Multi-Objective Alignment of Language Models for Personalized Psychotherapy
arXiv:2602.16053v1 Announce Type: new Abstract: Mental health disorders affect over 1 billion people worldwide, yet access to care remains limited by workforce shortages and cost constraints. While AI systems show therapeutic promise, current alignment approaches optimize objectives independently, failing to...
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...
ASPEN: Spectral-Temporal Fusion for Cross-Subject Brain Decoding
arXiv:2602.16147v1 Announce Type: new Abstract: Cross-subject generalization in EEG-based brain-computer interfaces (BCIs) remains challenging due to individual variability in neural signals. We investigate whether spectral representations offer more stable features for cross-subject transfer than temporal waveforms. Through correlation analyses across...
Differentially Private Non-convex Distributionally Robust Optimization
arXiv:2602.16155v1 Announce Type: new Abstract: Real-world deployments routinely face distribution shifts, group imbalances, and adversarial perturbations, under which the traditional Empirical Risk Minimization (ERM) framework can degrade severely. Distributionally Robust Optimization (DRO) addresses this issue by optimizing the worst-case expected...
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...
Muon with Spectral Guidance: Efficient Optimization for Scientific Machine Learning
arXiv:2602.16167v1 Announce Type: new Abstract: Physics-informed neural networks and neural operators often suffer from severe optimization difficulties caused by ill-conditioned gradients, multi-scale spectral behavior, and stiffness induced by physical constraints. Recently, the Muon optimizer has shown promise by performing orthogonalized...
Deep TPC: Temporal-Prior Conditioning for Time Series Forecasting
arXiv:2602.16188v1 Announce Type: new Abstract: LLM-for-time series (TS) methods typically treat time shallowly, injecting positional or prompt-based cues once at the input of a largely frozen decoder, which limits temporal reasoning as this information degrades through the layers. We introduce...
Rethinking Input Domains in Physics-Informed Neural Networks via Geometric Compactification Mappings
arXiv:2602.16193v1 Announce Type: new Abstract: Several complex physical systems are governed by multi-scale partial differential equations (PDEs) that exhibit both smooth low-frequency components and localized high-frequency structures. Existing physics-informed neural network (PINN) methods typically train with fixed coordinate system inputs,...
ModalImmune: Immunity Driven Unlearning via Self Destructive Training
arXiv:2602.16197v1 Announce Type: new Abstract: Multimodal systems are vulnerable to partial or complete loss of input channels at deployment, which undermines reliability in real-world settings. This paper presents ModalImmune, a training framework that enforces modality immunity by intentionally and controllably...
Geometric Neural Operators via Lie Group-Constrained Latent Dynamics
arXiv:2602.16209v1 Announce Type: new Abstract: Neural operators offer an effective framework for learning solutions of partial differential equations for many physical systems in a resolution-invariant and data-driven manner. Existing neural operators, however, often suffer from instability in multi-layer iteration and...
Graph neural network for colliding particles with an application to sea ice floe modeling
arXiv:2602.16213v1 Announce Type: new Abstract: This paper introduces a novel approach to sea ice modeling using Graph Neural Networks (GNNs), utilizing the natural graph structure of sea ice, where nodes represent individual ice pieces, and edges model the physical interactions,...
Bayesian Quadrature: Gaussian Processes for Integration
arXiv:2602.16218v1 Announce Type: new Abstract: Bayesian quadrature is a probabilistic, model-based approach to numerical integration, the estimation of intractable integrals, or expectations. Although Bayesian quadrature was popularised already in the 1980s, no systematic and comprehensive treatment has been published. The...
SEMixer: Semantics Enhanced MLP-Mixer for Multiscale Mixing and Long-term Time Series Forecasting
arXiv:2602.16220v1 Announce Type: new Abstract: Modeling multiscale patterns is crucial for long-term time series forecasting (TSF). However, redundancy and noise in time series, together with semantic gaps between non-adjacent scales, make the efficient alignment and integration of multi-scale temporal dependencies...
Factored Latent Action World Models
arXiv:2602.16229v1 Announce Type: new Abstract: Learning latent actions from action-free video has emerged as a powerful paradigm for scaling up controllable world model learning. Latent actions provide a natural interface for users to iteratively generate and manipulate videos. However, most...
What the Justice Department overlooks in its historical argument to end birthright citizenship
Immigration Matters is a recurring series by César Cuauhtémoc García Hernández that analyzes the court’s immigration docket, highlighting emerging legal questions about new policy and enforcement practices. In my last […]The postWhat the Justice Department overlooks in its historical argument...
“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...
OpenAI deepens India push with Pine Labs fintech partnership
OpenAI moves beyond ChatGPT in India with a Pine Labs deal targeting enterprise payments and AI-driven commerce.
Avey-B
arXiv:2602.15814v1 Announce Type: new Abstract: Compact pretrained bidirectional encoders remain the backbone of industrial NLP under tight compute and memory budgets. Their effectiveness stems from self-attention's ability to deliver high-quality bidirectional contextualization with sequence-level parallelism, as popularized by BERT-style architectures....
Colosseum: Auditing Collusion in Cooperative Multi-Agent Systems
arXiv:2602.15198v1 Announce Type: cross Abstract: Multi-agent systems, where LLM agents communicate through free-form language, enable sophisticated coordination for solving complex cooperative tasks. This surfaces a unique safety problem when individual agents form a coalition and \emph{collude} to pursue secondary goals...
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...
The Information Geometry of Softmax: Probing and Steering
arXiv:2602.15293v1 Announce Type: cross Abstract: This paper concerns the question of how AI systems encode semantic structure into the geometric structure of their representation spaces. The motivating observation of this paper is that the natural geometry of these representation spaces...
Near-Optimal Sample Complexity for Online Constrained MDPs
arXiv:2602.15076v1 Announce Type: new Abstract: Safety is a fundamental challenge in reinforcement learning (RL), particularly in real-world applications such as autonomous driving, robotics, and healthcare. To address this, Constrained Markov Decision Processes (CMDPs) are commonly used to enforce safety constraints...
Hybrid Feature Learning with Time Series Embeddings for Equipment Anomaly Prediction
arXiv:2602.15089v1 Announce Type: new Abstract: In predictive maintenance of equipment, deep learning-based time series anomaly detection has garnered significant attention; however, pure deep learning approaches often fail to achieve sufficient accuracy on real-world data. This study proposes a hybrid approach...
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...
MAVRL: Learning Reward Functions from Multiple Feedback Types with Amortized Variational Inference
arXiv:2602.15206v1 Announce Type: new Abstract: Reward learning typically relies on a single feedback type or combines multiple feedback types using manually weighted loss terms. Currently, it remains unclear how to jointly learn reward functions from heterogeneous feedback types such as...
BindCLIP: A Unified Contrastive-Generative Representation Learning Framework for Virtual Screening
arXiv:2602.15236v1 Announce Type: new Abstract: Virtual screening aims to efficiently identify active ligands from massive chemical libraries for a given target pocket. Recent CLIP-style models such as DrugCLIP enable scalable virtual screening by embedding pockets and ligands into a shared...
Closing the Distribution Gap in Adversarial Training for LLMs
arXiv:2602.15238v1 Announce Type: new Abstract: Adversarial training for LLMs is one of the most promising methods to reliably improve robustness against adversaries. However, despite significant progress, models remain vulnerable to simple in-distribution exploits, such as rewriting prompts in the past...
Size Transferability of Graph Transformers with Convolutional Positional Encodings
arXiv:2602.15239v1 Announce Type: new Abstract: Transformers have achieved remarkable success across domains, motivating the rise of Graph Transformers (GTs) as attention-based architectures for graph-structured data. A key design choice in GTs is the use of Graph Neural Network (GNN)-based positional...