We can still parse using syntactic rules
arXiv:2602.14238v1 Announce Type: new Abstract: This research introduces a new parsing approach, based on earlier syntactic work on context free grammar (CFG) and generalized phrase structure grammar (GPSG). The approach comprises both a new parsing algorithm and a set of...
Directional Concentration Uncertainty: A representational approach to uncertainty quantification for generative models
arXiv:2602.13264v1 Announce Type: new Abstract: In the critical task of making generative models trustworthy and robust, methods for Uncertainty Quantification (UQ) have begun to show encouraging potential. However, many of these methods rely on rigid heuristics that fail to generalize...
The Speed-up Factor: A Quantitative Multi-Iteration Active Learning Performance Metric
arXiv:2602.13359v1 Announce Type: new Abstract: Machine learning models excel with abundant annotated data, but annotation is often costly and time-intensive. Active learning (AL) aims to improve the performance-to-annotation ratio by using query methods (QMs) to iteratively select the most informative...
Comparing Classifiers: A Case Study Using PyCM
arXiv:2602.13482v1 Announce Type: new Abstract: Selecting an optimal classification model requires a robust and comprehensive understanding of the performance of the model. This paper provides a tutorial on the PyCM library, demonstrating its utility in conducting deep-dive evaluations of multi-class...
Finding Highly Interpretable Prompt-Specific Circuits in Language Models
arXiv:2602.13483v1 Announce Type: new Abstract: Understanding the internal circuits that language models use to solve tasks remains a central challenge in mechanistic interpretability. Most prior work identifies circuits at the task level by averaging across many prompts, implicitly assuming a...
Federated Learning of Nonlinear Temporal Dynamics with Graph Attention-based Cross-Client Interpretability
arXiv:2602.13485v1 Announce Type: new Abstract: Networks of modern industrial systems are increasingly monitored by distributed sensors, where each system comprises multiple subsystems generating high dimensional time series data. These subsystems are often interdependent, making it important to understand how temporal...
Preventing Rank Collapse in Federated Low-Rank Adaptation with Client Heterogeneity
arXiv:2602.13486v1 Announce Type: new Abstract: Federated low-rank adaptation (FedLoRA) has facilitated communication-efficient and privacy-preserving fine-tuning of foundation models for downstream tasks. In practical federated learning scenarios, client heterogeneity in system resources and data distributions motivates heterogeneous LoRA ranks across clients....
TrasMuon: Trust-Region Adaptive Scaling for Orthogonalized Momentum Optimizers
arXiv:2602.13498v1 Announce Type: new Abstract: Muon-style optimizers leverage Newton-Schulz (NS) iterations to orthogonalize updates, yielding update geometries that often outperform Adam-series methods. However, this orthogonalization discards magnitude information, rendering training sensitive to step-size hyperparameters and vulnerable to high-energy bursts. To...
$\gamma$-weakly $\theta$-up-concavity: Linearizable Non-Convex Optimization with Applications to DR-Submodular and OSS Functions
arXiv:2602.13506v1 Announce Type: new Abstract: Optimizing monotone non-convex functions is a fundamental challenge across machine learning and combinatorial optimization. We introduce and study $\gamma$-weakly $\theta$-up-concavity, a novel first-order condition that characterizes a broad class of such functions. This condition provides...
QuaRK: A Quantum Reservoir Kernel for Time Series Learning
arXiv:2602.13531v1 Announce Type: new Abstract: Quantum reservoir computing offers a promising route for time series learning by modelling sequential data via rich quantum dynamics while the only training required happens at the level of a lightweight classical readout. However, studies...
Scenario-Adaptive MU-MIMO OFDM Semantic Communication With Asymmetric Neural Network
arXiv:2602.13557v1 Announce Type: new Abstract: Semantic Communication (SemCom) has emerged as a promising paradigm for 6G networks, aiming to extract and transmit task-relevant information rather than minimizing bit errors. However, applying SemCom to realistic downlink Multi-User Multi-Input Multi-Output (MU-MIMO) Orthogonal...
Benchmark Leakage Trap: Can We Trust LLM-based Recommendation?
arXiv:2602.13626v1 Announce Type: new Abstract: The expanding integration of Large Language Models (LLMs) into recommender systems poses critical challenges to evaluation reliability. This paper identifies and investigates a previously overlooked issue: benchmark data leakage in LLM-based recommendation. This phenomenon occurs...
Optimization-Free Graph Embedding via Distributional Kernel for Community Detection
arXiv:2602.13634v1 Announce Type: new Abstract: Neighborhood Aggregation Strategy (NAS) is a widely used approach in graph embedding, underpinning both Graph Neural Networks (GNNs) and Weisfeiler-Lehman (WL) methods. However, NAS-based methods are identified to be prone to over-smoothing-the loss of node...
Cumulative Utility Parity for Fair Federated Learning under Intermittent Client Participation
arXiv:2602.13651v1 Announce Type: new Abstract: In real-world federated learning (FL) systems, client participation is intermittent, heterogeneous, and often correlated with data characteristics or resource constraints. Existing fairness approaches in FL primarily focus on equalizing loss or accuracy conditional on participation,...
Zero-Order Optimization for LLM Fine-Tuning via Learnable Direction Sampling
arXiv:2602.13659v1 Announce Type: new Abstract: Fine-tuning large pretrained language models (LLMs) is a cornerstone of modern NLP, yet its growing memory demands (driven by backpropagation and large optimizer States) limit deployment in resource-constrained settings. Zero-order (ZO) methods bypass backpropagation by...
ALMo: Interactive Aim-Limit-Defined, Multi-Objective System for Personalized High-Dose-Rate Brachytherapy Treatment Planning and Visualization for Cervical Cancer
arXiv:2602.13666v1 Announce Type: new Abstract: In complex clinical decision-making, clinicians must often track a variety of competing metrics defined by aim (ideal) and limit (strict) thresholds. Sifting through these high-dimensional tradeoffs to infer the optimal patient-specific strategy is cognitively demanding...
HBVLA: Pushing 1-Bit Post-Training Quantization for Vision-Language-Action Models
arXiv:2602.13710v1 Announce Type: new Abstract: Vision-Language-Action (VLA) models enable instruction-following embodied control, but their large compute and memory footprints hinder deployment on resource-constrained robots and edge platforms. While reducing weights to 1-bit precision through binarization can greatly improve efficiency, existing...
Discrete Double-Bracket Flows for Isotropic-Noise Invariant Eigendecomposition
arXiv:2602.13759v1 Announce Type: new Abstract: We study matrix-free eigendecomposition under a matrix-vector product (MVP) oracle, where each step observes a covariance operator $C_k = C_{sig} + \sigma_k^2 I + E_k$. Standard stochastic approximation methods either use fixed steps that couple...
On Representation Redundancy in Large-Scale Instruction Tuning Data Selection
arXiv:2602.13773v1 Announce Type: new Abstract: Data quality is a crucial factor in large language models training. While prior work has shown that models trained on smaller, high-quality datasets can outperform those trained on much larger but noisy or low-quality corpora,...
Cast-R1: Learning Tool-Augmented Sequential Decision Policies for Time Series Forecasting
arXiv:2602.13802v1 Announce Type: new Abstract: Time series forecasting has long been dominated by model-centric approaches that formulate prediction as a single-pass mapping from historical observations to future values. Despite recent progress, such formulations often struggle in complex and evolving settings,...
Fast Physics-Driven Untrained Network for Highly Nonlinear Inverse Scattering Problems
arXiv:2602.13805v1 Announce Type: new Abstract: Untrained neural networks (UNNs) offer high-fidelity electromagnetic inverse scattering reconstruction but are computationally limited by high-dimensional spatial-domain optimization. We propose a Real-Time Physics-Driven Fourier-Spectral (PDF) solver that achieves sub-second reconstruction through spectral-domain dimensionality reduction. By...
Testing For Distribution Shifts with Conditional Conformal Test Martingales
arXiv:2602.13848v1 Announce Type: new Abstract: We propose a sequential test for detecting arbitrary distribution shifts that allows conformal test martingales (CTMs) to work under a fixed, reference-conditional setting. Existing CTM detectors construct test martingales by continually growing a reference set...
GREPO: A Benchmark for Graph Neural Networks on Repository-Level Bug Localization
arXiv:2602.13921v1 Announce Type: new Abstract: Repository-level bug localization-the task of identifying where code must be modified to fix a bug-is a critical software engineering challenge. Standard Large Language Modles (LLMs) are often unsuitable for this task due to context window...
Why Code, Why Now: Learnability, Computability, and the Real Limits of Machine Learning
arXiv:2602.13934v1 Announce Type: new Abstract: Code generation has progressed more reliably than reinforcement learning, largely because code has an information structure that makes it learnable. Code provides dense, local, verifiable feedback at every token, whereas most reinforcement learning problems do...
A Not Too Collaborative Constitution? Collaboration as Constitutional Value Versus Model
Constitutional scholarship in recent years has seen a proliferation of ‘isms’ – or the rise of constitutional ideas ‘with adjectives’. Beneath the current trend toward ‘adjectival constitutionalism’ also lie different understandings of constitutionalism as a topic, model, mode of change...
Stephen Colbert says CBS forbid interview of Democrat because of FCC threat
Colbert: "I want to assure you this decision is for purely financial reasons."
Apple is reportedly cooking up a trio of AI wearables
As the AI hardware space heats up, the iPhone maker has multiple smart products in development.
Adani pledges $100B to build AI data centers as India seeks bigger role in the global AI race
Adani's plan targets up to 5 gigawatts of capacity, with data centers planned alongside partnerships with Google, Microsoft, and Flipkart.