LK Losses: Direct Acceptance Rate Optimization for Speculative Decoding
arXiv:2602.23881v1 Announce Type: cross Abstract: Speculative decoding accelerates autoregressive large language model (LLM) inference by using a lightweight draft model to propose candidate tokens that are then verified in parallel by the target model. The speedup is significantly determined by...
Jailbreak Foundry: From Papers to Runnable Attacks for Reproducible Benchmarking
arXiv:2602.24009v1 Announce Type: cross Abstract: Jailbreak techniques for large language models (LLMs) evolve faster than benchmarks, making robustness estimates stale and difficult to compare across papers due to drift in datasets, harnesses, and judging protocols. We introduce JAILBREAK FOUNDRY (JBF),...
RewardUQ: A Unified Framework for Uncertainty-Aware Reward Models
arXiv:2602.24040v1 Announce Type: cross Abstract: Reward models are central to aligning large language models (LLMs) with human preferences. Yet most approaches rely on pointwise reward estimates that overlook the epistemic uncertainty in reward models arising from limited human feedback. Recent...
U-CAN: Utility-Aware Contrastive Attenuation for Efficient Unlearning in Generative Recommendation
arXiv:2602.23400v1 Announce Type: new Abstract: Generative Recommendation (GenRec) typically leverages Large Language Models (LLMs) to redefine personalization as an instruction-driven sequence generation task. However, fine-tuning on user logs inadvertently encodes sensitive attributes into model parameters, raising critical privacy concerns. Existing...
Uncertainty-aware Language Guidance for Concept Bottleneck Models
arXiv:2602.23495v1 Announce Type: new Abstract: Concept Bottleneck Models (CBMs) provide inherent interpretability by first mapping input samples to high-level semantic concepts, followed by a combination of these concepts for the final classification. However, the annotation of human-understandable concepts requires extensive...
FedDAG: Clustered Federated Learning via Global Data and Gradient Integration for Heterogeneous Environments
arXiv:2602.23504v1 Announce Type: new Abstract: Federated Learning (FL) enables a group of clients to collaboratively train a model without sharing individual data, but its performance drops when client data are heterogeneous. Clustered FL tackles this by grouping similar clients. However,...
Sample Size Calculations for Developing Clinical Prediction Models: Overview and pmsims R package
arXiv:2602.23507v1 Announce Type: new Abstract: Background: Clinical prediction models are increasingly used to inform healthcare decisions, but determining the minimum sample size for their development remains a critical and unresolved challenge. Inadequate sample sizes can lead to overfitting, poor generalisability,...
Neural Operators Can Discover Functional Clusters
arXiv:2602.23528v1 Announce Type: new Abstract: Operator learning is reshaping scientific computing by amortizing inference across infinite families of problems. While neural operators (NOs) are increasingly well understood for regression, far less is known for classification and its unsupervised analogue: clustering....
Active Value Querying to Minimize Additive Error in Subadditive Set Function Learning
arXiv:2602.23529v1 Announce Type: new Abstract: Subadditive set functions play a pivotal role in computational economics (especially in combinatorial auctions), combinatorial optimization or artificial intelligence applications such as interpretable machine learning. However, specifying a set function requires assigning values to an...
Rudder: Steering Prefetching in Distributed GNN Training using LLM Agents
arXiv:2602.23556v1 Announce Type: new Abstract: Large-scale Graph Neural Networks (GNNs) are typically trained by sampling a vertex's neighbors to a fixed distance. Because large input graphs are distributed, training requires frequent irregular communication that stalls forward progress. Moreover, fetched data...
Dynamics of Learning under User Choice: Overspecialization and Peer-Model Probing
arXiv:2602.23565v1 Announce Type: new Abstract: In many economically relevant contexts where machine learning is deployed, multiple platforms obtain data from the same pool of users, each of whom selects the platform that best serves them. Prior work in this setting...
SDMixer: Sparse Dual-Mixer for Time Series Forecasting
arXiv:2602.23581v1 Announce Type: new Abstract: Multivariate time series forecasting is widely applied in fields such as transportation, energy, and finance. However, the data commonly suffers from issues of multi-scale characteristics, weak correlations, and noise interference, which limit the predictive performance...
Normalisation and Initialisation Strategies for Graph Neural Networks in Blockchain Anomaly Detection
arXiv:2602.23599v1 Announce Type: new Abstract: Graph neural networks (GNNs) offer a principled approach to financial fraud detection by jointly learning from node features and transaction graph topology. However, their effectiveness on real-world anti-money laundering (AML) benchmarks depends critically on training...
When Does Multimodal Learning Help in Healthcare? A Benchmark on EHR and Chest X-Ray Fusion
arXiv:2602.23614v1 Announce Type: new Abstract: Machine learning holds promise for advancing clinical decision support, yet it remains unclear when multimodal learning truly helps in practice, particularly under modality missingness and fairness constraints. In this work, we conduct a systematic benchmark...
FlexGuard: Continuous Risk Scoring for Strictness-Adaptive LLM Content Moderation
arXiv:2602.23636v1 Announce Type: new Abstract: Ensuring the safety of LLM-generated content is essential for real-world deployment. Most existing guardrail models formulate moderation as a fixed binary classification task, implicitly assuming a fixed definition of harmfulness. In practice, enforcement strictness -...
Disentangled Mode-Specific Representations for Tensor Time Series via Contrastive Learning
arXiv:2602.23663v1 Announce Type: new Abstract: Multi-mode tensor time series (TTS) can be found in many domains, such as search engines and environmental monitoring systems. Learning representations of a TTS benefits various applications, but it is also challenging since the complexities...
MAGE: Multi-scale Autoregressive Generation for Offline Reinforcement Learning
arXiv:2602.23770v1 Announce Type: new Abstract: Generative models have gained significant traction in offline reinforcement learning (RL) due to their ability to model complex trajectory distributions. However, existing generation-based approaches still struggle with long-horizon tasks characterized by sparse rewards. Some hierarchical...
TradeFM: A Generative Foundation Model for Trade-flow and Market Microstructure
arXiv:2602.23784v1 Announce Type: new Abstract: Foundation models have transformed domains from language to genomics by learning general-purpose representations from large-scale, heterogeneous data. We introduce TradeFM, a 524M-parameter generative Transformer that brings this paradigm to market microstructure, learning directly from billions...
GRAIL: Post-hoc Compensation by Linear Reconstruction for Compressed Networks
arXiv:2602.23795v1 Announce Type: new Abstract: Structured deep model compression methods are hardware-friendly and substantially reduce memory and inference costs. However, under aggressive compression, the resulting accuracy degradation often necessitates post-compression finetuning, which can be impractical due to missing labeled data...
MPU: Towards Secure and Privacy-Preserving Knowledge Unlearning for Large Language Models
arXiv:2602.23798v1 Announce Type: new Abstract: Machine unlearning for large language models often faces a privacy dilemma in which strict constraints prohibit sharing either the server's parameters or the client's forget set. To address this dual non-disclosure constraint, we propose MPU,...
Actor-Critic Pretraining for Proximal Policy Optimization
arXiv:2602.23804v1 Announce Type: new Abstract: Reinforcement learning (RL) actor-critic algorithms enable autonomous learning but often require a large number of environment interactions, which limits their applicability in robotics. Leveraging expert data can reduce the number of required environment interactions. A...
Beyond State-Wise Mirror Descent: Offline Policy Optimization with Parameteric Policies
arXiv:2602.23811v1 Announce Type: new Abstract: We investigate the theoretical aspects of offline reinforcement learning (RL) under general function approximation. While prior works (e.g., Xie et al., 2021) have established the theoretical foundations of learning a good policy from offline data...
Inferring Chronic Treatment Onset from ePrescription Data: A Renewal Process Approach
arXiv:2602.23824v1 Announce Type: new Abstract: Longitudinal electronic health record (EHR) data are often left-censored, making diagnosis records incomplete and unreliable for determining disease onset. In contrast, outpatient prescriptions form renewal-based trajectories that provide a continuous signal of disease management. We...
ULW-SleepNet: An Ultra-Lightweight Network for Multimodal Sleep Stage Scoring
arXiv:2602.23852v1 Announce Type: new Abstract: Automatic sleep stage scoring is crucial for the diagnosis and treatment of sleep disorders. Although deep learning models have advanced the field, many existing models are computationally demanding and designed for single-channel electroencephalography (EEG), limiting...
Hierarchical Concept-based Interpretable Models
arXiv:2602.23947v1 Announce Type: new Abstract: Modern deep neural networks remain challenging to interpret due to the opacity of their latent representations, impeding model understanding, debugging, and debiasing. Concept Embedding Models (CEMs) address this by mapping inputs to human-interpretable concept representations...
Learning Generation Orders for Masked Discrete Diffusion Models via Variational Inference
arXiv:2602.23968v1 Announce Type: new Abstract: Masked discrete diffusion models (MDMs) are a promising new approach to generative modelling, offering the ability for parallel token generation and therefore greater efficiency than autoregressive counterparts. However, achieving an optimal balance between parallel generation...
Intrinsic Lorentz Neural Network
arXiv:2602.23981v1 Announce Type: new Abstract: Real-world data frequently exhibit latent hierarchical structures, which can be naturally represented by hyperbolic geometry. Although recent hyperbolic neural networks have demonstrated promising results, many existing architectures remain partially intrinsic, mixing Euclidean operations with hyperbolic...
MINT: Multimodal Imaging-to-Speech Knowledge Transfer for Early Alzheimer's Screening
arXiv:2602.23994v1 Announce Type: new Abstract: Alzheimer's disease is a progressive neurodegenerative disorder in which mild cognitive impairment (MCI) marks a critical transition between aging and dementia. Neuroimaging modalities, such as structural MRI, provide biomarkers of this transition; however, their high...
Foundation World Models for Agents that Learn, Verify, and Adapt Reliably Beyond Static Environments
arXiv:2602.23997v1 Announce Type: new Abstract: The next generation of autonomous agents must not only learn efficiently but also act reliably and adapt their behavior in open worlds. Standard approaches typically assume fixed tasks and environments with little or no novelty,...
pathsig: A GPU-Accelerated Library for Truncated and Projected Path Signatures
arXiv:2602.24066v1 Announce Type: new Abstract: Path signatures provide a rich representation of sequential data, with strong theoretical guarantees and good performance in a variety of machine-learning tasks. While signatures have progressed from fixed feature extractors to trainable components of machine-learning...