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Polynomial neural surrogates for designing photonic quantum experiments

arXiv:2610.06032v1 Announce Type: cross Abstract: Physics simulators can support the discovery of quantum experiments by predicting the states generated by experimental configurations. When these simu

Private Component-by-Component Learning

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DGX agent

arXiv:2610.05102v1 Announce Type: new Abstract: We study differentially private learning problems in the realizable setting, where a hypothesis is specified by k components. A direct iteration of priv

researcharxiv-cs-lg

6 Oct 2026

Private online learning and prediction for Littlestone classes

arXiv:2610.06822v1 Announce Type: new Abstract: We study mistake bounds for differentially private online learning and online prediction under oblivious realisable adversaries. Online learning require

Probabilistic Algorithms for Ising Machines from Optimization to Generative AI

arXiv:2610.03972v1 Announce Type: new Abstract: Ising machines have emerged as promising hardware accelerators for intractable optimization and sampling problems, yet their practical impact increasing

Probability flow ODEs in score-based and reflected diffusion models

arXiv:2610.03846v1 Announce Type: cross Abstract: Probability-flow ordinary differential equations (PF-ODEs) are widely used as deterministic samplers for score-based diffusion models. Their usual jus

ProDER: A Continual Learning Approach for Fault Classification and Localization in Evolving Smart Grids

arXiv:2511.05420v3 Announce Type: replace Abstract: Data-driven fault diagnosis models for smart grids are usually trained once on a fixed dataset, whereas in operation new fault types appear and moni

Protocol-Sensitive Evaluation of Log Anomaly Detection: Component Costs and Target-Access Sensitivity on HDFS and BGL

arXiv:2610.05807v1 Announce Type: cross Abstract: Protocol choices can change the conclusions drawn from log anomaly detection benchmarks even when detector settings are fixed. We present a joint empi

Proximal Causal Learning under Unmeasured Confounding

arXiv:2610.04519v1 Announce Type: new Abstract: Estimating treatment effects from observational data typically relies on the No Unmeasured Confounding Assumption (NUCA), which rarely holds in practice

PyINE: A Framework for Scalable Elicitation and Oversight via Code Execution

arXiv:2610.04737v1 Announce Type: cross Abstract: Reasoning models can remain capable of solving a task while still defaulting to cheaper but misleading shortcuts. This creates a central oversight pro

Quantifying the Stability of Multi-Step Reasoning via Error Amplification

arXiv:2610.06404v1 Announce Type: new Abstract: We consider the stability of multi-step reasoning processes, which have extensive applications in language models, including chain-of-thought and algori

Quantum data loading from the learned shared structure of real signals

arXiv:2610.06076v1 Announce Type: cross Abstract: Preparing quantum states from classical data can cost more than the computation they serve; most loaders tailor a circuit to each input. Here we show

Quantum Machine Learning Protection of Military Quantum Key Distribution Against Cryptographically Camouflaged Attacks

arXiv:2610.04543v1 Announce Type: cross Abstract: Quantum key distribution proves its protocol secure and says nothing about the hardware beneath it, so military and government operators fielding it f

REACT: Physically and Chemically Consistent Reconstruction of Marine Active Tracers

arXiv:2610.03888v1 Announce Type: cross Abstract: Reconstructing global sea surface pH from sparse observations is critical for monitoring ocean acidification and understanding marine carbon cycling.

Reading the Mood: Emotion-Guided Book-to-Music Recommendation via CGANs and LLMs

arXiv:2610.06703v1 Announce Type: cross Abstract: Background music that matches the mood of a text has been shown to make readers feel more immersed and improve their reading experience, motivating re

Readout Blindness: VLM Scores Miss the Spatial Direction Their Frozen Encoders Retain

arXiv:2610.06324v1 Announce Type: new Abstract: CLIP-like vision-language models remain a cornerstone of multimodal systems, yet their scores stay near chance on directed spatial relations, such as wh

Real-Time Conformal-Seeded Hybrid Inverse Kinematics for Offset Redundant Manipulators

arXiv:2610.04266v1 Announce Type: new Abstract: This paper presents a conformal-seeded hybrid strategy for solving inverse kinematics of offset, redundant 7-DoF robot arms of the humanoid class. Analy

REDIRECT: A 1% Fix for Bad Robot Habits

arXiv:2610.03997v1 Announce Type: new Abstract: Robots can acquire bad habits from a few defective moments in otherwise useful teleoperation. In mixed-quality robot data, normal and problematic demons

Referring Multi-Object Tracking in Moving-Camera Videos via Global Motion Compensation

arXiv:2610.04185v1 Announce Type: new Abstract: Referring multi-object tracking (RMOT) takes a video and a language expression as input and tracks all referred objects. Many tracking requirements invo

REFLEX: Reflective Evolution from LLM Experience

arXiv:2606.16496v2 Announce Type: replace-cross Abstract: Large multimodal language models (MLLMs) have emerged as powerful tools for guiding evolutionary search toward interpretable programmatic poli

ReFRM3D: A Radiomics-enhanced Fused Residual Multiparametric 3D Network with Multi-Scale Feature Fusion for Glioma Characterization

arXiv:2512.22570v2 Announce Type: replace Abstract: Gliomas are among the most aggressive cancers, with complex diagnostic processes. Existing glioma segmentation methods often struggle with high vari

ReMaD: Tuning-free Domain Adaptation for Classification and Out-of-Distribution Detection

arXiv:2610.05718v1 Announce Type: new Abstract: We introduce Reduced-rank Mahalanobis Distance (ReMaD), a novel prototypical distance-based refinement to classification and out-of-distribution (OOD) d

ReMem: Streaming Video Understanding With Long Context Retention

arXiv:2610.05940v1 Announce Type: new Abstract: Despite their impressive performance on a wide range of video understanding tasks, current Vision Language Models (VLMs) are predominantly designed for

Render to Reason: Novel-View Semantic Prediction Improves Spatial Understanding in VLMs

arXiv:2610.05417v1 Announce Type: new Abstract: Recent works augment Vision-Language Models with geometry features from pretrained 3D models, expecting that the geometric signal will boost spatial rea

Repeated-Measure Leakage, Distribution Shift, and Reliability under Partial Observation in Patient World Models

arXiv:2610.04778v1 Announce Type: new Abstract: Patient world models are increasingly proposed for longitudinal prediction, intervention-aware reasoning, and clinical-trial simulation. Causal or clini

Representation-Aligned Auxiliary Supervision for Language Model Adaptation

arXiv:2610.04098v1 Announce Type: cross Abstract: Language models exhibit strong reasoning capabilities, yet adapting them to structured domains remains challenging and can yield inconsistent outcomes

Resisting Adversarial Attacks in Deep Neural Networks using Diverse Decision Boundaries

arXiv:2208.08697v2 Announce Type: replace-cross Abstract: The security of deep learning (DL) systems is an extremely important field of study as they are being deployed in several applications due to

Rethinking Long-Video Efficiency: A Joint Allocation Perspective on Frames, Pixels, and Front-End Latency

arXiv:2610.04318v1 Announce Type: new Abstract: Efficient long-video understanding with vision-language models (VLMs) is often framed as selecting informative frames or visual tokens at a fixed native

Rethinking Tabular Foundation Models On Data Streams

arXiv:2610.05352v1 Announce Type: new Abstract: Tabular foundation models (TFMs) outperform established machine learning models on tabular benchmarks through in-context learning. Building on this succ

Retrieval-Based In-Context Learning: A Domain Adaptation Framework

arXiv:2610.05717v1 Announce Type: cross Abstract: In-context retrieval (ICR) is a retrieval-based form of in-context learning (ICL) in which demonstrations are retrieved from a source database based o

Retrieval-Centric Deep Learning in Growing Nonparametric Neural Networks

arXiv:2610.03858v1 Announce Type: new Abstract: We investigate a general-purpose layer for deep learning that, instead of compressing arbitrary-size training data into fixed-size weight matrices, stor

Revisiting Ground-Truth Synthesis from High-Speed Video: Exact Validity Conditions and an Audited Consumer Capture Corpus

arXiv:2610.05298v1 Announce Type: new Abstract: Motion-deblurring datasets are commonly synthesised by averaging N consecutive high-frame-rate frames and labelling the result with the central frame. W

Revisiting the Broken Symmetry Phase of Solid Hydrogen: A Neural Network Variational Monte Carlo Study

arXiv:2512.17703v3 Announce Type: replace-cross Abstract: The crystal structure of high-pressure solid hydrogen remains a fundamental open problem. Although the research frontier has mostly shifted to

Revisiting the Generalization of Neural Graph Edit Distance Models

arXiv:2610.04644v1 Announce Type: new Abstract: Neural approaches to Graph Edit Distance (GED) have achieved strong results under standard within-dataset evaluation, but much less is known about how w

Robotic Long-Horizon Manipulation with Bayesian Non-parametric Skill Priors

arXiv:2503.21975v2 Announce Type: replace Abstract: Long-horizon manipulation under sparse rewards remains challenging for reinforcement learning due to delayed feedback and inefficient exploration. E

Robust blind unmixing: A geometric approach to overcoming basis variation

arXiv:2610.04091v1 Announce Type: new Abstract: Signal separation problems are common in science. A prominent example of this occurs during the use of diffraction or spectroscopy to identify the indiv

Robust Tensor Completion via Reflective Convolution Nuclear Norm Minimization

arXiv:2610.04979v1 Announce Type: new Abstract: Robust tensor completion recovers multidimensional data from partial observations corrupted by sparse gross errors. Existing convolutional low-rank mode

RoMod: Temporal Routing Modulation via Mixture-of-Experts for Video Anomaly Detection

arXiv:2610.05131v1 Announce Type: new Abstract: Intermediate-layer features from multimodal large language models have shown strong potential for video anomaly detection (VAD), yet the origin of their

S2PD: Serial-to-Parallel Diffusion for Physically and Logically Consistent Video Generation

arXiv:2610.06847v1 Announce Type: new Abstract: Bidirectional video diffusion models denoise entire videos in parallel, yet when trained on effectively unlimited in-distribution data from procedural g

Salvation Lies Within: Eliciting Inherent Style Transfer in Step-Distilled Diffusion Models

arXiv:2610.05066v1 Announce Type: new Abstract: Adapting step-distilled text-to-image (T2I) models through post-training incurs additional computational costs and affects native few-step generation be

Scaling 3D Visual Grounding in Abdominal CT

arXiv:2610.04095v1 Announce Type: new Abstract: Visual grounding models can enhance radiology workflows by linking report findings to image regions. This is particularly valuable for 3D CT, where find

ScopeSAE: Model-Scope Feature Discovery with Interpretable Layer Selection

arXiv:2610.04905v1 Announce Type: new Abstract: Sparse autoencoders (SAEs) are a central tool in mechanistic interpretability. However, existing SAEs are primarily trained per layer. The modeling subs

Screw Geometry Meets Bandits: Incremental Acquisition of Demonstrations to Generate Manipulation Plans

arXiv:2410.18275v2 Announce Type: replace Abstract: In this paper, we study the problem of methodically obtaining a sufficient set of kinesthetic demonstrations, one at a time, such that a robot can b

Separating Decision Time from Decision Quality in the Real-Time Gap of Distilled Deciders: Evidence from a Game and a Conveyor Simulator

arXiv:2610.04810v1 Announce Type: new Abstract: Real-time agents are often evaluated with the world paused, or with decision time rounded to whole ticks (tick conversion). Tick conversion predicts alm

Shadow Feature Refinement Network: Progressive Feature Refinement based on Knowledge Distillation for Effective Shadow Removal

arXiv:2610.05325v1 Announce Type: new Abstract: In the field of deep learning, has seen significant advancements; however, shadow removal remains a persistent challenge owing to the variable sizes and

Sharp Deviations Bounds for Dirichlet Weighted Sums with Application to analysis of Bayesian algorithms

arXiv:2304.03056v1 Announce Type: cross Abstract: In this work, we derive sharp non-asymptotic deviation bounds for weighted sums of Dirichlet random variables. These bounds are based on a novel integ

Sharp dimensional analysis of midpoint methods for Langevin sampling

arXiv:2610.06308v1 Announce Type: cross Abstract: We study deterministic and randomized midpoint discretizations of Langevin dynamics for a target pi propto e^{-V}, where 0 prec alpha Ipreceqnabla^2Vp

Sharp Integrality Gaps in Calibration Distance

arXiv:2610.05679v1 Announce Type: new Abstract: We study the offline gap between deterministic calibration distance C and its fractional relaxation L for binary unit-weight sequences under total absol

SIFT: Robust Meta-Faithfulness Verification of Chain-of-Thought Reasoning Under Distribution Shift

arXiv:2610.04594v1 Announce Type: cross Abstract: Chain-of-Thought (CoT) faithfulness detectors are widely used to audit reasoning models, yet a detector is itself a predictor whose verdicts are treat