Search: “research”

KOALA: Koopman Operator Learning for WiFi-Based Anticipatory Hum

arXiv:2608.15815v1 Announce Type: new Abstract: WiFi Channel State Information (CSI) has emerged as a privacy-preserving alternative to cameras for human pose estimation. However, existing approaches

Content type

All Blog X Post Paper YouTube Reddit GitHub

Clear filters

Koopman early warning signals for bifurcation and rate-induced tipping

DGX agent

arXiv:2608.14716v1 Announce Type: cross Abstract: Abrupt transitions in complex systems are often preceded by early warning signals. However, most indicators rely on the notion of critical slowing dow

researcharxiv-cs-lg

18 Aug 2026

KV-Rescue: Recovering Reasoning Language Model KV Eviction Loss via Stepwise Interleaving

arXiv:2608.15797v1 Announce Type: new Abstract: KV-cache eviction caps the memory cost of long reasoning traces but is inherently lossy because the model decodes from a partial view of its history. Un

Large Discovery Models: Empirically-grounded Model-Based Open-Ended Search

arXiv:2608.15669v1 Announce Type: new Abstract: Scientific discovery often involves optimising expensive-to-evaluate objectives over vast, structured, and open-ended hypothesis spaces, such as molecul

Large Language Model Assisted Operational Monitoring for Battery Energy Storage System Integrated Power Distribution Networks

arXiv:2608.15396v1 Announce Type: new Abstract: Battery energy storage systems (BESS) are increasingly used in distribution networks for voltage regulation and demand response, which increases the vol

Large Language Models as Implicit Sociological Models: Reconstructing Voting Behaviour from Sociodemographic Profiles

arXiv:2608.15871v1 Announce Type: cross Abstract: Large language models (LLMs) trained on large-scale internet corpora encode extensive statistical regularities about social identities, attitudes, and

researcharxiv-cs-cl

Large language models as synthetic clinical experts to inform longitudinal rare-disease modeling

arXiv:2608.16507v1 Announce Type: new Abstract: Due to the limited amount of information, modeling longitudinal rare-disease data can benefit from integrating clinical knowledge. Yet, elicitation of e

Learning Auditable Classifier Models: Source-Disjoint Tree Ensembles

arXiv:2608.15725v1 Announce Type: new Abstract: Predictive models in clinical and regulated settings must be accurate and fully auditable. Tree ensembles deliver strong accuracy on tabular data, but t

Learning Discrete Riemannian Metrics for Physical Fields with Cochain-Frame Equivarianc

arXiv:2608.14556v1 Announce Type: new Abstract: Physical fields on meshes require a separation between topology and geometry: conservation laws are topological and should be exact, while geometry, mat

Learning Generalizable Reconstruction of High-Dimensional Neural Dynamics

arXiv:2608.16569v1 Announce Type: new Abstract: Accurate reconstruction of long-duration neural recordings is challenging because local field potentials (LFPs) are high-resolution, multichannel, trans

Learning to Price with Persuasion

arXiv:2608.16699v1 Announce Type: cross Abstract: Motivated by modern marketplaces, where the platform or the seller routinely gathers detailed user profiles, we study a novel learning theoretic model

LightLoc++: Sensor-Robust Representation Learning for Efficient Outdoor LiDAR Localization

arXiv:2608.15317v1 Announce Type: new Abstract: Scene coordinate regression (SCR) achieves strong performance in outdoor LiDAR localization, but it usually requires scene-specific training that can ta

Lightweight Deep Learning-Based Channel Estimation for RIS-Aided Extremely Large-Scale MIMO Systems on Resource-Limited Edge Devices

arXiv:2507.09627v3 Announce Type: replace-cross Abstract: Next-generation wireless technologies such as 6G aim to meet demanding requirements such as ultra-high data rates, low latency, and enhanced c

Lipschitz Bandits with Arbitrary Feedback Delays

arXiv:2608.15036v1 Announce Type: new Abstract: The Lipschitz bandit problem extends the traditional multi-armed bandit framework to continuous action spaces by assuming that the reward functions sati

Local Gains and Fixed-Assignment Set Losses in Shared Set Decoders

arXiv:2608.14717v1 Announce Type: new Abstract: A query-relation deletion can improve the edited slot while reducing the utility of the prediction set that contains it. We study this tension in two re

Logical Embeddings for Argument Analysis

arXiv:2608.15325v1 Announce Type: cross Abstract: We propose a new framework for machine-learning-oriented argument analysis tasks. Our proposal involves replacing traditional contextualized word embe

Longitudinal and Graph-Augmented Prediction of Adolescent Substance Use Onset in the ABCD Study

arXiv:2608.14578v1 Announce Type: new Abstract: Early identification of adolescent substance-use risk is an important prevention challenge, yet the relative value of baseline characteristics, longitud

Look Before You Lift: Visual and Quantitative Diagnostics for Topological Deep Learning

arXiv:2608.15388v1 Announce Type: new Abstract: Topological deep learning (TDL) methods rely on lifting raw data into higher-order discrete domains such as simplicial complexes, cell complexes, and hy

Looks Can be Deceiving: Annotator and Reviewer Performance Across Imagery Sources in Crowd-Sourced Aerial Damage Assessment

arXiv:2608.14942v1 Announce Type: cross Abstract: This paper presents the first known empirical investigation of annotator and reviewer performance across multi-source remotely sensed imagery, evaluat

M-LINKX: Multiview Graph Learning for Brain Cognitive Disease Detection

arXiv:2608.14847v1 Announce Type: new Abstract: Electroencephalogram (EEG) is a non-invasive and relatively low-cost procedure that measures brain electricity for the detection of cognitive diseases.

Machine Learning Approaches to Decoding Topological Quantum Codes

arXiv:2608.15760v1 Announce Type: cross Abstract: Decoding is an essential component of quantum error correction (QEC), translating stabilizer measurement outcomes into corrective actions that suppres

MegaParts: Scaling Part-Aware 3D Object Generation to 300 Parts via Token-Efficient Autoregressive Modeling

arXiv:2608.14783v1 Announce Type: new Abstract: Part-aware 3D object generation is essential for graphics applications such as controllable modeling, editing, and articulation, where objects are repre

Memory-Bounded Continuation of Greedy Sampling for Continual Anomaly Detection

arXiv:2608.15277v1 Announce Type: new Abstract: Greedy sampling produces a compact yet representative summary of normal data, which is essential for reliable anomaly detection that relies on measuring

Mental Model Management: An Operator-Based Framework for LLM Memory

arXiv:2608.15451v1 Announce Type: new Abstract: Large language models process large amounts of information but usually lack an explicit mechanism for maintaining compact and evolving conceptual repres

MISTac: A Vision-Based Tactile Sensor for Minimally Invasive Surgery

arXiv:2608.14772v1 Announce Type: new Abstract: Minimally invasive and robot-assisted surgery offer many advantages over traditional open surgery, but deprive surgeons of tactile feedback and the abil

researcharxiv-cs-ro

Mitigating Error Accumulation in Co-Speech Motion Generation via Global Rotation Diffusion and Multi-Level Constraints

arXiv:2511.10076v3 Announce Type: replace Abstract: Reliable long-horizon co-speech gesture generation requires precise motion representation and consistent structural priors across all joints. Existi

Mixture of experts architectures for machine learning interatomic potentials

arXiv:2603.07977v3 Announce Type: replace-cross Abstract: Machine Learning Interatomic Potentials (MLIPs) enable accurate large-scale atomistic simulations, yet improving their expressive capacity eff

mmWave Radar Aware Dual-Conditioned GAN for Speech Reconstruction of Signals With Low SNR

arXiv:2602.22431v2 Announce Type: replace-cross Abstract: Millimeter-wave (mmWave) radar captures are band-limited and noisy, making for difficult reconstruction of intelligible full-bandwidth speech.

MODAL: Multi-Modal Object Re-ID via Model-Driven Sparse Decoupling and Text-Image Differential Filtering

arXiv:2608.15096v1 Announce Type: new Abstract: Multi-modal object re-identification (Re-ID) aims to facilitate cross-camera object retrieval in complex environments by leveraging complementary inform

MOSS-VL Technical Report

arXiv:2608.15045v1 Announce Type: new Abstract: We present MOSS-VL, an open vision-language model family that treats real-time interaction -- perceiving while it speaks -- as a first-class capability.

MotionGS-SLAM: Event-Modulated Gaussian Splatting for Motion-Blur Robust SLAM

arXiv:2608.15024v1 Announce Type: cross Abstract: Current Vision-based SLAM systems fail catastrophically when motion blur corrupts the visual input, as they attempt the ill-posed inverse problem of r

Multi-scale Decomposed Convolution Refinement Network for Visible-Infrared Person Re-Identification

arXiv:2608.16015v1 Announce Type: new Abstract: Visible-infrared person re-identification (VI-ReID) suffers from cross-modal discrepancies and limited discriminative capabilities, leading to suboptima

Multilook Coherent Imaging: Theoretical Guarantees and Algorithms

arXiv:2505.23594v2 Announce Type: replace-cross Abstract: Multilook coherent imaging is a widely used technique in applications such as digital holography, ultrasound imaging, and synthetic aperture r

MUPA^{2}E: Multimodal Unified Perception with Asymmetric Attention for Emotion Assessment

arXiv:2608.15999v1 Announce Type: new Abstract: Automatic emotion assessment can benefit from combining neural and behavioral signals, but many multimodal approaches rely on separate, modality-specifi

Near-Equilibrium Propagation training in nonlinear wave systems

arXiv:2510.16084v3 Announce Type: replace Abstract: Backpropagation learning algorithm, the workhorse of modern artificial intelligence, is notoriously difficult to implement in physical neural networ

NeuRoute: Logit-Guided Neural Routing for Billion-Scale Vector Search with Sub-Hour Index Construction

arXiv:2608.15438v1 Announce Type: cross Abstract: Building approximate nearest neighbor (ANN) indexes at billion scale is often dominated by expensive global clustering or graph construction, making t

Nexus: Structured Synergy for Efficient Text-to-Image Generation using Rectified Flow Model

arXiv:2608.16104v1 Announce Type: new Abstract: Diffusion and flow matching models have made significant progress in text-to-image generation, yet high computation, quadratic complexity, and large mem

NICE: Scale-Stable Perturbations for Graph Neural Network Explanations via Noise Corruption

arXiv:2608.16038v1 Announce Type: cross Abstract: Post-hoc Graph Neural Network (GNN) explainers commonly follow a Perturb-Query paradigm, inferring the importance of graph elements based on queried p

Noesis: Bidirectional Graph-RAG with Adaptive Parallelism and Cross-Knowledge-Base Semantic Discovery

arXiv:2608.15919v1 Announce Type: cross Abstract: Retrieval-Augmented Generation over knowledge graphs (Graph-RAG) has emerged as a powerful paradigm for grounding large language models in domain-spec

Non-Crossing Deep Quantile Regression for Distributional Survival Prediction

arXiv:2608.16864v1 Announce Type: cross Abstract: In survival analysis the way covariates act on the risk of an event often differs between early and late failure times, yet hazard- and mean-based sum

Not All Neighbors Matter: Understanding the Impact of Graph Sparsification on GNN Pipelines

arXiv:2603.06952v2 Announce Type: replace Abstract: As graphs scale to billions of nodes and edges, graph Machine Learning workloads are constrained by the cost of multi-hop traversals over exponentia

OceanDepths: A Global Dataset of Paired Subsurface and Surface Ocean Observations

arXiv:2608.16373v1 Announce Type: cross Abstract: Despite comprising over 70% of its surface, the world's oceans are critically underobserved compared to the land surface or the atmosphere.Understandi

On Stopping Rules and Spatial Adaptation for CART

arXiv:2608.15649v1 Announce Type: cross Abstract: The popular CART algorithm for regression trees combines a greedy splitting rule with a stopping rule, but while the splitting rule has been well stud

On Stopping Times of Power-one Sequential Tests: Tight Lower and Upper Bounds

arXiv:2504.19952v2 Announce Type: replace-cross Abstract: We present two general lower bounds for stopping times of sequential tests between arbitrary composite nulls mathcal P and alternatives mathca

On the Principles Behind Neural Network Optimizers

arXiv:2608.16760v1 Announce Type: new Abstract: Reliable optimization is central to neural network (NN) training, yet Adam, the default optimizer for modern LLMs, rests on a fragile foundation. This t

One Score, Two Decisions: Selective Prediction on the Rare-Disease Tail

arXiv:2608.14683v1 Announce Type: new Abstract: Given a patient's clinical findings, a diagnostic system ranks possible diseases and must decide when to endorse its first prediction or defer it for re

One-shot Robust Federated Learning of Independent Component Analysis

arXiv:2505.20532v2 Announce Type: replace Abstract: This paper studies robust one-shot aggregation for distributed and federated Independent Component Analysis (ICA). In this setting, each client comp

Online Convex Optimization with Dueling Feedback

arXiv:2608.15050v1 Announce Type: new Abstract: We study online convex optimization with dueling (pairwise comparison) feedback, where the learner observes only a binary preference between two queried