Contextual Preference Distribution Learning
arXiv:2603.17139v1 Announce Type: new Abstract: Decision-making problems often feature uncertainty stemming from heterogeneous and context-dependent human preferences. To address this, we propose a sequential learning-and-optimization pipeline to learn preference distributions and leverage them to solve downstream problems, for example risk-averse...
REAL: Regression-Aware Reinforcement Learning for LLM-as-a-Judge
arXiv:2603.17145v1 Announce Type: new Abstract: Large language models (LLMs) are increasingly deployed as automated evaluators that assign numeric scores to model outputs, a paradigm known as LLM-as-a-Judge. However, standard Reinforcement Learning (RL) methods typically rely on binary rewards (e.g., 0-1...
Personalized Fall Detection by Balancing Data with Selective Feedback Using Contrastive Learning
arXiv:2603.17148v1 Announce Type: new Abstract: Personalized fall detection models can significantly improve accuracy by adapting to individual motion patterns, yet their effectiveness is often limited by the scarcity of real-world fall data and the dominance of non-fall feedback samples. This...
Noise-Response Calibration: A Causal Intervention Protocol for LLM-Judges
arXiv:2603.17172v1 Announce Type: new Abstract: Large language models (LLMs) are increasingly used as automated judges and synthetic labelers, especially in low-label settings. Yet these systems are stochastic and often overconfident, which makes deployment decisions difficult when external ground truth is...
Domain-informed explainable boosting machines for trustworthy lateral spread predictions
arXiv:2603.17175v1 Announce Type: new Abstract: Explainable Boosting Machines (EBMs) provide transparent predictions through additive shape functions, enabling direct inspection of feature contributions. However, EBMs can learn non-physical relationships that reduce their reliability in natural hazard applications. This study presents a...
MetaClaw: Just Talk -- An Agent That Meta-Learns and Evolves in the Wild
arXiv:2603.17187v1 Announce Type: new Abstract: Large language model (LLM) agents are increasingly used for complex tasks, yet deployed agents often remain static, failing to adapt as user needs evolve. This creates a tension between the need for continuous service and...
Self-Conditioned Denoising for Atomistic Representation Learning
arXiv:2603.17196v1 Announce Type: new Abstract: The success of large-scale pretraining in NLP and computer vision has catalyzed growing efforts to develop analogous foundation models for the physical sciences. However, pretraining strategies using atomistic data remain underexplored. To date, large-scale supervised...
Abstraction as a Memory-Efficient Inductive Bias for Continual Learning
arXiv:2603.17198v1 Announce Type: new Abstract: The real world is non-stationary and infinitely complex, requiring intelligent agents to learn continually without the prohibitive cost of retraining from scratch. While online continual learning offers a framework for this setting, learning new information...
Catching rationalization in the act: detecting motivated reasoning before and after CoT via activation probing
arXiv:2603.17199v1 Announce Type: new Abstract: Large language models (LLMs) can produce chains of thought (CoT) that do not accurately reflect the actual factors driving their answers. In multiple-choice settings with an injected hint favoring a particular option, models may shift...
On the Cone Effect and Modality Gap in Medical Vision-Language Embeddings
arXiv:2603.17246v1 Announce Type: new Abstract: Vision-Language Models (VLMs) exhibit a characteristic "cone effect" in which nonlinear encoders map embeddings into highly concentrated regions of the representation space, contributing to cross-modal separation known as the modality gap. While this phenomenon has...
Binary Latent Protein Fitness Landscapes for Quantum Annealing Optimization
arXiv:2603.17247v1 Announce Type: new Abstract: We propose Q-BIOLAT, a framework for modeling and optimizing protein fitness landscapes in binary latent spaces. Starting from protein sequences, we leverage pretrained protein language models to obtain continuous embeddings, which are then transformed into...
Pathology-Aware Multi-View Contrastive Learning for Patient-Independent ECG Reconstruction
arXiv:2603.17248v1 Announce Type: new Abstract: Reconstructing a 12-lead electrocardiogram (ECG) from a reduced lead set is an ill-posed inverse problem due to anatomical variability. Standard deep learning methods often ignore underlying cardiac pathology losing vital morphology in precordial leads. We...
Variational Rectification Inference for Learning with Noisy Labels
arXiv:2603.17255v1 Announce Type: new Abstract: Label noise has been broadly observed in real-world datasets. To mitigate the negative impact of overfitting to label noise for deep models, effective strategies (\textit{e.g.}, re-weighting, or loss rectification) have been broadly applied in prevailing...
Classifier Pooling for Modern Ordinal Classification
arXiv:2603.17278v1 Announce Type: new Abstract: Ordinal data is widely prevalent in clinical and other domains, yet there is a lack of both modern, machine-learning based methods and publicly available software to address it. In this paper, we present a model-agnostic...
WINFlowNets: Warm-up Integrated Networks Training of Generative Flow Networks for Robotics and Machine Fault Adaptation
arXiv:2603.17301v1 Announce Type: new Abstract: Generative Flow Networks for continuous scenarios (CFlowNets) have shown promise in solving sequential decision-making tasks by learning stochastic policies using a flow and a retrieval network. Despite their demonstrated efficiency compared to state-of-the-art Reinforcement Learning...
Learning Permutation Distributions via Reflected Diffusion on Ranks
arXiv:2603.17353v1 Announce Type: new Abstract: The finite symmetric group S_n provides a natural domain for permutations, yet learning probability distributions on S_n is challenging due to its factorially growing size and discrete, non-Euclidean structure. Recent permutation diffusion methods define forward...
Variational Kernel Design for Internal Noise: Gaussian Chaos Noise, Representation Compatibility, and Reliable Deep Learning
arXiv:2603.17365v1 Announce Type: new Abstract: Internal noise in deep networks is usually inherited from heuristics such as dropout, hard masking, or additive perturbation. We ask two questions: what correlation geometry should internal noise have, and is the implemented perturbation compatible...
Efficient Exploration at Scale
arXiv:2603.17378v1 Announce Type: new Abstract: We develop an online learning algorithm that dramatically improves the data efficiency of reinforcement learning from human feedback (RLHF). Our algorithm incrementally updates reward and language models as choice data is received. The reward model...
Cohomological Obstructions to Global Counterfactuals: A Sheaf-Theoretic Foundation for Generative Causal Models
arXiv:2603.17384v1 Announce Type: new Abstract: Current continuous generative models (e.g., Diffusion Models, Flow Matching) implicitly assume that locally consistent causal mechanisms naturally yield globally coherent counterfactuals. In this paper, we prove that this assumption fails fundamentally when the causal graph...
The Causal Uncertainty Principle: Manifold Tearing and the Topological Limits of Counterfactual Interventions
arXiv:2603.17385v1 Announce Type: new Abstract: Judea Pearl's do-calculus provides a foundation for causal inference, but its translation to continuous generative models remains fraught with geometric challenges. We establish the fundamental limits of such interventions. We define the Counterfactual Event Horizon...
Large-Scale 3D Ground-Motion Synthesis with Physics-Inspired Latent Operator Flow Matching
arXiv:2603.17403v1 Announce Type: new Abstract: Earthquake hazard analysis and design of spatially distributed infrastructure, such as power grids and energy pipeline networks, require scenario-specific ground-motion time histories with realistic frequency content and spatiotemporal coherence. However, producing the large ensembles needed...
Causal Representation Learning on High-Dimensional Data: Benchmarks, Reproducibility, and Evaluation Metrics
arXiv:2603.17405v1 Announce Type: new Abstract: Causal representation learning (CRL) models aim to transform high-dimensional data into a latent space, enabling interventions to generate counterfactual samples or modify existing data based on the causal relationships among latent variables. To facilitate the...
The Phasor Transformer: Resolving Attention Bottlenecks on the Unit Circle
arXiv:2603.17433v1 Announce Type: new Abstract: Transformer models have redefined sequence learning, yet dot-product self-attention introduces a quadratic token-mixing bottleneck for long-context time-series. We introduce the \textbf{Phasor Transformer} block, a phase-native alternative representing sequence states on the unit-circle manifold $S^1$. Each...
Baguan-TS: A Sequence-Native In-Context Learning Model for Time Series Forecasting with Covariates
arXiv:2603.17439v1 Announce Type: new Abstract: Transformers enable in-context learning (ICL) for rapid, gradient-free adaptation in time series forecasting, yet most ICL-style approaches rely on tabularized, hand-crafted features, while end-to-end sequence models lack inference-time adaptation. We bridge this gap with a...
Efficient Soft Actor-Critic with LLM-Based Action-Level Guidance for Continuous Control
arXiv:2603.17468v1 Announce Type: new Abstract: We present GuidedSAC, a novel reinforcement learning (RL) algorithm that facilitates efficient exploration in vast state-action spaces. GuidedSAC leverages large language models (LLMs) as intelligent supervisors that provide action-level guidance for the Soft Actor-Critic (SAC)...
Auto-Unrolled Proximal Gradient Descent: An AutoML Approach to Interpretable Waveform Optimization
arXiv:2603.17478v1 Announce Type: new Abstract: This study explores the combination of automated machine learning (AutoML) with model-based deep unfolding (DU) for optimizing wireless beamforming and waveforms. We convert the iterative proximal gradient descent (PGD) algorithm into a deep neural network,...
QuantFL: Sustainable Federated Learning for Edge IoT via Pre-Trained Model Quantisation
arXiv:2603.17507v1 Announce Type: new Abstract: Federated Learning (FL) enables privacy-preserving intelligence on Internet of Things (IoT) devices but incurs a significant carbon footprint due to the high energy cost of frequent uplink transmission. While pre-trained models are increasingly available on...
Translation Invariance of Neural Operators for the FitzHugh-Nagumo Model
arXiv:2603.17523v1 Announce Type: new Abstract: Neural Operators (NOs) are a powerful deep learning framework designed to learn the solution operator that arise from partial differential equations. This study investigates NOs ability to capture the stiff spatio-temporal dynamics of the FitzHugh-Nagumo...
Supreme Court asylum decision burdens already overworked DOJ
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. Requests for asylum […]The postSupreme Court asylum decision burdens already overworked DOJappeared first...
Court to hear argument in case that could have significant impact on 2026 elections
The Supreme Court will kick off its March argument session by hearing a case that could have major implications for the 2026 elections and beyond. In Watson v. Republican National […]The postCourt to hear argument in case that could have...