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arXiv stat.ML Papers

Benign Overfitting in Linear Classifiers with a Bias Term

arXiv:2511.12840v2 Announce Type: replace Abstract: Overparameterized models often generalize well even when they interpolate noisy training data. This is known as benign overfitting. For linear classification,…

arXiv cs.CL Papers

A Hamiltonian-Inspired Local-Operator Ansatz for Slimming Large Language Models

arXiv:2605.25344v2 Announce Type: replace Abstract: Dense linear maps carry much of the parameter and computational burden of modern neural networks, yet their dense form leaves the…

arXiv cs.CL Papers

LLM generation novelty through the lens of semantic similarity

arXiv:2510.27313v3 Announce Type: replace-cross Abstract: Generation novelty is a key indicator of an LLM's ability to generalize, yet measuring it against full pretraining corpora is computationally…

arXiv cs.CV Papers

Automatic LV Localization and Short-Axis Plane Estimation from Arbitrary CMR Slice

arXiv:2608.00145v1 Announce Type: cross Abstract: Accurate estimation of left ventricular (LV) orientation is essential for cardiac magnetic resonance (CMR) imaging and downstream analysis. Existing methods typically…

arXiv cs.CV Papers

Ensemble Deep Learning Approaches for AI-Altered Video Detection

arXiv:2607.06872v2 Announce Type: replace Abstract: The increasing accessibility of artificial intelligence has led to a rapid rise in AI-generated videos, making it more difficult to distinguish…

arXiv cs.LG Papers

Priors Persist Through Suppression: A Stroop Paradigm for Lexical Override

arXiv:2606.07555v4 Announce Type: replace-cross Abstract: Glossaries, technical specifications, and system prompts routinely ask language models to use familiar words in unfamiliar ways. The instruction competes with…

arXiv cs.NE Papers

Divisive Normalization Shapes Low-Rank Slow Manifolds for Continuous Working Memory

arXiv:2608.01947v1 Announce Type: cross Abstract: The ability to robustly maintain and update continuous variables is a hallmark of working memory. While classical continuous attractor networks suffer…

arXiv stat.ML Papers

Dominant Arm Identification with Mixing and Recycling Observed Samples

arXiv:2608.01545v1 Announce Type: new Abstract: We study the problem of identifying the dominant arm in multi-armed bandits, where the objective is to find the action with…

arXiv cs.CL Papers

Visualising Information Flow in Word Embeddings with Diffusion Tensor Imaging

arXiv:2601.05713v2 Announce Type: replace Abstract: Understanding how large language models (LLMs) represent natural language is a central challenge in natural language processing (NLP) research. Many existing…

arXiv cs.CL Papers

XL-DocBench: Benchmarking Evidence-Grounded Extra-Long Document Understanding

arXiv:2608.00036v1 Announce Type: new Abstract: Real-world document tasks often ask professionals to answer questions from annual reports, regulations, clinical guidelines, and technical manuals that span hundreds…

arXiv cs.CV Papers

Volcanic Clouds Detection through QCNN and Geostationary Satellite Multispectral Imagery

arXiv:2608.00072v1 Announce Type: new Abstract: Recent advances in quantum computing are opening new possibilities for Earth Observation (EO) data analysis. Quantum machine learning (QML) approaches offer…

arXiv cs.CV Papers

Beyond Edge Maps: Wavelet-Domain Conditioning for Multi-Adapter Map-to-Satellite Diffusion

arXiv:2608.00083v1 Announce Type: new Abstract: Commercial mapping partnerships are often unavailable in low-resource regions, leaving satellite basemaps stale and motivating synthesis of satellite imagery from independently…

arXiv cs.LG Papers

A Physics-Chemistry-Informed Neural Network (PCINN) for Real-Time Spatial-ALD Coverage Prediction and Reliable Kinetics Inversion

arXiv:2608.00212v1 Announce Type: new Abstract: Spatial atomic layer deposition (SALD) is a leading atmospheric-pressure, high-throughput route to industrial ALD, but design and control are limited by…

arXiv cs.NE Papers

SMM Transformer: Leveraging Spiking Neural Networks for Multimodal Tasks

arXiv:2608.01622v1 Announce Type: new Abstract: Spiking Neural Networks (SNNs) enable event-driven computation with sparse activations, but building multimodal Transformers on SNNs is hindered by unstable training…

arXiv stat.ML Papers

Finite-Probe Total-Variation Certificates for Finite-Basis Drifting Models

arXiv:2608.01547v1 Announce Type: new Abstract: Drifting objectives compare a target and model distribution through a vector field observed noisily at finitely many locations. We ask what…

arXiv cs.CL Papers

MAPLE: Metadata Augmented Private Language Evolution

arXiv:2603.19258v2 Announce Type: replace Abstract: Differentially private (DP) fine-tuning of large language models (LLMs) requires massive compute and full model access, which rules out state-of-the-art proprietary…

arXiv cs.CL Papers

UniHEAR: Unified Heterogeneous-Source Attentive Retrieval for Knowledge-Based Visual Question Answering

arXiv:2608.01147v1 Announce Type: cross Abstract: Knowledge-Based Visual Question Answering (KB-VQA) requires retrieving relevant entity knowledge from external sources to answer visually grounded questions. Existing retrieval-augmented systems…

arXiv cs.CV Papers

Beyond Random Partitioning: Unsupervised Spatio-Temporal Stratification for Cohort Balancing in Longitudinal Medical Imaging

arXiv:2608.00073v1 Announce Type: new Abstract: Rigorous dataset partitioning is a foundational, yet frequently overlooked, prerequisite for reliable deep learning in longitudinal medical imaging. Naively shuffling small…

arXiv cs.LG Papers

From Preimage Search To Source-Grounded Feature Inversion

arXiv:2607.12526v2 Announce Type: replace Abstract: Interpreting a neural network requires understanding what its internal features extract from a particular input. Feature inversion seeks to express a…

arXiv cs.LG Papers

Instruction-Conditioned Exploration with Asymmetric Reinforcement Learning and Self-Distillation

arXiv:2608.02087v1 Announce Type: cross Abstract: Post-training Large Language Models (LLMs) with Reinforcement Learning (RL) has become an important tool for improving model capabilities, but the LLM…

arXiv cs.NE Papers

DGA$_2$D: Directed Graph-Guided Automated Algorithm Design with Large Language Models

arXiv:2608.00700v1 Announce Type: cross Abstract: The rapid development of Large Language Models (LLMs) has opened new avenues for Automated Heuristic Design (AHD) for solving NP-hard combinatorial…

arXiv stat.ML Papers

How fine a change can moments see? A scale law for detecting distribution shift, with a kernel calibration rule

arXiv:2608.01268v1 Announce Type: new Abstract: Detecting that a stream of high-dimensional embeddings has changed is usually framed as a choice of statistic. We give a scale…

arXiv cs.CL Papers

Geometry-Guided Layerwise FFN Width Allocation in Transformers

arXiv:2608.02064v1 Announce Type: cross Abstract: Feed-forward networks (FFNs) account for a large fraction of Transformer parameters, yet their hidden width is usually constant across depth. We…

arXiv cs.CL Papers

Trustworthiness Costs of Domain Adaptation in Small Language Models:A Cross-Architecture Empirical Study

arXiv:2608.00042v1 Announce Type: new Abstract: Domain adaptation of small language models (SLMs) has emerged as a practical strategy for deploying capable NLP systems in resource-constrained, high-stakes…