FedAvg-Based CTMC Hazard Model for Federated Bridge Deterioration Assessment
arXiv:2602.20194v1 Announce Type: new Abstract: Bridge periodic inspection records contain sensitive information about public infrastructure, making cross-organizational data sharing impractical under existing data governance constraints. We propose a federated framework for estimating a Continuous-Time Markov Chain (CTMC) hazard model of...
Uncertainty-Aware Delivery Delay Duration Prediction via Multi-Task Deep Learning
arXiv:2602.20271v1 Announce Type: new Abstract: Accurate delivery delay prediction is critical for maintaining operational efficiency and customer satisfaction across modern supply chains. Yet the increasing complexity of logistics networks, spanning multimodal transportation, cross-country routing, and pronounced regional variability, makes this...
cc-Shapley: Measuring Multivariate Feature Importance Needs Causal Context
arXiv:2602.20396v1 Announce Type: new Abstract: Explainable artificial intelligence promises to yield insights into relevant features, thereby enabling humans to examine and scrutinize machine learning models or even facilitating scientific discovery. Considering the widespread technique of Shapley values, we find that...
GeoPT: Scaling Physics Simulation via Lifted Geometric Pre-Training
arXiv:2602.20399v1 Announce Type: new Abstract: Neural simulators promise efficient surrogates for physics simulation, but scaling them is bottlenecked by the prohibitive cost of generating high-fidelity training data. Pre-training on abundant off-the-shelf geometries offers a natural alternative, yet faces a fundamental...
CGSTA: Cross-Scale Graph Contrast with Stability-Aware Alignment for Multivariate Time-Series Anomaly Detection
arXiv:2602.20468v1 Announce Type: new Abstract: Multivariate time-series anomaly detection is essential for reliable industrial control, telemetry, and service monitoring. However, the evolving inter-variable dependencies and inevitable noise render it challenging. Existing methods often use single-scale graphs or instance-level contrast. Moreover,...
Memory-guided Prototypical Co-occurrence Learning for Mixed Emotion Recognition
arXiv:2602.20530v1 Announce Type: new Abstract: Emotion recognition from multi-modal physiological and behavioral signals plays a pivotal role in affective computing, yet most existing models remain constrained to the prediction of singular emotions in controlled laboratory settings. Real-world human emotional experiences,...
Sample-efficient evidence estimation of score based priors for model selection
arXiv:2602.20549v1 Announce Type: new Abstract: The choice of prior is central to solving ill-posed imaging inverse problems, making it essential to select one consistent with the measurements $y$ to avoid severe bias. In Bayesian inverse problems, this could be achieved...
GENSR: Symbolic Regression Based in Equation Generative Space
arXiv:2602.20557v1 Announce Type: new Abstract: Symbolic Regression (SR) tries to reveal the hidden equations behind observed data. However, most methods search within a discrete equation space, where the structural modifications of equations rarely align with their numerical behavior, leaving fitting...
Benchmarking GNN Models on Molecular Regression Tasks with CKA-Based Representation Analysis
arXiv:2602.20573v1 Announce Type: new Abstract: Molecules are commonly represented as SMILES strings, which can be readily converted to fixed-size molecular fingerprints. These fingerprints serve as feature vectors to train ML/DL models for molecular property prediction tasks in the field of...
Justices reveal little about whether the deadline for removing cases to federal court can be excused
When a plaintiff files a lawsuit in state court asserting a claim that could be brought in federal court, federal law gives the defendant 30 days to remove the case […]The postJustices reveal little about whether the deadline for removing...
Musk has no proof OpenAI stole xAI trade secrets, judge rules, tossing lawsuit
Even twisting an ex-employee's text to favor xAI's reading fails to sway judge.
About 12% of US teens turn to AI for emotional support or advice
General-purpose tools like ChatGPT, Claude, and Grok are not designed for this use, making mental health professionals wary.
Do LLMs and VLMs Share Neurons for Inference? Evidence and Mechanisms of Cross-Modal Transfer
arXiv:2602.19058v1 Announce Type: new Abstract: Large vision-language models (LVLMs) have rapidly advanced across various domains, yet they still lag behind strong text-only large language models (LLMs) on tasks that require multi-step inference and compositional decision-making. Motivated by their shared transformer...
Astra: Activation-Space Tail-Eigenvector Low-Rank Adaptation of Large Language Models
arXiv:2602.19111v1 Announce Type: new Abstract: Parameter-Efficient Fine-Tuning (PEFT) methods, especially LoRA, are widely used for adapting pre-trained models to downstream tasks due to their computational and storage efficiency. However, in the context of LoRA and its variants, the potential of...
How Do LLMs Encode Scientific Quality? An Empirical Study Using Monosemantic Features from Sparse Autoencoders
arXiv:2602.19115v1 Announce Type: new Abstract: In recent years, there has been a growing use of generative AI, and large language models (LLMs) in particular, to support both the assessment and generation of scientific work. Although some studies have shown that...
Learning to Reason for Multi-Step Retrieval of Personal Context in Personalized Question Answering
arXiv:2602.19317v1 Announce Type: new Abstract: Personalization in Question Answering (QA) requires answers that are both accurate and aligned with users' background, preferences, and historical context. Existing state-of-the-art methods primarily rely on retrieval-augmented generation (RAG) solutions that construct personal context by...
Beyond a Single Extractor: Re-thinking HTML-to-Text Extraction for LLM Pretraining
arXiv:2602.19548v1 Announce Type: new Abstract: One of the first pre-processing steps for constructing web-scale LLM pretraining datasets involves extracting text from HTML. Despite the immense diversity of web content, existing open-source datasets predominantly apply a single fixed extractor to all...
DEEP: Docker-based Execution and Evaluation Platform
arXiv:2602.19583v1 Announce Type: new Abstract: Comparative evaluation of several systems is a recurrent task in researching. It is a key step before deciding which system to use for our work, or, once our research has been conducted, to demonstrate the...
Learning to Remember: End-to-End Training of Memory Agents for Long-Context Reasoning
arXiv:2602.18493v1 Announce Type: new Abstract: Long-context LLMs and Retrieval-Augmented Generation (RAG) systems process information passively, deferring state tracking, contradiction resolution, and evidence aggregation to query time, which becomes brittle under ultra long streams with frequent updates. We propose the Unified...
Weak-Form Evolutionary Kolmogorov-Arnold Networks for Solving Partial Differential Equations
arXiv:2602.18515v1 Announce Type: new Abstract: Partial differential equations (PDEs) form a central component of scientific computing. Among recent advances in deep learning, evolutionary neural networks have been developed to successively capture the temporal dynamics of time-dependent PDEs via parameter evolution....
Learning Beyond Optimization: Stress-Gated Dynamical Regime Regulation in Autonomous Systems
arXiv:2602.18581v1 Announce Type: new Abstract: Despite their apparent diversity, modern machine learning methods can be reduced to a remarkably simple core principle: learning is achieved by continuously optimizing parameters to minimize or maximize a scalar objective function. This paradigm has...
Diagnosing LLM Reranker Behavior Under Fixed Evidence Pools
arXiv:2602.18613v1 Announce Type: new Abstract: Standard reranking evaluations study how a reranker orders candidates returned by an upstream retriever. This setup couples ranking behavior with retrieval quality, so differences in output cannot be attributed to the ranking policy alone. We...
Adaptive Time Series Reasoning via Segment Selection
arXiv:2602.18645v1 Announce Type: new Abstract: Time series reasoning tasks often start with a natural language question and require targeted analysis of a time series. Evidence may span the full series or appear in a few short intervals, so the model...
Global Low-Rank, Local Full-Rank: The Holographic Encoding of Learned Algorithms
arXiv:2602.18649v1 Announce Type: new Abstract: Grokking -- the abrupt transition from memorization to generalization after extended training -- has been linked to the emergence of low-dimensional structure in learning dynamics. Yet neural network parameters inhabit extremely high-dimensional spaces. How can...
Large Causal Models for Temporal Causal Discovery
arXiv:2602.18662v1 Announce Type: new Abstract: Causal discovery for both cross-sectional and temporal data has traditionally followed a dataset-specific paradigm, where a new model is fitted for each individual dataset. Such an approach limits the potential of multi-dataset pretraining. The concept...
Transformers for dynamical systems learn transfer operators in-context
arXiv:2602.18679v1 Announce Type: new Abstract: Large-scale foundation models for scientific machine learning adapt to physical settings unseen during training, such as zero-shot transfer between turbulent scales. This phenomenon, in-context learning, challenges conventional understanding of learning and adaptation in physical systems....
Issues with Measuring Task Complexity via Random Policies in Robotic Tasks
arXiv:2602.18856v1 Announce Type: new Abstract: Reinforcement learning (RL) has enabled major advances in fields such as robotics and natural language processing. A key challenge in RL is measuring task complexity, which is essential for creating meaningful benchmarks and designing effective...
Boosting for Vector-Valued Prediction and Conditional Density Estimation
arXiv:2602.18866v1 Announce Type: new Abstract: Despite the widespread use of boosting in structured prediction, a general theoretical understanding of aggregation beyond scalar losses remains incomplete. We study vector-valued and conditional density prediction under general divergences and identify stability conditions under...
Standing in and after Bost
Controlling Opinions is a recurring series by Richard Re that explores the interaction of law, ideology, and discretion at the Supreme Court. The Supreme Court’s recent decision in Bost v. […]The postStanding in and after Bostappeared first onSCOTUSblog.
DJI sues the FCC for “carelessly” restricting its drones
DJI lawsuit says company has been "severely harmed by the FCC’s ruling."