Search: “research”

JevSoup: System-One Routing for Training-Free LoRA Composition

arXiv:2609.30922v1 Announce Type: new Abstract: Building adaptable AI systems requires effective coordination of specialized capabilities across diverse tasks. Low-rank adaptation (LoRA) enables modul

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Kintsugi-VLA: Turning Failed Robot Rollouts into Recovery Data through Interventional Recoverability

DGX agent

arXiv:2609.31048v1 Announce Type: new Abstract: Simulation enables scalable training of Vision-Language-Action policies by using privileged experts to generate visual demonstrations without requiring

researcharxiv-cs-ro

28 Sept 2026

KneePreM: Towards 3D Knee MRI Foundation Models via Large-Scale Unlabeled Pretraining and Label-Efficient Fine-Tuning

arXiv:2609.31461v1 Announce Type: new Abstract: Background: Large volumes of unlabeled knee MRI scans are available across repositories but remain insufficiently leveraged. We developed KneePreM, a kn

LadderMIL: Multiple Instance Learning with Coarse-to-Fine Self-Distillation

arXiv:2502.02707v5 Announce Type: replace Abstract: Multiple Instance Learning (MIL) for whole slide image (WSI) analysis in computational pathology often neglects instance-level learning as supervisi

LapDDPM: Spectral Perturbation Diffusion for Robust Single-Cell Manifold Generation

arXiv:2506.13344v2 Announce Type: replace-cross Abstract: Generating high-fidelity and biologically plausible synthetic single-cell RNA sequencing (scRNA-seq) data is a critical challenge in computati

Latent Generative Solvers for Generalizable Long-Term Physics Simulation

arXiv:2602.11229v3 Announce Type: replace Abstract: Reliable physics simulation demands two capabilities that today's neural PDE solvers do not deliver together: generalization across heterogeneous PD

LEAD: An EEG Foundation Model for Alzheimer's Disease Detection

arXiv:2502.01678v5 Announce Type: replace-cross Abstract: Electroencephalography (EEG) provides a non-invasive, highly accessible, and cost-effective approach for detecting Alzheimer's disease (AD). H

Learning-Based Pressure Predictive Control of a Vertebraic Soft Robotic Tail

arXiv:2609.30479v1 Announce Type: new Abstract: Soft robots have attracted much attention for their safe human-robot interaction and flexibility, but the typical continuum structure and nonlinear mate

Learning coarse-step dynamics and internal mechanical response with graph networks

arXiv:2609.30344v1 Announce Type: new Abstract: Modern sensing records the motion of physical systems, but often leaves the forces and mechanical response governing that motion unobserved. Inferring t

Learning Hierarchical Causal Representations of the Effects of Forcings on Temperature in Climate Models

arXiv:2609.30995v1 Announce Type: new Abstract: Machine learning (ML) emulators provide a fast and cost-effective method to simulate climate change scenarios after being trained on Earth System Models

Learning Natural Conversational Behavior in Tandem Speech-to-Speech Models with Randomized Guidance

arXiv:2609.30773v1 Announce Type: new Abstract: Tandem speech-to-speech architectures couple a responsive speech frontend with an asynchronous text backend. In KAME, a large language model (LLM) serve

researcharxiv-cs-cl

Learning to Replace MCMC in Split-Gibbs Diffusion Posterior Sampling via Deep Unfolding

arXiv:2609.30539v1 Announce Type: cross Abstract: Split Gibbs sampling enables diffusion posterior inference for general nonlinear inverse problems by decoupling prior and likelihood computations, all

Light Field Primitive for Novel View Synthesis

arXiv:2609.31198v1 Announce Type: new Abstract: We present Light Field Primitives (LFP), a formulation for novel view synthesis that replaces the dense ray database with a compact set of differentiabl

LipSSM: Structurally Lipschitz-Bounded Cascaded State-Space Model via Metric Transfer between Consecutive SSM Layers

arXiv:2609.30973v1 Announce Type: new Abstract: Lipschitz continuity is a fundamental principle in the design of certifiably robust deep neural networks (DNNs), wherein adjusting the Lipschitz constan

LiTe-GS: Oracle-Efficient Next Best View Selection for 3D Gaussian Splatting

arXiv:2609.30393v1 Announce Type: new Abstract: Selecting informative camera views is critical for efficient training and adaptive refinement in 3D Gaussian Splatting, where each observation significa

Low-Bit Recurrent States in Hybrid Language Models

arXiv:2609.30950v1 Announce Type: new Abstract: Hybrid language models maintain fixed-size recurrent states, but existing quantizers typically use eight bits or more. Quantization errors persist accor

Low-Rank Friction for Memory-Efficient Transformer Pretraining

arXiv:2609.30342v1 Announce Type: cross Abstract: iKFAD is a recently proposed optimiser that replaces adaptive learning rates with adaptive friction in the momentum dynamics, yet performs as well as

MARCEDES: Score-based causal discovery under non-Gaussianity with continuous optimization

arXiv:2609.30643v1 Announce Type: cross Abstract: We consider the problem of learning the underlying causal directed acyclic graph (DAG) structure corresponding to a structural equation model (SEM) wi

Measurement and Potential Field-Based Patient Modeling for Model-Mediated Tele-ultrasound

arXiv:2509.15325v2 Announce Type: replace Abstract: Teleoperated ultrasound can improve diagnostic medical imaging access for remote communities. Having accurate force feedback is important for enabli

MedTokenBudget: Lesion-Preserving Token Routing for Dermoscopic Image Classification

arXiv:2609.30613v1 Announce Type: cross Abstract: Dermoscopy classifiers built on Vision Transformers process all image patches uniformly, although diagnostic evidence is concentrated in the lesion re

Mentored Decoding: Faster Inference meets Boosting

arXiv:2609.30474v1 Announce Type: new Abstract: Speculative decoding is a successful technique speeding up inference of a target autoregressive language model via a fast drafter model. Lossy speculati

Metacognitive Selective Ensemble for Mobile Systems

arXiv:2609.31031v1 Announce Type: new Abstract: Deep ensembles improve robustness in mobile sensing, but repeatedly executing many models over continuous sensor streams is costly. Selecting only a few

Missingness-Aware Conformal Prediction Under Cross-Hospital Distribution Shift

arXiv:2609.30781v1 Announce Type: new Abstract: Clinical measurements are recorded for some patients but not others, at rates that differ across hospitals, and marginal conformal coverage does not ens

Modeling and Generative-AI-Based Design of Load-Adaptive Gravity Balancing Mechanisms

arXiv:2609.31386v1 Announce Type: new Abstract: Load-adaptive gravity balancing mechanisms (LA-GBMs) can accommodate various loading conditions by passively changing their characteristics in response

Modeling Student Sensemaking with LLMs and Knowledge-Graph-Guided Inference

arXiv:2609.31046v1 Announce Type: new Abstract: Collaborative science learning requires nuanced interpretation of student dialogue to characterize how learners identify knowledge gaps, build explanati

researcharxiv-cs-cl

Moment-guided edge sampling

arXiv:2609.30472v1 Announce Type: new Abstract: Edge sampling makes local decisions to achieve graph-level objectives, such as preserving structural properties. This creates a fundamental challenge: e

More Sensors Only One Field: Rethinking Continual Spatio-Temporal Forecasting

arXiv:2609.31325v1 Announce Type: new Abstract: Continual spatio-temporal forecasting supports traffic management and environmental monitoring under evolving dynamics and expanding sensor networks. Ho

MoTop: Motion-Topological Model For Micro AU Detection

arXiv:2609.31285v1 Announce Type: new Abstract: Facial micro-expressions are spontaneous, brief, and subtle facial movements that reveal suppressed emotions in high-stakes environments. In contrast to

Mutable Transcripts: Mitigating Context Pollution through Editable Conversation State

arXiv:2609.31354v1 Announce Type: new Abstract: Contemporary large language model (LLM) chat systems treat conversation history as an immutable sequence of turns that defines the model's working conte

NBAvatar: Neural Billboards Avatars with Realistic Hand-Face Interaction

arXiv:2603.12063v2 Announce Type: replace Abstract: We present NBAvatar - a method for realistic rendering of head avatars handling non-rigid deformations caused by hand-face interaction. To this end,

Neural Ideals and Neural Codes: An Algebraic Framework for Neural Network Classification and Feature Interpretation

arXiv:2609.30279v1 Announce Type: new Abstract: Understanding the features captured by the hidden layers of neural networks is a fundamental challenge in machine learning, despite their widespread suc

NeuralCert: certified computational discovery of extremal mathematical constructions

arXiv:2609.30296v1 Announce Type: new Abstract: Neural networks are becoming popular in solving mathematical problems, but stochastic models do not provide mathematical exactness by themselves. This s

Nonparametric In-Context Learning under Growing Geometric Complexity: Minimax Optimality and Local Geometry-Adaptivity of Transformers

arXiv:2609.31458v1 Announce Type: cross Abstract: Transformers have become a central architecture for in-context learning (ICL), particularly through their state-of-the-art performance in large langua

Not All Memories Are Equal: Hierarchical Collaborative Memory for Validity-Aware Retrieval in LLM Agents

arXiv:2609.30289v1 Announce Type: new Abstract: In team collaboration scenarios, memory is heterogeneous and continually evolving. Team memories capture collective decisions, protocols, and current co

researcharxiv-cs-cl

OC-GS: Gaussian Splatting for Irregular Turntable Capture

arXiv:2609.31572v1 Announce Type: cross Abstract: Uneven rotation and dropped frames make equal-angle assumptions unreliable for turntable reconstruction. We present OC-GS, an object-centric Gaussian

On associative neural networks for sparse patterns with huge capacities

arXiv:2603.26217v2 Announce Type: replace-cross Abstract: Generalized Hopfield models with higher-order or exponential interaction terms are known to have substantially larger storage capacities than

OneWorld: Learning Consistent Physics Across Actions in World Models

arXiv:2609.30946v1 Announce Type: cross Abstract: Action-conditioned video world models aim to predict scene evolution under different actions, a capability that is essential for reliable planning, de

Ordinary Nonconvex SGD under Distance-Dependent Moments: Finite-Horizon Stationarity and Nagaev Bounds

arXiv:2609.30499v1 Announce Type: cross Abstract: Uniform noise-moment bounds exclude stochastic gradients whose variability increases with the iterate. We study ordinary, single-sample stochastic gra

Peer-Grounded Counterfactual Path Planning for Chronic Health Management

arXiv:2609.30838v1 Announce Type: new Abstract: Effective behavioral intervention in chronic disease management requires not a single prescription but a sequence of incremental steps, each grounded in

Peer Review as Structured Commentary: Immutable Identity, Public Dialogue, and Reproducible Scholarship

arXiv:2506.22497v2 Announce Type: replace-cross Abstract: This paper reconceptualises peer review as structured public commentary. Traditional academic validation is hindered by anonymity, latency, an

Persistent Homology of Time Series through Complex Networks

arXiv:2605.01624v1 Announce Type: cross Abstract: We present a unified pipeline for univariate time series classification via complex networks and persistent homology. A time series is mapped to a gra

PICO: Projection-Informed Consistency Optimisation for 6DoF Surgical Tool Pose Estimation

arXiv:2609.30989v1 Announce Type: new Abstract: Purpose: Accurate 6 DoF pose estimation of surgical tools is critical for automa- tion, robotic proprioception, and safe interaction with the tissue ope

POIL: Point-based One-Shot Imitation Learning with Stable Dynamical Systems

arXiv:2609.30404v1 Announce Type: new Abstract: We present POIL, a point-based one-shot imitation learning framework with stable dynamical systems. While one-shot imitation avoids collecting extensive

Predicting Transmembrane Protein Topology from 3D Structure

arXiv:2609.30446v1 Announce Type: new Abstract: This paper presents a novel approach to infer protein topology using the state-of-the-art graph neural network (GNN), SchNet. The model is trained on th

Preserve-and-Compose Training for Composed Image Retrieval

arXiv:2609.31202v1 Announce Type: new Abstract: Composed image retrieval (CIR) aims to retrieve images that satisfy a user-specified modification while preserving relevant visual content from a refere

Pretrained ASR Pseudo-labeling for Noisy Police Audio

arXiv:2609.30469v1 Announce Type: new Abstract: Pretrained ASR systems perform poorly on noisy Broadcast Police Communication (BPC), hindering efforts to understand police decision-making. Pseudo-labe

ProCAP: Probabilistic Cross-Attentive Prompt Learning for Vision-Language Models

arXiv:2609.30434v1 Announce Type: new Abstract: Pre-trained vision-language models such as CLIP can recognize new categories via prompting, but they often struggle when labeled data are scarce or the

Programs-of-Layers in LLMs through the Lens of Cortical Areas

arXiv:2609.31360v1 Announce Type: new Abstract: Inference in LLMs is conventionally a fixed-depth, fixed-order forward pass through every layer, regardless of how difficult the input is. The human bra