arXiv:2606.06696v1 Announce Type: cross Abstract: Vision and language models (VLMs) hold immense promise to transform biomedical imaging workflows, from detecting lesions in chest X-rays to profiling cellular features in microscopy. Realizing this potential, however, requires robust and fine-grained visual perception. Models need to correctly interpret subtle features in images, and they must do so across […]
Graph Neural Network leveraging Higher-order Class Label Connectivity for Heterophilous Graphs
arXiv:2606.07475v1 Announce Type: cross Abstract: Node classification in graph neural networks (GNNs) has been widely applied in various fields of graph analysis. GNNs achieve high-accuracy node classification in homophilous graphs, where nodes with the same class label tend to be connected. However, their performance remains limited in heterophilous graphs, where nodes with different class labels […]
Focus-then-Context: Subject-Centric Progressive Visual Token Reduction for Vision-Language Models
arXiv:2605.20950v2 Announce Type: replace-cross Abstract: Vision-Language Models (VLMs) face a bottleneck of prohibitive computational costs arising from massive visual token sequences during inference. Existing vision token reduction methods alleviate this burden, but they unintentionally preserve the isolated visual subject strictly aligned with the user’s query, which fails to substantially explore salient subjects and their contextual […]
A robust PPG foundation model using multimodal physiological supervision
arXiv:2606.07365v1 Announce Type: cross Abstract: Photoplethysmography (PPG), a non-invasive measure of changes in blood volume, is widely used in both wearable devices and clinical settings. Recent PPG foundation models either use open-source ICU datasets with pretraining paradigms that require curated data and thus complicate generalization to field-like data, or use closed-source field-like PPG data. In […]
VeriHGN: Heterogeneous Graph-Based Congestion Prediction for Chip Layout Verification
arXiv:2603.11075v3 Announce Type: replace-cross Abstract: As Very Large Scale Integration (VLSI) designs continue to scale in size and complexity, layout verification has become a central challenge in modern Electronic Design Automation (EDA) workflows. In practice, congestion can only be accurately identified after detailed routing, making traditional verification both time-consuming and costly. Learning-based approaches have therefore […]
Linear Ordering Problem: Time for a Change
arXiv:2605.31051v2 Announce Type: replace-cross Abstract: The Linear Ordering Problem (LOP) is a fundamental combinatorial optimization problem with important applications in areas such as economics, social choice, and machine learning. Its most prominent use is the triangulation of economic input-output tables, which helps identify critical industries in an economy. Most existing algorithms have been evaluated on […]
A Temporal Spatial Minimax Rate for Smoothly-Varying Distributions in Wasserstein Space
arXiv:2606.07325v1 Announce Type: cross Abstract: We study the minimax rate of estimating a future value $mu_t_n+h$ of a curve $tmapstomu_t$ in the $2$-Wasserstein space $mathcalP_2(mathbbR^d)$ from finitely many noisy snapshots of its past, under an adiabatic bound $|nabla_t^k v|levarepsilon$ on the $k$-th covariant derivative of the velocity field. Our central result is a unified temporal-spatial […]
When Surface Form Changes Moderation Decisions: A Paired Study of Code-Mixed Workflow Instability
arXiv:2606.05654v2 Announce Type: replace-cross Abstract: Hate moderation is often evaluated as classification on clean English inputs, but deployed systems must route content to actions such as ALLOW, FLAG, or REVIEW. We study how this workflow changes under code-mixed inputs using a paired evaluation setting where the same underlying content is expressed as clean English and […]
Scenario Generation for Risk-Aware Reinforcement Learning with Probably Approximately Safe Guarantees
arXiv:2606.04812v2 Announce Type: replace-cross Abstract: Guaranteeing safety is critical to the deployment of reinforcement learning (RL) agents in the real-world, especially as policies learned using deep RL may demonstrate susceptibility to transition perturbations that result in unknown or unsafe behaviour. A method of policy verification is to construct probabilistic barrier-certificates by sampling policy trajectories with […]
Detecting and Mitigating Bias by Treating Fairness as a Symmetry Operation
arXiv:2606.06514v1 Announce Type: new Abstract: Machine learning systems deployed in high stakes socioeconomic settings routinely display bias. We formalize bias as a symmetry breaking operation: a classifier is fair if its outputs remain invariant under the counterfactual operation of switching a sensitive attribute, with merit features held fixed. We implement loss based regularization as a […]
Generative Models Erode Human Temporal Learning Through Market Selection
arXiv:2606.06572v1 Announce Type: cross Abstract: We argue that modern generative models create structural risks for knowledge and cultural production at current, sub-AGI capability levels. We define Human Temporal Learning (HTL) as path-dependent knowledge accumulation through sustained engagement with problems over time. Generative outputs increasingly resemble HTL-intensive work in surface features, so verifying whether a given […]
How Language Models Fail: Token-Level Signatures of Committed and Persistent Reasoning Failures
arXiv:2606.06635v1 Announce Type: cross Abstract: Failures in language model reasoning emerge through distinct processes that leave identifiable signatures in the reasoning trace. We characterize these failures using token-level uncertainty signals, finding they arise through two empirically distinguishable processes. The first is committed failure, in which a model locks onto an incorrect reasoning path early in […]