Search: “research”

LC-QAT: Data-Efficient 2-Bit QAT for LLMs via Linear-Constrained Vector Quantization

arXiv:2606.10531v1 Announce Type: cross Abstract: Quantization-aware training (QAT) is essential for extremely low-bit large language models (LLMs). Current QAT methods are mainly based on scalar quan

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Research

Learning Where to Simulate: Generative Active Sampling for Online PDE Surrogate Training

DGX agent

arXiv:2606.09949v1 Announce Type: cross Abstract: Data-driven PDE surrogates are trained with data produced by numerical PDE solvers. However, when the surrogate's goal is to generalize across a wide

researcharxiv-cs-ai

10 Jun 2026

LieIPM: Lie Group Interior Point Method for Direct Trajectory Optimization of Rigid Bodies

arXiv:2606.10579v1 Announce Type: new Abstract: Designing dynamically feasible trajectories for rigid bodies is a fundamental problem in robotics. While direct methods are widely used, the existing co

researcharxiv-cs-ro

Limitations of Learning Tanh Neural Networks with Finite Precision

arXiv:2606.11104v1 Announce Type: new Abstract: We investigate limitations of learning anh neural networks from point evaluations under finite-precision computations and L^p accuracy guarantees, build

LLM-Guided Neural Architecture Search for Robust Co-Design of Physical Neural Networks

arXiv:2606.10294v1 Announce Type: cross Abstract: Deploying neural networks on unconventional hardware demands architectures that co-optimize task accuracy and platform-specific constraints such as en

LongMoE: Longitudinal Multimodal Learning via Trajectory-Aware Mixture-of-Experts

arXiv:2606.09907v1 Announce Type: cross Abstract: Multimodal clinical learning is increasingly important for integrating diverse patient data, including imaging, text, and personalised health records.

Lost in Serialization: Invariance and Generalization of LLM Graph Reasoners

arXiv:2511.10234v3 Announce Type: replace-cross Abstract: While promising, graph reasoners based on Large Language Models (LLMs) lack built-in invariance to symmetries in graph representations. Operat

MAD: Manifold Attracted Diffusion

arXiv:2509.24710v2 Announce Type: replace-cross Abstract: Score-based diffusion models are a highly effective method for generating samples from a distribution of images. We consider scenarios where t

Making Time Editable in Video Diffusion Transformers

arXiv:2606.10183v1 Announce Type: cross Abstract: Modern Diffusion Transformers for video generation provide limited control over the progression of time and the editing of temporal dynamics. We propo

Maximum Matching Accuracy: An Instance Segmentation Evaluation Metric Utilizing Globally Optimal Matching

arXiv:2606.10107v1 Announce Type: new Abstract: Reliable evaluation of instance segmentation models requires metrics that accurately and consistently reflect segmentation quality. However, the metrics

MetaPlate: Counterfactual-Guided RAG-LLM Tool for Personalized Food Recommendation and Hyperglycemia Prevention

arXiv:2606.10120v1 Announce Type: cross Abstract: Postprandial hyperglycemia is a key risk factor for metabolic disorders; however, existing dietary guidance is often static, impractical, and insuffic

Minimalist Genetic Programming

arXiv:2606.10237v1 Announce Type: new Abstract: Genetic programming (GP) is based on two important insights. First, that any learning task can fundamentally be posed as a program induction problem, wh

Minimum Distortion Quantization with Specified Output Distribution

arXiv:2606.10458v1 Announce Type: cross Abstract: We derive the optimal quantizer of a real-valued random variable W with distribution P_W such that 1) the distribution of the quantization output X th

Mix, Don't Pick: Why Synthetic Corpus Composition Matters for Time Series Foundation Model Pretraining

arXiv:2606.09912v1 Announce Type: cross Abstract: Choosing the wrong synthetic generator for time-series foundation model pretraining is costly: under identical training budgets, the best and worst ge

Model-Based Diffusion Sampling for Predictive Control in Offline Decision Making

arXiv:2512.08280v3 Announce Type: replace-cross Abstract: Offline decision-making via diffusion models often produces trajectories that are misaligned with system dynamics, limiting their reliability

Model-Based Reinforcement Learning in Discrete-Action Non-Markovian Reward Decision Processes

arXiv:2512.14617v2 Announce Type: replace-cross Abstract: Many practical decision-making problems involve tasks whose success depends on the entire system history, rather than on achieving a state wit

Multi-Angular Reflectance Anisotropy Observed from UAV Multispectral Imagery

arXiv:2606.10350v1 Announce Type: new Abstract: UAV multispectral imagery naturally contains multi-angular observations due to low flight altitude and wide field-of-view imaging, which may introduce g

Multimodal Brain Tumour Classification Using Feature Fusion

arXiv:2606.11107v1 Announce Type: cross Abstract: Clinicians diagnose brain tumors by synthesizing patient symptoms, medical history, and quantitative imaging data from modalities such as MRI and CT s

Near-Exponential Convergence Rates for kNN Classification based on Boltzmann Margin

arXiv:2606.10361v1 Announce Type: cross Abstract: Convergence-rate analysis for classifiers is often conducted under either Tsybakov margin or Massart margin. The former is a relatively weak condition

++nnU-Net: Scaling nnU-Net with Prefix-Based Data Augmentation

arXiv:2606.10713v1 Announce Type: cross Abstract: The nnU-Net has demonstrated continuous success in medical segmentation tasks, which heavily rely on the availability and diversity of annotated biome

NoiseSDF2NoiseSDF: Learning Clean Neural Fields from Noisy Supervision

arXiv:2507.13595v3 Announce Type: replace Abstract: Reconstructing accurate implicit surface representations from point clouds remains a challenging task, particularly when data is captured using low-

NuWa: Deriving Lightweight Class-Specific Vision Transformers for Edge Devices

arXiv:2504.03118v2 Announce Type: replace-cross Abstract: Vision Transformers (ViTs) often need to be compressed for deployment on resource-constrained edge devices like drones and smart vehicles. How

OMG: Omni-Modal Motion Generation for Generalist Humanoid Control

arXiv:2606.10340v1 Announce Type: new Abstract: Humanoid whole-body control has made significant progress in recent years, yet existing approaches remain limited to few-skill policies with heavy rewar

researcharxiv-cs-ro

On the Condition Number Dependency in Bilevel Optimization

arXiv:2511.22331v2 Announce Type: replace-cross Abstract: Bilevel optimization minimizes an objective function, defined by an upper-level problem whose feasible region is the solution of a lower-level

One Lens, Many Worlds : A Capability-Typed Interface for World-Model Interpretability

arXiv:2606.09936v1 Announce Type: cross Abstract: World models are now built on substantially different computational substrates. Latent recurrent state-space models such as PlaNet and the Dreamer fam

One Token per Multimodal Evidence: Latent Memory for Resource-Constrained QA

arXiv:2606.10572v1 Announce Type: new Abstract: External memory effectively grounds large language models (LLMs) and vision-language models (VLMs)-based question answering (QA) in relevant multimodal

Model Releases

OpenRTLSet: A Fully Open-Source Dataset for Large Language Model-based Verilog Module Design

model-releasesarxiv-cs-cl

arXiv:2606.10285v1 Announce Type: new Abstract: OpenRTLSet introduces the largest fully open-source dataset for hardware design, offering over 131,000 diverse Verilog code samples to the research comm

Optimizing 2D Input Representations and Sub-phase Fusion Strategies for Differential Diagnosis of Asthma and COPD Using CNN- and GRU-Based Networks

arXiv:2606.10972v1 Announce Type: cross Abstract: This study aims to explore the performance of the VAR model in comparison with mel-frequency cepstral coefficient (MFCC) matrices and log-mel spectrog

Optuna Constrained Tree-Structured Parzen Estimator Is a Joint Density Generalization of c-TPE

arXiv:2606.09889v1 Announce Type: new Abstract: Constrained hyperparameter optimization (HPO) is common in practice, yet Optuna's widely used constrained TPE lacks algorithmic analysis. While c-TPE pr

Overlapped Wavelet Diffusion for Low-Light Image Enhancement

arXiv:2606.10280v1 Announce Type: cross Abstract: In this study, we propose an overlapped wavelet diffusion framework for Low-Light Image Enhancement (LLIE), which incorporates two complementary compo

Parity Cross-Resonance: A Multiqubit Gate

arXiv:2508.10807v2 Announce Type: replace-cross Abstract: We present a native three-qubit entangling gate that exploits engineered interactions to realize control-control-target and control-target-tar

PathRelax: Parallel-Path Relaxed Speculative Jacobi Decoding for Accelerating Auto-Regressive Text-to-Image Generation

arXiv:2606.10492v1 Announce Type: new Abstract: The growing need for high-resolution image generation in autoregressive text-to-image models has resulted in extended token sequences, significantly inc

Patient-Level Diagnosis of Acute Myeloid Leukemia via Deep Learning Analysis of Bone Marrow Smear

arXiv:2606.10735v1 Announce Type: new Abstract: Bone marrow smear review remains important for acute myeloid leukemia (AML) assessment, but manual single-cell interpretation is labor-intensive and pat

PENet+: A Lightweight Residual Transformer Framework for Efficient Image Steganalysis

arXiv:2606.10939v1 Announce Type: new Abstract: Image steganalysis, the detection of hidden information embedded in digital images, is a core component of modern cybersecurity and digital forensics. R

PF-Trans: Physics-Embedded Frequency-Aware Transformer for Spectral Reconstruction

arXiv:2606.10373v1 Announce Type: new Abstract: Snapshot Broadband Filter Array (BFA) imaging provides high light throughput for spectral reconstruction but introduces severe spectral aliasing due to

PhysMetrics.Weather: An Evaluation Framework for Physical Consistency in ML Weather Models

arXiv:2606.10642v1 Announce Type: new Abstract: Machine learning weather prediction (MLWP) models have achieved impressive forecasting performance at a small fraction of the computational costs requir

Population-Aware Physics-Informed Neural Particle Flow for Bayesian Update

arXiv:2606.10959v1 Announce Type: new Abstract: Physics-informed neural particle flow (PINPF) learns a deterministic transport field that moves particles from a prior distribution toward a Bayesian po

Predicting Future Behaviors in Reasoning Models Enables Better Steering

arXiv:2606.11172v1 Announce Type: new Abstract: Deployed large reasoning models (LRMs) often behave unexpectedly. Test-time steering controls LRM outputs by intervening on their hidden representations

Predictive Assistance and the Temporal Dynamics of Exploratory Compression

arXiv:2606.10094v1 Announce Type: new Abstract: Classical theories of cognition describe problem solving as exploratory search through structured problem spaces in which repeated interaction gradually

Prefilling-dLLM: Predictive Prefilling for Long-Context Inference in Diffusion Language Models

arXiv:2606.10537v1 Announce Type: new Abstract: Diffusion large language models (dLLMs) re-encode the entire prefix at every denoising step, causing recomputation that scales quadratically with contex

researcharxiv-cs-cl

PRISM: Parallel Residual Iterative Sequence Model

arXiv:2602.10796v3 Announce Type: replace Abstract: Generative sequence modeling faces a fundamental tension between the expressivity of Transformers and the efficiency of linear sequence models. Exis

PrismAvatar: Pseudo-Multiview Reconstruction and Subpixel Prism Rendering for Real-Time Stereoscopic Communication

arXiv:2606.10550v1 Announce Type: new Abstract: Real-time stereoscopic video communication has long been a goal of immersive telepresence, yet practical systems still require specialized capture rigs

Profy: Interpretable Visualization of Expertise-Dependent Motor Skills Toward Supporting Piano Practice

arXiv:2606.10627v1 Announce Type: cross Abstract: The quality of piano performance depends on nuanced timing, articulation, and dynamic control, but practice feedback is often summary-based and hard t

Provenance-Grounded Gating and Adaptive Recovery in Synthetic Post-Training Data Curation

arXiv:2606.11127v1 Announce Type: cross Abstract: Synthetic post-training pipelines commonly filter generated samples with reward models or holistic LLM judges, yet two practices remain rarely examine

Provenance Tracking in AI Compilers through the Lens of Coalgebra

arXiv:2606.10937v1 Announce Type: cross Abstract: AI compilers aggressively rewrite computation graphs through normalization, lowering, and optimization, making it difficult to track the provenance of

Pushing the limits of one-dimensional NMR spectroscopy for automated structure elucidation using artificial intelligence

arXiv:2512.18531v2 Announce Type: replace-cross Abstract: One-dimensional NMR spectroscopy is one of the most widely used techniques for the characterization of organic compounds and natural products.

QSplitFL: Capability Aware Deep Q-Learning for Optimal Split Point Selection in Split Federated Learning

arXiv:2606.09869v1 Announce Type: cross Abstract: Federated Learning (FL) combined with Split Learning (SL) is a privacy preserving paradigm that enables training deep neural networks (DNNs) on resour

Range Penalization: Theoretical Insights with Applications in Federated Learning

arXiv:2606.10916v1 Announce Type: cross Abstract: This paper introduces range regularization for federated learning with linear systematic components to enhance statistical accuracy and induce cross-c