TENG-BC: Unified Time-Evolving Natural Gradient for Neural PDE Solvers with General Boundary Conditions
arXiv:2603.00397v1 Announce Type: new Abstract: Accurately solving time-dependent partial differential equations (PDEs) with neural networks remains challenging due to long-time error accumulation and the difficulty of enforcing general boundary conditions. We introduce TENG-BC, a high-precision neural PDE solver based on...
USE: Uncertainty Structure Estimation for Robust Semi-Supervised Learning
arXiv:2603.00404v1 Announce Type: new Abstract: In this study, a novel idea, Uncertainty Structure Estimation (USE), a lightweight, algorithm-agnostic procedure that emphasizes the often-overlooked role of unlabeled data quality is introduced for Semi-supervised learning (SSL). SSL has achieved impressive progress, but...
Exact and Asymptotically Complete Robust Verifications of Neural Networks via Quantum Optimization
arXiv:2603.00408v1 Announce Type: new Abstract: Deep neural networks (DNNs) enable high performance across domains but remain vulnerable to adversarial perturbations, limiting their use in safety-critical settings. Here, we introduce two quantum-optimization-based models for robust verification that reduce the combinatorial burden...
Physics-Aware Learnability: From Set-Theoretic Independence to Operational Constraints
arXiv:2603.00417v1 Announce Type: new Abstract: Beyond binary classification, learnability can become a logically fragile notion: in EMX, even the class of all finite subsets of $[0,1]$ is learnable in some models of ZFC and not in others. We argue the...
ROKA: Robust Knowledge Unlearning against Adversaries
arXiv:2603.00436v1 Announce Type: new Abstract: The need for machine unlearning is critical for data privacy, yet existing methods often cause Knowledge Contamination by unintentionally damaging related knowledge. Such a degraded model performance after unlearning has been recently leveraged for new...
Rooted Absorbed Prefix Trajectory Balance with Submodular Replay for GFlowNet Training
arXiv:2603.00454v1 Announce Type: new Abstract: Generative Flow Networks (GFlowNets) enable fine-tuning large language models to approximate reward-proportional posteriors, but they remain prone to mode collapse, manifesting as prefix collapse and length bias. We attribute this to two factors: (i) weak...
Benchmarking Few-shot Transferability of Pre-trained Models with Improved Evaluation Protocols
arXiv:2603.00478v1 Announce Type: new Abstract: Few-shot transfer has been revolutionized by stronger pre-trained models and improved adaptation algorithms.However, there lacks a unified, rigorous evaluation protocol that is both challenging and realistic for real-world usage. In this work, we establish FEWTRANS,...
Analyzing Physical Adversarial Example Threats to Machine Learning in Election Systems
arXiv:2603.00481v1 Announce Type: new Abstract: Developments in the machine learning voting domain have shown both promising results and risks. Trained models perform well on ballot classification tasks (> 99% accuracy) but are at risk from adversarial example attacks that cause...
Dynamic Spatio-Temporal Graph Neural Network for Early Detection of Pornography Addiction in Adolescents Based on Electroencephalogram Signals
arXiv:2603.00488v1 Announce Type: new Abstract: Adolescent pornography addiction requires early detection based on objective neurobiological biomarkers because self-report is prone to subjective bias due to social stigma. Conventional machine learning has not been able to model dynamic functional connectivity of...
Déjà vu all over again
The Relist Watch column examines cert petitions that the Supreme Court has “relisted” for its upcoming conference. A short explanation of relists is available here. The Supreme Court is continuing to […]The postDéjà vu all over againappeared first onSCOTUSblog.
The UK Supreme Court
Welcome to SCOUTSblog’s newest recurring series, in which we interview experts on different supreme courts around the world and how they compare to our own. For our debut column, we […]The postThe UK Supreme Courtappeared first onSCOTUSblog.
The justices’ troubling message to lower courts
Civil Rights and Wrongs is a recurring series by Daniel Harawa covering criminal justice and civil rights cases before the court. In two recent decisions, the Supreme Court summarily reversed […]The postThe justices’ troubling message to lower courtsappeared first onSCOTUSblog.
SCOTUStoday for Tuesday, March 3
As we’ve noted before, we read a lot of legal news in the process of preparing this newsletter. Here’s a headline we saw recently that we won’t soon forget: References […]The postSCOTUStoday for Tuesday, March 3appeared first onSCOTUSblog.
Episode 41: Reading Recommendations - EJIL: The Podcast!
Episode 41: Thinking through Rupture in International Economic Law: Views from Latin America - EJIL: The Podcast!
Cybersecurity’s Role in Securing Elections
SPEAKERS: Professor Chris Hoofnagle, Beth Calley, Lucy Huang Podcast Transcript: [Lucy Huang] 00:07 Hello and welcome to the Berkeley Technology Law Journal podcast. My name is Lucy Huang and I am one of the senior editors of the podcast. Today,...
France or Spain or Germany or France: A Neural Account of Non-Redundant Redundant Disjunctions
arXiv:2602.23547v1 Announce Type: new Abstract: Sentences like "She will go to France or Spain, or perhaps to Germany or France." appear formally redundant, yet become acceptable in contexts such as "Mary will go to a philosophy program in France or...
Multi-Agent Causal Reasoning for Suicide Ideation Detection Through Online Conversations
arXiv:2602.23577v1 Announce Type: new Abstract: Suicide remains a pressing global public health concern. While social media platforms offer opportunities for early risk detection through online conversation trees, existing approaches face two major limitations: (1) They rely on predefined rules (e.g.,...
BRIDGE the Gap: Mitigating Bias Amplification in Automated Scoring of English Language Learners via Inter-group Data Augmentation
arXiv:2602.23580v1 Announce Type: new Abstract: In the field of educational assessment, automated scoring systems increasingly rely on deep learning and large language models (LLMs). However, these systems face significant risks of bias amplification, where model prediction gaps between student groups...
LFQA-HP-1M: A Large-Scale Human Preference Dataset for Long-Form Question Answering
arXiv:2602.23603v1 Announce Type: new Abstract: Long-form question answering (LFQA) demands nuanced evaluation of multi-sentence explanatory responses, yet existing metrics often fail to reflect human judgment. We present LFQA-HP-1M, a large-scale dataset comprising 1.3M human pairwise preference annotations for LFQA. We...
TRIZ-RAGNER: A Retrieval-Augmented Large Language Model for TRIZ-Aware Named Entity Recognition in Patent-Based Contradiction Mining
arXiv:2602.23656v1 Announce Type: new Abstract: TRIZ-based contradiction mining is a fundamental task in patent analysis and systematic innovation, as it enables the identification of improving and worsening technical parameters that drive inventive problem solving. However, existing approaches largely rely on...
From Static Benchmarks to Dynamic Protocol: Agent-Centric Text Anomaly Detection for Evaluating LLM Reasoning
arXiv:2602.23729v1 Announce Type: new Abstract: The evaluation of large language models (LLMs) has predominantly relied on static datasets, which offer limited scalability and fail to capture the evolving reasoning capabilities of recent models. To overcome these limitations, we propose an...
Structured Prompt Optimization for Few-Shot Text Classification via Semantic Alignment in Latent Space
arXiv:2602.23753v1 Announce Type: new Abstract: This study addresses the issues of semantic entanglement, unclear label structure, and insufficient feature representation in few-shot text classification, and proposes an optimization framework based on structured prompts to enhance semantic understanding and task adaptation...
GLUScope: A Tool for Analyzing GLU Neurons in Transformer Language Models
arXiv:2602.23826v1 Announce Type: new Abstract: We present GLUScope, an open-source tool for analyzing neurons in Transformer-based language models, intended for interpretability researchers. We focus on more recent models than previous tools do; specifically we consider gated activation functions such as...
The Astonishing Ability of Large Language Models to Parse Jabberwockified Language
arXiv:2602.23928v1 Announce Type: new Abstract: We show that large language models (LLMs) have an astonishing ability to recover meaning from severely degraded English texts. Texts in which content words have been randomly substituted by nonsense strings, e.g., "At the ghybe...
EDDA-Coordinata: An Annotated Dataset of Historical Geographic Coordinates
arXiv:2602.23941v1 Announce Type: new Abstract: This paper introduces a dataset of enriched geographic coordinates retrieved from Diderot and d'Alembert's eighteenth-century Encyclopedie. Automatically recovering geographic coordinates from historical texts is a complex task, as they are expressed in a variety of...
MemEmo: Evaluating Emotion in Memory Systems of Agents
arXiv:2602.23944v1 Announce Type: new Abstract: Memory systems address the challenge of context loss in Large Language Model during prolonged interactions. However, compared to human cognition, the efficacy of these systems in processing emotion-related information remains inconclusive. To address this gap,...
The GRADIEND Python Package: An End-to-End System for Gradient-Based Feature Learning
arXiv:2602.23993v1 Announce Type: new Abstract: We present gradiend, an open-source Python package that operationalizes the GRADIEND method for learning feature directions from factual-counterfactual MLM and CLM gradients in language models. The package provides a unified workflow for feature-related data creation,...
Task Complexity Matters: An Empirical Study of Reasoning in LLMs for Sentiment Analysis
arXiv:2602.24060v1 Announce Type: new Abstract: Large language models (LLMs) with reasoning capabilities have fueled a compelling narrative that reasoning universally improves performance across language tasks. We test this claim through a comprehensive evaluation of 504 configurations across seven model families--including...
HiDrop: Hierarchical Vision Token Reduction in MLLMs via Late Injection, Concave Pyramid Pruning, and Early Exit
arXiv:2602.23699v1 Announce Type: cross Abstract: The quadratic computational cost of processing vision tokens in Multimodal Large Language Models (MLLMs) hinders their widespread adoption. While progressive vision token pruning offers a promising solution, current methods misinterpret shallow layer functions and use...