Search: “research”

Inconsistent Databases and Argumentation Frameworks with Collective Attacks

arXiv:2605.03954v1 Announce Type: cross Abstract: The connection between subset-maximal repairs for inconsistent databases involving various integrity constraints and acceptable sets of arguments with

researcharxiv-cs-ai

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InSpatio-WorldFM: An Open-Source Real-Time Generative Frame Model

DGX agent

arXiv:2603.11911v3 Announce Type: replace Abstract: We present InSpatio-WorldFM, an open-source real-time frame model for spatial intelligence. Unlike video-based world models that rely on sequential

researcharxiv-cs-cv

7 May 2026

InterMesh: Explicit Interaction-Aware End-to-End Multi-Person Human Mesh Recovery

arXiv:2605.04554v1 Announce Type: new Abstract: Humans constantly interact with their surroundings. Existing end-to-end multi-person human mesh recovery methods, typically based on the DETR framework,

ipc_shared_ptr: A Publish/Subscribe-Aware Smart Pointer for Cross-Process Object Lifetime Management

arXiv:2605.04226v1 Announce Type: cross Abstract: True zero-copy Inter-Process Communication (IPC) in publish/subscribe (pub/sub) middleware such as Robot Operating System 2 (ROS 2) requires subscribe

researcharxiv-cs-ro

ITBoost: Information-Theoretic Trust for Robust Boosting

arXiv:2605.04671v1 Announce Type: new Abstract: Gradient boosting remains a strong and widely used method for tabular data learning, but its performance often degrades when training labels are noisy.

Koopman Identification of Nonlinear Systems via Reservoir Liftings

arXiv:2605.04917v1 Announce Type: new Abstract: Learning tractable linear representations of nonlinear dynamical systems via Koopman operator theory is often hindered by dictionary selection, temporal

Large Scale Diffusion Distillation via Score-Regularized Continuous-Time Consistency

arXiv:2510.08431v3 Announce Type: replace Abstract: Although continuous-time consistency models (e.g., sCM, MeanFlow) are theoretically principled and empirically powerful for fast academic-scale diff

Layerwise LQR for Geometry-Aware Optimization of Deep Networks

arXiv:2605.04230v1 Announce Type: new Abstract: Geometry-aware optimizers such as Newton and natural gradient can improve conditioning in deep learning, but scalable variants such as K-FAC, Shampoo, a

Learning-based Statistical Refinement for Denoising

arXiv:2605.04332v1 Announce Type: cross Abstract: This work proposes a learning-based statistical refinement method for improving the denoising results of a given denoiser without knowing the precise

Learning reveals invisible structure in low-rank RNNs

arXiv:2605.04115v1 Announce Type: new Abstract: Learning in neural systems arises from synaptic changes that reshape the representations underlying behavior. While low-rank recurrent neural networks (

Learning Time-Varying Graphs from Incomplete Graph Signals

arXiv:2510.17903v3 Announce Type: replace-cross Abstract: This paper tackles the challenging problem of jointly inferring time-varying network topologies and imputing missing data from partially obser

Leveraging Pretrained Language Models as Energy Functions for Glauber Dynamics Text Diffusion

arXiv:2605.04291v1 Announce Type: new Abstract: We present a discrete diffusion-based language model using Glauber dynamics from statistical physics. Our main insight is that instead of trying to trai

Library learning with e-graphs on jazz harmony

arXiv:2605.04622v1 Announce Type: new Abstract: Humans can acquire a highly structured intuitive understanding of musical patterns, yet these patterns often require multiple iterations of reflection a

Lightning Unified Video Editing via In-Context Sparse Attention

arXiv:2605.04569v1 Announce Type: new Abstract: Video editing has evolved toward In-Context Learning (ICL) paradigms, yet the resulting quadratic attention costs create a critical computational bottle

Lookahead Drifting Model

arXiv:2605.04060v1 Announce Type: cross Abstract: Recently, a new paradigm named drifting model has been proposed for mapping distributions, which achieves the SOTA image generation performance over I

Low-Cost Black-Box Detection of LLM Hallucinations via Dynamical System Prediction

arXiv:2605.05134v1 Announce Type: new Abstract: Large Language Models (LLMs) frequently generate plausible but non-factual content, a phenomenon known as hallucination. While existing detection method

Malliavin Calculus for Counterfactual Gradient Estimation in Adaptive Inverse Reinforcement Learning

arXiv:2604.01345v2 Announce Type: replace Abstract: Inverse reinforcement learning (IRL) recovers the loss function of a forward learner from its observed responses. Adaptive IRL aims to reconstruct t

MalPurifier: Enhancing Android Malware Detection with Adversarial Purification against Evasion Attacks

arXiv:2312.06423v3 Announce Type: replace-cross Abstract: Machine learning (ML) has gained significant adoption in Android malware detection to address the escalating threats posed by the rapid prolif

Manifold Steering Reveals the Shared Geometry of Neural Network Representation and Behavior

arXiv:2605.05115v1 Announce Type: new Abstract: Neural representations carry rich geometric structure; but does that structure causally shape behavior? To address this question, we intervene along pat

Measuring Psychological States Through Semantic Projection: A Theory-Driven Approach to Language-Based Assessment

arXiv:2605.04873v1 Announce Type: new Abstract: Recent advances in natural language processing have enabled increasingly accurate estimation of psychological traits from language. However, most existi

researcharxiv-cs-cl

Membership Inference Attacks for Retrieval Based In-Context Learning for Document Question Answering

arXiv:2605.04116v1 Announce Type: cross Abstract: We show that remotely hosted applications employing in-context learning when augmented with a retrieval function to select in-context examples can be

Mitigating Label Shift in Tabular In-Context Learning via Test-Time Posterior Adjustment

arXiv:2605.04363v1 Announce Type: new Abstract: TabPFN has recently gained attention as a foundation model for tabular datasets, achieving strong performance by leveraging in-context learning on synth

Model synthesis and identifiability analysis of stiff chemical reaction systems with inVAErt networks

arXiv:2605.04134v1 Announce Type: new Abstract: We consider the problem of learning data-driven replicas for stiff systems of ordinary differential equations arising in chemical kinetics that can be e

MoLF: Mixture-of-Latent-Flow for Pan-Cancer Spatial Gene Expression Prediction from Histology

arXiv:2602.02282v2 Announce Type: replace Abstract: Inferring spatial transcriptomics (ST) from histology enables scalable histogenomic profiling, yet current methods are largely restricted to single-

Morphology-Guided Cross-Task Coupling for Joint Building Height and Footprint Estimation

arXiv:2605.04731v1 Announce Type: new Abstract: Building height (BH) and building footprint (BF) jointly describe the vertical and horizontal extent of the built environment and are required inputs fo

MuCALD-SplitFed: Causal-Latent Diffusion for Privacy-Preserving Multi-Task Split-Federated Medical Image Segmentation

arXiv:2605.04108v1 Announce Type: new Abstract: Federated Learning enables decentralized training by aggregating model updates across clients without sharing raw data, while Split Federated Learning f

Multi-site modelling and reconstruction of past extreme skew surges along the French Atlantic coast

arXiv:2505.00835v2 Announce Type: replace-cross Abstract: Appropriate modelling of extreme skew surges is crucial, particularly for coastal risk management. Our study focuses on modelling extreme skew

Multiscale Euclidean Network Trajectories: Second-Moment Geometry, Attribution, and Change Points

arXiv:2605.04589v1 Announce Type: cross Abstract: A central challenge in dynamic network analysis is to represent temporal evolution in a way that is both geometrically meaningful and statistically id

Neural Discovery of Strichartz Extremizers

arXiv:2605.04918v1 Announce Type: cross Abstract: Strichartz inequalities are a cornerstone of the modern theory of dispersive PDEs, but their extremizers are known explicitly only in a handful of sha

Neural-Guided Domain Restriction to Accelerate Pseudospectra Computation for Structured Non-normal Banded Matrices

arXiv:2605.04550v1 Announce Type: cross Abstract: Computing pseudospectra of non-normal matrices is essential for understanding the stability and transient behavior of dynamical systems. Such analysis

Norm Anchors Make Model Edits Last

arXiv:2602.02543v3 Announce Type: replace Abstract: Sequential Locate-and-Edit (L&E) model editing can fail abruptly after many edits. We identify and formalize this failure as a positive norm-feedbac

Not Every Subject Should Stay: Machine Unlearning for Noisy Engagement Recognition

arXiv:2605.04713v1 Announce Type: new Abstract: Engagement recognition datasets are typically subject-indexed and often contain noisy, subjective supervision, making post-hoc dataset revision a practi

NSL-MT: Linguistically Informed Negative Samples for Efficient Machine Translation in Low-Resource Languages

arXiv:2511.09537v2 Announce Type: replace Abstract: We introduce negative space learning machine translation (NSL-MT), a training method for underresourced languages, that augments limited parallel da

On the Architectural Complexity of Neural Networks

arXiv:2605.04325v1 Announce Type: new Abstract: We introduce a unified theoretical framework for the rigorous analysis and systematic construction of deep neural networks (DNNs). This framework addres

On the Influence of the Feature Computation Budget on Per-Instance Algorithm Selection for Black-Box Optimization

arXiv:2605.04954v1 Announce Type: cross Abstract: Per-instance algorithm selection (PIAS) takes advantage of complementarity between a set of algorithms by deciding which algorithm to run on a given i

On the Non-decoupling of Supervised Fine-tuning and Reinforcement Learning in Post-training

arXiv:2601.07389v2 Announce Type: replace Abstract: Post-training of large language models routinely interleaves supervised fine-tuning (SFT) with reinforcement learning (RL). These two methods have d

On the Wasserstein Gradient Flow Interpretation of Drifting Models

arXiv:2605.05118v1 Announce Type: new Abstract: Recently, Deng et al. (2026) proposed Generative Modeling via Drifting (GMD), a novel framework for generative tasks. This note presents an analysis of

Online Nonstochastic Prediction: Logarithmic Regret via Predictive Online Least Squares

arXiv:2605.04364v1 Announce Type: new Abstract: We study online prediction for marginally stable, partially observed linear dynamical systems under nonstochastic disturbances. Our objective is to mini

Optimize-at-Capture: Highly-adaptive Exposure Controlling for In-Vehicle Non-contact Heart-rate Monitoring

arXiv:2605.04397v1 Announce Type: new Abstract: Remote photoplethysmography (rPPG) holds great promise for continuous heart-rate monitoring of drivers in intelligent vehicles. However, its performance

Pack it in: Packing into Partially Filled Containers Through Contact

arXiv:2602.12095v3 Announce Type: replace Abstract: The automation of warehouse operations is crucial for improving productivity and reducing human exposure to hazardous environments. One operation fr

researcharxiv-cs-ro

PAIR-CI: Calibrated Conditional Independence Testing for Causal Discovery with Incomplete Data

arXiv:2605.04838v1 Announce Type: cross Abstract: The standard constraint-based paradigm for causal discovery with incomplete data -- impute first, test second -- is frequently miscalibrated: any cons

PhysForge: Generating Physics-Grounded 3D Assets for Interactive Virtual World

arXiv:2605.05163v1 Announce Type: new Abstract: Synthesizing physics-grounded 3D assets is a critical bottleneck for interactive virtual worlds and embodied AI. Existing methods predominantly focus on

Physics-Guided Regime Unmixing

arXiv:2605.04247v1 Announce Type: new Abstract: The Linear Mixing Model (LMM) dominates spectral unmixing for its simplicity, but fails under multiple scattering; existing nonlinear models compensate

Physiologically Grounded Driver Behavior Classification: SHAP-Driven Elite Feature Selection and Hybrid Gradient Boosting for Multimodal Physiological Signals

arXiv:2605.05120v1 Announce Type: new Abstract: An interpretable and scalable framework for decoding driving behaviors from multimodal physiological signals is proposed in this study. We utilize multi

Position: the Stochastic Parrot in the Coal Mine. Model Collapse is a Threat to Low-Resource Communities

arXiv:2605.04127v1 Announce Type: cross Abstract: Model collapse, the degradation in performance that arises when generative models are trained on the outputs of prior models, is an increasing concern

researcharxiv-cs-cl

PRISM: Color-Stratified Point Cloud Sampling

arXiv:2601.06839v2 Announce Type: replace Abstract: We present PRISM, a novel color-guided stratified sampling method for RGB-LiDAR point clouds. Our approach is motivated by the observation that uniq

Probabilistic Classification and Uncertainty Quantification of Sahara Desert Climate Using Feedforward Neural Networks

arXiv:2605.04286v1 Announce Type: new Abstract: Climate classification plays a vital role in agricultural planning, hydrological studies, and climate science. One of the most widely used systems for c

Programmatic Context Augmentation for LLM-based Symbolic Regression

arXiv:2605.03101v1 Announce Type: new Abstract: Symbolic regression (SR), the task of discovering mathematical expressions that best describe a given dataset, remains a fundamental challenge in scient

researcharxiv-cs-ai