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...
BLUEPRINT Rebuilding a Legacy: Multimodal Retrieval for Complex Engineering Drawings and Documents
arXiv:2602.13345v1 Announce Type: new Abstract: Decades of engineering drawings and technical records remain locked in legacy archives with inconsistent or missing metadata, making retrieval difficult and often manual. We present Blueprint, a layout-aware multimodal retrieval system designed for large-scale engineering...
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...
Accelerated Discovery of Cryoprotectant Cocktails via Multi-Objective Bayesian Optimization
arXiv:2602.13398v1 Announce Type: new Abstract: Designing cryoprotectant agent (CPA) cocktails for vitrification is challenging because formulations must be concentrated enough to suppress ice formation yet non-toxic enough to preserve cell viability. This tradeoff creates a large, multi-objective design space in...
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....
$\gamma$-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...
Fast Swap-Based Element Selection for Multiplication-Free Dimension Reduction
arXiv:2602.13532v1 Announce Type: new Abstract: In this paper, we propose a fast algorithm for element selection, a multiplication-free form of dimension reduction that produces a dimension-reduced vector by simply selecting a subset of elements from the input. Dimension reduction is...
Interpretable clustering via optimal multiway-split decision trees
arXiv:2602.13586v1 Announce Type: new Abstract: Clustering serves as a vital tool for uncovering latent data structures, and achieving both high accuracy and interpretability is essential. To this end, existing methods typically construct binary decision trees by solving mixed-integer nonlinear optimization...
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...
Near-Optimal Regret for Policy Optimization in Contextual MDPs with General Offline Function Approximation
arXiv:2602.13706v1 Announce Type: new Abstract: We introduce \texttt{OPO-CMDP}, the first policy optimization algorithm for stochastic Contextual Markov Decision Process (CMDPs) under general offline function approximation. Our approach achieves a high probability regret bound of $\widetilde{O}(H^4\sqrt{T|S||A|\log(|\mathcal{F}||\mathcal{P}|)}),$ where $S$ and $A$ denote...
MEMTS: Internalizing Domain Knowledge via Parameterized Memory for Retrieval-Free Domain Adaptation of Time Series Foundation Models
arXiv:2602.13783v1 Announce Type: new Abstract: While Time Series Foundation Models (TSFMs) have demonstrated exceptional performance in generalized forecasting, their performance often degrades significantly when deployed in real-world vertical domains characterized by temporal distribution shifts and domain-specific periodic structures. Current solutions...
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,...
Mean Flow Policy with Instantaneous Velocity Constraint for One-step Action Generation
arXiv:2602.13810v1 Announce Type: new Abstract: Learning expressive and efficient policy functions is a promising direction in reinforcement learning (RL). While flow-based policies have recently proven effective in modeling complex action distributions with a fast deterministic sampling process, they still face...
sleep2vec: Unified Cross-Modal Alignment for Heterogeneous Nocturnal Biosignals
arXiv:2602.13857v1 Announce Type: new Abstract: Tasks ranging from sleep staging to clinical diagnosis traditionally rely on standard polysomnography (PSG) devices, bedside monitors and wearable devices, which capture diverse nocturnal biosignals (e.g., EEG, EOG, ECG, SpO$_2$). However, heterogeneity across devices and...
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 Multi-Agent Framework for Code-Guided, Modular, and Verifiable Automated Machine Learning
arXiv:2602.13937v1 Announce Type: new Abstract: Automated Machine Learning (AutoML) has revolutionized the development of data-driven solutions; however, traditional frameworks often function as "black boxes", lacking the flexibility and transparency required for complex, real-world engineering tasks. Recent Large Language Model (LLM)-based...
Criminalising ‘Conversion Therapy’
An increasing number of jurisdictions have introduced legal bans on so-called ‘conversion therapy’ practices. Yet significant uncertainty and disagreement persist among legal scholars, policymakers and advocates about whether criminal law is an appropriate tool in this area and, if so,...
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.