Construction of a classification model for dementia among Brazilian adults aged 50 and over
arXiv:2602.16887v1 Announce Type: new Abstract: To build a dementia classification model for middle-aged and elderly Brazilians, implemented in Python, combining variable selection and multivariable analysis, using low-cost variables with modification potential. Observational study with a predictive modeling approach using a...
Neural Proposals, Symbolic Guarantees: Neuro-Symbolic Graph Generation with Hard Constraints
arXiv:2602.16954v1 Announce Type: new Abstract: We challenge black-box purely deep neural approaches for molecules and graph generation, which are limited in controllability and lack formal guarantees. We introduce Neuro-Symbolic Graph Generative Modeling (NSGGM), a neurosymbolic framework that reapproaches molecule generation...
A Unified Framework for Locality in Scalable MARL
arXiv:2602.16966v1 Announce Type: new Abstract: Scalable Multi-Agent Reinforcement Learning (MARL) is fundamentally challenged by the curse of dimensionality. A common solution is to exploit locality, which hinges on an Exponential Decay Property (EDP) of the value function. However, existing conditions...
Malliavin Calculus as Stochastic Backpropogation
arXiv:2602.17013v1 Announce Type: new Abstract: We establish a rigorous connection between pathwise (reparameterization) and score-function (Malliavin) gradient estimators by showing that both arise from the Malliavin integration-by-parts identity. Building on this equivalence, we introduce a unified and variance-aware hybrid estimator...
Forecasting Anomaly Precursors via Uncertainty-Aware Time-Series Ensembles
arXiv:2602.17028v1 Announce Type: new Abstract: Detecting anomalies in time-series data is critical in domains such as industrial operations, finance, and cybersecurity, where early identification of abnormal patterns is essential for ensuring system reliability and enabling preventive maintenance. However, most existing...
AdvSynGNN: Structure-Adaptive Graph Neural Nets via Adversarial Synthesis and Self-Corrective Propagation
arXiv:2602.17071v1 Announce Type: new Abstract: Graph neural networks frequently encounter significant performance degradation when confronted with structural noise or non-homophilous topologies. To address these systemic vulnerabilities, we present AdvSynGNN, a comprehensive architecture designed for resilient node-level representation learning. The proposed...
FLoRG: Federated Fine-tuning with Low-rank Gram Matrices and Procrustes Alignment
arXiv:2602.17095v1 Announce Type: new Abstract: Parameter-efficient fine-tuning techniques such as low-rank adaptation (LoRA) enable large language models (LLMs) to adapt to downstream tasks efficiently. Federated learning (FL) further facilitates this process by enabling collaborative fine-tuning across distributed clients without sharing...
AI-Driven Legal Automation to Enhance Legal Processes with Natural Language Processing
The legal sector often faces delays and inefficiencies due to the overwhelming volume of information, the labor-intensive nature of research, and high service costs. This paper introduces a novel framework for AI-driven legal automation, which employs Natural Language Processing (NLP)...
Justices to consider constitutionality of tax foreclosure sales
The argument next week in Pung v Isabella County asks the court to consider the constitutionality of the longstanding practice of tax foreclosures sales. This is one of those cases […]The postJustices to consider constitutionality of tax foreclosure salesappeared first...
From Transcripts to AI Agents: Knowledge Extraction, RAG Integration, and Robust Evaluation of Conversational AI Assistants
arXiv:2602.15859v1 Announce Type: new Abstract: Building reliable conversational AI assistants for customer-facing industries remains challenging due to noisy conversational data, fragmented knowledge, and the requirement for accurate human hand-off - particularly in domains that depend heavily on real-time information. This...
Reranker Optimization via Geodesic Distances on k-NN Manifolds
arXiv:2602.15860v1 Announce Type: new Abstract: Current neural reranking approaches for retrieval-augmented generation (RAG) rely on cross-encoders or large language models (LLMs), requiring substantial computational resources and exhibiting latencies of 3-5 seconds per query. We propose Maniscope, a geometric reranking method...
Towards Fair and Efficient De-identification: Quantifying the Efficiency and Generalizability of De-identification Approaches
arXiv:2602.15869v1 Announce Type: new Abstract: Large language models (LLMs) have shown strong performance on clinical de-identification, the task of identifying sensitive identifiers to protect privacy. However, previous work has not examined their generalizability between formats, cultures, and genders. In this...
DocSplit: A Comprehensive Benchmark Dataset and Evaluation Approach for Document Packet Recognition and Splitting
arXiv:2602.15958v1 Announce Type: new Abstract: Document understanding in real-world applications often requires processing heterogeneous, multi-page document packets containing multiple documents stitched together. Despite recent advances in visual document understanding, the fundamental task of document packet splitting, which involves separating a...
A Curious Class of Adpositional Multiword Expressions in Korean
arXiv:2602.16023v1 Announce Type: new Abstract: Multiword expressions (MWEs) have been widely studied in cross-lingual annotation frameworks such as PARSEME. However, Korean MWEs remain underrepresented in these efforts. In particular, Korean multiword adpositions lack systematic analysis, annotated resources, and integration into...
CLAA: Cross-Layer Attention Aggregation for Accelerating LLM Prefill
arXiv:2602.16054v1 Announce Type: new Abstract: The prefill stage in long-context LLM inference remains a computational bottleneck. Recent token-ranking heuristics accelerate inference by selectively processing a subset of semantically relevant tokens. However, existing methods suffer from unstable token importance estimation, often...
Distributed physics-informed neural networks via domain decomposition for fast flow reconstruction
arXiv:2602.15883v1 Announce Type: new Abstract: Physics-Informed Neural Networks (PINNs) offer a powerful paradigm for flow reconstruction, seamlessly integrating sparse velocity measurements with the governing Navier-Stokes equations to recover complete velocity and latent pressure fields. However, scaling such models to large...
MolCrystalFlow: Molecular Crystal Structure Prediction via Flow Matching
arXiv:2602.16020v1 Announce Type: new Abstract: Molecular crystal structure prediction represents a grand challenge in computational chemistry due to large sizes of constituent molecules and complex intra- and intermolecular interactions. While generative modeling has revolutionized structure discovery for molecules, inorganic solids,...
Omni-iEEG: A Large-Scale, Comprehensive iEEG Dataset and Benchmark for Epilepsy Research
arXiv:2602.16072v1 Announce Type: new Abstract: Epilepsy affects over 50 million people worldwide, and one-third of patients suffer drug-resistant seizures where surgery offers the best chance of seizure freedom. Accurate localization of the epileptogenic zone (EZ) relies on intracranial EEG (iEEG)....
Feature-based morphological analysis of shape graph data
arXiv:2602.16120v1 Announce Type: new Abstract: This paper introduces and demonstrates a computational pipeline for the statistical analysis of shape graph datasets, namely geometric networks embedded in 2D or 3D spaces. Unlike traditional abstract graphs, our purpose is not only to...
HiPER: Hierarchical Reinforcement Learning with Explicit Credit Assignment for Large Language Model Agents
arXiv:2602.16165v1 Announce Type: new Abstract: Training LLMs as interactive agents for multi-turn decision-making remains challenging, particularly in long-horizon tasks with sparse and delayed rewards, where agents must execute extended sequences of actions before receiving meaningful feedback. Most existing reinforcement learning...
“Open & Close Strategy”: How Japanese Tech Companies with Niche Technologies Can Leverage IP for Competitive Advantage
Tomotaka Hosokawa, LL.M. Class of 2026 The Strategy The “Open & Close Strategy” refers to a business and intellectual property strategy where a Japanese technology company intentionally “opens” specific technologies to expand the market while simultaneously “closing” other technologies to...
Seeing to Generalize: How Visual Data Corrects Binding Shortcuts
arXiv:2602.15183v1 Announce Type: cross Abstract: Vision Language Models (VLMs) are designed to extend Large Language Models (LLMs) with visual capabilities, yet in this work we observe a surprising phenomenon: VLMs can outperform their underlying LLMs on purely text-only tasks, particularly...
How to Train Your Long-Context Visual Document Model
arXiv:2602.15257v1 Announce Type: cross Abstract: We present the first comprehensive, large-scale study of training long-context vision language models up to 344K context, targeting long-document visual question answering with measured transfer to long-context text. While several such strong are open-weight, namely...
FrameRef: A Framing Dataset and Simulation Testbed for Modeling Bounded Rational Information Health
arXiv:2602.15273v1 Announce Type: cross Abstract: Information ecosystems increasingly shape how people internalize exposure to adverse digital experiences, raising concerns about the long-term consequences for information health. In modern search and recommendation systems, ranking and personalization policies play a central role...
Prescriptive Scaling Reveals the Evolution of Language Model Capabilities
arXiv:2602.15327v1 Announce Type: cross Abstract: For deploying foundation models, practitioners increasingly need prescriptive scaling laws: given a pre training compute budget, what downstream accuracy is attainable with contemporary post training practice, and how stable is that mapping as the field...
Learning Representations from Incomplete EHR Data with Dual-Masked Autoencoding
arXiv:2602.15159v1 Announce Type: new Abstract: Learning from electronic health records (EHRs) time series is challenging due to irregular sam- pling, heterogeneous missingness, and the resulting sparsity of observations. Prior self-supervised meth- ods either impute before learning, represent missingness through a...
Learning Data-Efficient and Generalizable Neural Operators via Fundamental Physics Knowledge
arXiv:2602.15184v1 Announce Type: new Abstract: Recent advances in scientific machine learning (SciML) have enabled neural operators (NOs) to serve as powerful surrogates for modeling the dynamic evolution of physical systems governed by partial differential equations (PDEs). While existing approaches focus...
Scaling Laws for Masked-Reconstruction Transformers on Single-Cell Transcriptomics
arXiv:2602.15253v1 Announce Type: new Abstract: Neural scaling laws -- power-law relationships between loss, model size, and data -- have been extensively documented for language and vision transformers, yet their existence in single-cell genomics remains largely unexplored. We present the first...
Hybrid Federated and Split Learning for Privacy Preserving Clinical Prediction and Treatment Optimization
arXiv:2602.15304v1 Announce Type: new Abstract: Collaborative clinical decision support is often constrained by governance and privacy rules that prevent pooling patient-level records across institutions. We present a hybrid privacy-preserving framework that combines Federated Learning (FL) and Split Learning (SL) to...
FedPSA: Modeling Behavioral Staleness in Asynchronous Federated Learning
arXiv:2602.15337v1 Announce Type: new Abstract: Asynchronous Federated Learning (AFL) has emerged as a significant research area in recent years. By not waiting for slower clients and executing the training process concurrently, it achieves faster training speed compared to traditional federated...