arXiv:2604.25209v2 Announce Type: replace-cross Abstract: Dimensionality reduction methods such as UMAP and t-SNE are central tools for visualising high-dimensional data, but their local-neighborhood objectives can preserve sampling noise while distorting global topology. We show that standard local metrics reward this noise memorisation: top-performing embeddings invent cycles and disconnected islands absent from the data. We introduce […]
Autonomous Knowledge Graph Exploration with Adaptive Breadth-Depth Retrieval
arXiv:2601.13969v2 Announce Type: replace Abstract: Retrieving evidence for language model queries from knowledge graphs requires balancing broad search across the graph with multi-hop traversal to follow relational links. Similarity-based retrievers provide coverage but remain shallow, whereas traversal-based methods rely on selecting seed nodes to start exploration, which can fail when queries span multiple entities and […]
Data-Centric Foundation Models in Computational Healthcare: A Survey
arXiv:2401.02458v3 Announce Type: replace-cross Abstract: The advent of foundation models (FMs) as an emerging suite of AI techniques has struck a wave of opportunities in computational healthcare. The interactive nature of these models, guided by pre-training data and human instructions, has ignited a data-centric AI paradigm that emphasizes better data characterization, quality, and scale. In […]
Efficient Traffic Forecasting on Large-Scale Road Network by Regularized Adaptive Graph Convolution
arXiv:2506.07179v2 Announce Type: replace-cross Abstract: Traffic prediction is a critical task in spatial-temporal forecasting with broad applications in travel planning and urban management. To model the complex spatial-temporal dependencies in traffic data, Spatial-Temporal Graph Convolutional Networks (STGCNs) have been widely employed, achieving advanced performance. However, when applied to large-scale road networks, the quadratic computational complexity […]
PBiLoss: Popularity-Aware Regularization to Improve Fairness in Graph-Based Recommender Systems
arXiv:2507.19067v2 Announce Type: replace-cross Abstract: Recommender systems based on graph neural networks (GNNs) have been proved to perform well on user-item interactions. However, they commonly suffer from popularity bias — the tendency to over-recommend popular items — resulting in less personalization, unfair exposure and lower recommendation diversity. Current solutions address popularity bias through different stages […]
Stress Testing Factual Consistency Metrics for Long-Document Summarization
arXiv:2511.07689v2 Announce Type: replace-cross Abstract: Evaluating the factual consistency of abstractive text summarization remains a significant challenge, particularly for long documents, where conventional metrics struggle with input length limitations and long-range dependencies. In this work, we systematically evaluate the reliability of six widely used reference-free factuality metrics, originally proposed for short-form summarization, in the long-document […]
CoFL: Continuous Flow Fields for Language-Conditioned Navigation
arXiv:2603.02854v2 Announce Type: replace-cross Abstract: Existing language-conditioned navigation systems typically rely on modular pipelines or trajectory generators, but the latter use each scene–instruction annotation mainly to supervise one start-conditioned rollout. To address these limitations, we present CoFL, an end-to-end policy that maps a bird’s-eye view (BEV) observation and a language instruction to a continuous flow […]
Rethinking Satellite Image Restoration for Onboard AI: A Lightweight Learning-Based Approach
arXiv:2604.12807v2 Announce Type: replace-cross Abstract: Satellite image restoration aims to improve image quality by compensating for degradations (e.g., noise and blur) introduced by the imaging system and acquisition conditions. As a fundamental preprocessing step, restoration directly impacts both ground-based product generation and emerging onboard AI applications. Traditional restoration pipelines based on sequential physical models are […]
Inverting Foundation Models of Brain Function with Simulation-Based Inference
arXiv:2604.23865v2 Announce Type: replace-cross Abstract: Foundation models of brain activity promise a new frontier for in silico neuroscience by emulating neural responses to complex stimuli across tasks and modalities. A natural next step is to ask whether these models can also be used in reverse. Can we recover a stimulus or its properties from synthetic […]
Hyper Input Convex Neural Networks for Shape Constrained Learning and Optimal Transport
arXiv:2604.26942v1 Announce Type: cross Abstract: We introduce Hyper Input Convex Neural Networks (HyCNNs), a novel neural network architecture designed for learning convex functions. HyCNNs combine the principles of Maxout networks with input convex neural networks (ICNNs) to create a neural network that is always convex in the input, theoretically capable of leveraging depth, and performs […]
ToolPRM: Fine-Grained Inference Scaling of Structured Outputs for Function Calling
arXiv:2510.14703v2 Announce Type: replace Abstract: Large language models (LLMs) excel at function calling, but inference scaling has been explored mainly for unstructured generation. We propose an inference-scaling framework for structured outputs that combines fine-grained beam search with textbfToolPRM, a process reward model scoring each intra-call decision (function name and argument filling). We build the first […]
Learning to Rewrite Tool Descriptions for Reliable LLM-Agent Tool Use
arXiv:2602.20426v2 Announce Type: replace Abstract: While most efforts to improve LLM-based tool-using agents focus on the agent itself – through larger models, better prompting, or fine-tuning – agent performance increasingly plateaus due to the quality of the tool interfaces these agents consume. Tool descriptions are often written for human developers and tolerate ambiguity that agents […]