawei Ascend neural processing units (NPUs) faces a two-fold knowledge bottleneck: unlike the CUDA ecosystem, there are few public reference implementations to learn from, and performance hinges on a…