arXiv:2603.21460v1 Announce Type: cross Abstract: Patient education materials for solid-organ transplantation vary substantially across U.S. centers, yet no systematic method exists to quantify this heterogeneity at scale. We introduce a framework that grounds the same patient questions in different centers’ handbooks using retrieval-augmented language models and compares the resulting answers using a five-label consistency taxonomy. […]
Adaptive Insurance Reserving with CVaR-Constrained Reinforcement Learning under Macroeconomic Regimes
arXiv:2504.09396v2 Announce Type: replace-cross Abstract: We develop a reinforcement learning (RL) framework for insurance loss reserving that formulates reserve setting as a finite-horizon sequential decision problem under claim development uncertainty, macroeconomic stress, and solvency governance. The reserving process is modeled as a Markov Decision Process (MDP) in which reserve adjustments influence future reserve adequacy, capital […]
KG-Hopper: Empowering Compact Open LLMs with Knowledge Graph Reasoning via Reinforcement Learning
arXiv:2603.21440v1 Announce Type: cross Abstract: Large Language Models (LLMs) demonstrate impressive natural language capabilities but often struggle with knowledge-intensive reasoning tasks. Knowledge Base Question Answering (KBQA), which leverages structured Knowledge Graphs (KGs) exemplifies this challenge due to the need for accurate multi-hop reasoning. Existing approaches typically perform sequential reasoning steps guided by predefined pipelines, restricting […]
Randomness and signal propagation in physics-informed neural networks (PINNs): A neural PDE perspective
arXiv:2509.18131v3 Announce Type: replace-cross Abstract: Physics-informed neural networks (PINNs) often exhibit weight matrices that appear statistically random after training, yet their implications for signal propagation and stability remain unsatisfactorily understood, let alone the interpretability. In this work, we analyze the spectral and statistical properties of trained PINN weights using viscous and inviscid variants of the […]
The Spillover Effects of Peer AI Rinsing on Corporate Green Innovation
arXiv:2603.18415v2 Announce Type: replace-cross Abstract: At a time when the phenomenon of ‘AI washing’ is quietly spreading, an increasing number of enterprises are using the label of artificial intelligence merely as a cosmetic embellishment in their annual reports, rather than as a genuine engine driving transformation. A test regarding the essence of innovation and the […]
StaR-KVQA: Structured Reasoning Traces for Implicit-Knowledge Visual Question Answering
arXiv:2510.06638v3 Announce Type: replace-cross Abstract: Knowledge-based Visual Question Answering (KVQA) requires models to ground entities in images and reason over factual knowledge. Recent work has introduced its implicit-knowledge variant, IK-KVQA, where a multimodal large language model (MLLM) is the sole knowledge source and answers are produced without external retrieval. Existing IK-KVQA approaches, however, are typically […]
Taxonomy-Aware Representation Alignment for Hierarchical Visual Recognition with Large Multimodal Models
arXiv:2603.00431v2 Announce Type: replace-cross Abstract: A high-performing, general-purpose visual understanding model should map visual inputs to a taxonomic tree of labels, identify novel categories beyond the training set for which few or no publicly available images exist. Large Multimodal Models (LMMs) have achieved remarkable progress in fine-grained visual recognition (FGVR) for known categories. However, they […]
Interpretable Cross-Domain Few-Shot Learning with Rectified Target-Domain Local Alignment
arXiv:2603.17655v2 Announce Type: replace-cross Abstract: Cross-Domain Few-Shot Learning (CDFSL) adapts models trained with large-scale general data (source domain) to downstream target domains with only scarce training data, where the research on vision-language models (e.g., CLIP) is still in the early stages. Typical downstream domains, such as medical diagnosis, require fine-grained visual cues for interpretable recognition, […]
HyReach: Vision-Guided Hybrid Manipulator Reaching in Unseen Cluttered Environments
arXiv:2603.21421v1 Announce Type: cross Abstract: As robotic systems increasingly operate in unstructured, cluttered, and previously unseen environments, there is a growing need for manipulators that combine compliance, adaptability, and precise control. This work presents a real-time hybrid rigid-soft continuum manipulator system designed for robust open-world object reaching in such challenging environments. The system integrates vision-based […]
Manifold-Aware Exploration for Reinforcement Learning in Video Generation
arXiv:2603.21872v1 Announce Type: cross Abstract: Group Relative Policy Optimization (GRPO) methods for video generation like FlowGRPO remain far less reliable than their counterparts for language models and images. This gap arises because video generation has a complex solution space, and the ODE-to-SDE conversion used for exploration can inject excess noise, lowering rollout quality and making […]
MHPO: Modulated Hazard-aware Policy Optimization for Stable Reinforcement Learning
arXiv:2603.16929v2 Announce Type: replace-cross Abstract: Regulating the importance ratio is critical for the training stability of Group Relative Policy Optimization (GRPO) based frameworks. However, prevailing ratio control methods, such as hard clipping, suffer from non-differentiable boundaries and vanishing gradient regions, failing to maintain gradient fidelity. Furthermore, these methods lack a hazard-aware mechanism to adaptively suppress […]
One Model, Two Markets: Bid-Aware Generative Recommendation
arXiv:2603.22231v1 Announce Type: cross Abstract: Generative Recommender Systems using semantic ids, such as TIGER (Rajput et al., 2023), have emerged as a widely adopted competitive paradigm in sequential recommendation. However, existing architectures are designed solely for semantic retrieval and do not address concerns such as monetization via ad revenue and incorporation of bids for commercial […]