arXiv:2603.18916v2 Announce Type: replace Abstract: This paper presents a manifesto that articulates the conceptual foundations of Agentic Business Process Management (APM), an extension of Business Process Management (BPM) for governing autonomous agents executing processes in organizations. From a management perspective, APM represents a paradigm shift from the traditional process view of the business process, driven […]
Adaptive Layerwise Perturbation: Unifying Off-Policy Corrections for LLM RL
arXiv:2603.19470v1 Announce Type: cross Abstract: Off-policy problems such as policy staleness and training-inference mismatch, has become a major bottleneck for training stability and further exploration for LLM RL. To enhance inference efficiency, the distribution gap between the inference and updated policy grows, leading to heavy-tailed importance ratios. Heavy-tailed ratios arise when the policy is locally […]
A Subgoal-driven Framework for Improving Long-Horizon LLM Agents
arXiv:2603.19685v1 Announce Type: new Abstract: Large language model (LLM)-based agents have emerged as powerful autonomous controllers for digital environments, including mobile interfaces, operating systems, and web browsers. Web navigation, for example, requires handling dynamic content and long sequences of actions, making it particularly challenging. Existing LLM-based agents struggle with long-horizon planning in two main ways. […]
FedAgain: A Trust-Based and Robust Federated Learning Strategy for an Automated Kidney Stone Identification in Ureteroscopy
arXiv:2603.19512v1 Announce Type: cross Abstract: The reliability of artificial intelligence (AI) in medical imaging critically depends on its robustness to heterogeneous and corrupted images acquired with diverse devices across different hospitals which is highly challenging. Therefore, this paper introduces FedAgain, a trust-based Federated Learning (Federated Learning) strategy designed to enhance robustness and generalization for automated […]
Superclass-Guided Representation Disentanglement for Spurious Correlation Mitigation
arXiv:2508.08570v2 Announce Type: replace-cross Abstract: To enhance group robustness to spurious correlations, prior work often relies on auxiliary group annotations and assumes identical sets of groups across training and test domains. To overcome these limitations, we propose to leverage superclasses — categories that lie higher in the semantic hierarchy than the task’s actual labels — […]
dinov3.seg: Open-Vocabulary Semantic Segmentation with DINOv3
arXiv:2603.19531v1 Announce Type: cross Abstract: Open-Vocabulary Semantic Segmentation (OVSS) assigns pixel-level labels from an open set of text-defined categories, demanding reliable generalization to unseen classes at inference. Although modern vision-language models (VLMs) support strong open-vocabulary recognition, their representations learned through global contrastive objectives remain suboptimal for dense prediction, prompting many OVSS methods to depend on […]
A Unified Phase-native Computational Principle Governs Hippocampal Spike Timing and Neural Coding
arXiv:2603.19690v1 Announce Type: new Abstract: Hippocampal neurons exhibit precise phase locking to network oscillations, but the computational principle governing this temporal precision is still unclear. Neural information is conveyed jointly by firing rates and spike timing, but existing models treat these dimensions separately, limiting mechanistic interpretation of spike-field coupling and its reported association with spectral […]
Optimal Scalar Quantization for Matrix Multiplication: Closed-Form Density and Phase Transition
arXiv:2603.19559v1 Announce Type: cross Abstract: We study entrywise scalar quantization of two matrices prior to multiplication. Given $Ain R^mtimes k$ and $Bin R^ktimes n$, we quantize entries of $A$ and $B$ independently using scalar quantizers with $K_X$ and $K_Y$ levels per entry, and form $widehat C=widehat A,widehat B$. The objective is to minimize the matrix […]
Dementia-R1: Reinforced Pretraining and Reasoning from Unstructured Clinical Notes for Real-World Dementia Prognosis
arXiv:2601.03018v2 Announce Type: replace-cross Abstract: While Large Language Models (LLMs) have shown strong performance on clinical text understanding, they struggle with longitudinal prediction tasks such as dementia prognosis, which require reasoning over complex, non-monotonic symptom trajectories across multiple visits. Standard supervised training lacks explicit annotations for symptom evolution, while direct Reinforcement Learning (RL) is hindered […]
Stochastic Averaging and Statistical Inference of Glycolytic Pathway
arXiv:2603.19577v1 Announce Type: cross Abstract: Many biological processes exhibit oscillatory behavior. Among these, glycolytic oscillations have been extensively studied due to their well-characterized biochemical reaction networks. However, the complexity of these networks necessitates low-dimensional ordinary differential equation (ODE) models to identify core mechanisms and perform stability analysis. While previous studies proposed reduced ODE models, these […]
Stepwise: Neuro-Symbolic Proof Search for Automated Systems Verification
arXiv:2603.19715v1 Announce Type: new Abstract: Formal verification via interactive theorem proving is increasingly used to ensure the correctness of critical systems, yet constructing large proof scripts remains highly manual and limits scalability. Advances in large language models (LLMs), especially in mathematical reasoning, make their integration into software verification increasingly promising. This paper introduces a neuro-symbolic […]
ARMOR: Adaptive Resilience Against Model Poisoning Attacks in Continual Federated Learning for Mobile Indoor Localization
arXiv:2603.19594v1 Announce Type: cross Abstract: Indoor localization has become increasingly essential for applications ranging from asset tracking to delivering personalized services. Federated learning (FL) offers a privacy-preserving approach by training a centralized global model (GM) using distributed data from mobile devices without sharing raw data. However, real-world deployments require a continual federated learning (CFL) setting, […]