arXiv:2602.03906v1 Announce Type: cross Abstract: Information Bottleneck (IB) is widely used, but in deep learning, it is usually implemented through tractable surrogates, such as variational bounds or neural mutual information (MI) estimators, rather than directly controlling the MI I(X;Z) itself. The looseness and estimator-dependent bias can make IB “compression” only indirectly controlled and optimization fragile. We revisit the IB problem through the lens of information geometry and propose a textbf{Geo}metric textbf{I}nformation textbf{B}ottleneck (textbf{GeoIB}) that dispenses with mutual information […]

Ver mais

Excess inventory is one of the most persistent and costly inefficiencies in modern supply chains. For many organizations, it quietly erodes working capital, inflates warehousing costs, and creates a false sense of security around product availability. The traditional approach to managing inventory, which relied on intuition, spreadsheets, and static reorder points, simply cannot keep pace with the complexity of today’s demand signals. What has changed the game significantly is the application of data-driven optimization frameworks that bring together […]

Ver mais

arXiv:2601.20906v1 Announce Type: new Abstract: Precision oncology requires forecasting clinical events and trajectories, yet modeling sparse, multi-modal clinical time series remains a critical challenge. We introduce TwinWeaver, an open-source framework that serializes longitudinal patient histories into text, enabling unified event prediction as well as forecasting with large language models, and use it to build Genie Digital Twin (GDT) on 93,054 patients across 20 cancer types. In benchmarks, GDT significantly reduces forecasting error, achieving a median Mean Absolute Scaled […]

Ver mais

arXiv:2602.20294v1 Announce Type: new Abstract: Simulating real personalities with large language models requires grounding generation in authentic personal data. Existing evaluation approaches rely on demographic surveys, personality questionnaires, or short AI-led interviews as proxies, but lack direct assessment against what individuals actually said. We address this gap with an interview-grounded evaluation framework for personality simulation at a large scale. We extract over 671,000 question-answer pairs from 23,000 verified interview transcripts across 1,000 public personalities, each with an average […]

Ver mais

Background: Knee osteoarthritis (OA) frequently exhibits discordance between structural damage observed in imaging and patient-reported symptoms such as pain. This mismatch complicates clinical interpretation and patient stratification and remains insufficiently modeled in existing decision-support systems. Methods: We propose a discordance-aware multimodal framework that combines machine learning prediction models with a tool-grounded multi-agent reasoning system. Using baseline data from the FNIH Osteoarthritis Biomarkers Consortium (600 knees), we trained multimodal models to predict two progression tasks: (i) joint-space-loss-only progression versus […]

Ver mais

Federated learning (FL) is an effective paradigm for distributed environments such as the Internet of Things (IoT), where data from diverse devices with varying functionalities remains localized while contributing to a shared global model. By eliminating the need to transmit raw data, FL inherently preserves privacy. However, the heterogeneous nature of IoT data, stemming from differences in device capabilities, data formats, and communication constraints, poses significant challenges to maintaining both global model performance and privacy. In the context […]

Ver mais

arXiv:2602.23553v1 Announce Type: new Abstract: Neuro-symbolic approaches to long-form video question answering (LVQA) have demonstrated significant accuracy improvements by grounding temporal reasoning in formal verification. However, existing methods incur prohibitive latency overheads, up to 90x slower than base VLM prompting, rendering them impractical for latency-sensitive edge deployments. We present LE-NeuS, a latency-efficient neuro-symbolic framework that preserves the accuracy benefits of temporal logic-guided video understanding while drastically reducing inference latency. Our key insight is that the dominant computational bottleneck […]

Ver mais