Search: “research”

Learning Admissible Heuristics via Cost Partitioning

arXiv:2606.04597v1 Announce Type: new Abstract: Admissible heuristics are essential for optimal planning, yet learning them remains challenging due to the risk of overestimation. Cost partitioning com

Learning Control-Affine Reduced-Order Models via Autoencoders

DGX agent

arXiv:2606.05045v1 Announce Type: cross Abstract: We present in this paper a framework for the identification of control-affine reduced-order models (ROMs). The proposed method utilizes autoencoders (

researcharxiv-cs-lg

4 Jun 2026

Learning symplectic model reduction based on a approximation theorem of symplectic embeddings

arXiv:2606.04623v1 Announce Type: new Abstract: High-dimensional Hamiltonian systems play a central role in many scientific and engineering disciplines, with dynamics evolving on symplectic manifolds.

Light or Full Verb? A Minimal-Pair Dataset for Probing Phraseological Competence in Language Models

arXiv:2606.05087v1 Announce Type: new Abstract: Frequent English verbs such as 'have' and 'make' can function either as collocates in light-verb constructions or as full lexical predicates, as in 'mak

LiSeCo: Linear Semantic Control for Language Generation

arXiv:2405.15454v4 Announce Type: replace Abstract: The prevalence of Large Language Models (LLMs) in critical applications highlights the need for controlled language generation methods that are both

LMM-IR: Large-Scale Netlist-Aware Multimodal Framework for Static IR-Drop Prediction

arXiv:2511.12581v2 Announce Type: replace Abstract: Static IR drop analysis is a fundamental and critical task in the field of chip design. Nevertheless, this process can be quite time-consuming, pote

Local Intrinsic Dimensionality of Ground Motion Data for Early Detection of Catastrophic Slope Failure

arXiv:2601.03569v3 Announce Type: replace Abstract: Local Intrinsic Dimensionality (LID) has shown strong potential for anomaly detection in high-dimensional data, including landslide failure detectio

Loss-Conditional PINNs for Parametric PDE Families

arXiv:2606.04420v1 Announce Type: new Abstract: Physics-informed neural networks (PINNs) approximate solutions of ODEs and PDEs by minimising a weighted combination of residual, boundary, initial, and

Low-Rank Decay for Grokking in Scale-Invariant Transformers: A Spectral-Geometric View

arXiv:2606.04405v1 Announce Type: cross Abstract: Modern Transformer architectures frequently employ normalization mechanisms such as RMSNorm and Query-Key Normalization, making parts of the model app

MaCo-GAN: Manifold-Contrastive Adversarial Learning for Single Image Super-Resolution

arXiv:2606.05068v1 Announce Type: new Abstract: Conventional Generative Adversarial Networks (GANs) for Single Image Super-Resolution (SISR) often struggle with hallucinated artifacts, largely because

Making Expert Reasoning Learnable with Self-Distillation

arXiv:2602.02405v2 Announce Type: replace-cross Abstract: Improving the reasoning capabilities of large language models (LLMs) typically relies either on the model's ability to sample a correct soluti

MAOAM: Unified Object and Material Selection with Vision-Language Models

arXiv:2606.04880v1 Announce Type: new Abstract: Selection is a core operation in interactive image editing. To be practical, a user should be able to specify and disambiguate the desired selection reg

Med-Banana: Learning Quality-Controlled Medical Image Editing from Success-and-Failure Trajectories

arXiv:2511.00801v4 Announce Type: replace Abstract: Text-guided medical image editing must satisfy the requested pathology while preserving anatomy, modality-specific appearance, and clinical plausibi

MeshFlow: Efficient Artistic Mesh Generation via MeshVAE and Flow-based Diffusion Transformer

arXiv:2606.04621v1 Announce Type: new Abstract: We present MeshFlow, a new method for generating artist-like 3D meshes. Current mesh generators often adopt Auto-Regressive (AR) next-token prediction,

Mid-Think: Training-Free Intermediate-Budget Reasoning via Token-Level Triggers

arXiv:2601.07036v2 Announce Type: replace-cross Abstract: Hybrid reasoning language models are commonly controlled through high-level Think/No-think instructions to regulate reasoning behavior, yet we

Model-Preserving Adaptive Rounding

arXiv:2505.22988v3 Announce Type: replace-cross Abstract: The goal of quantization is to produce a compressed model whose output distribution is as close to the original model's as possible. To do thi

MuCO: Generative Peptide Cyclization Empowered by Multi-stage Conformation Optimization

arXiv:2602.11189v2 Announce Type: replace-cross Abstract: Modeling peptide cyclization is critical for the virtual screening of candidate peptides with desirable physical and pharmaceutical properties

Multi-Granularity 3D Kidney Lesion Characterization from CT Volumes

arXiv:2606.04365v1 Announce Type: cross Abstract: Radiology reports describe kidney lesions by type, size, enhancement, and attenuation, yet existing 3D methods predict only at the patient or organ le

Multilingual Long-Form Speech Instruction Following: KIT's Submission to IWSLT 2026

arXiv:2606.04730v1 Announce Type: new Abstract: With the advent of Large Language Models, single-task and token-based multi-task models have evolved into instruction-based systems that infer task and

Near-Optimal Decentralized Stochastic Convex Optimization over Networks

arXiv:2606.04757v1 Announce Type: cross Abstract: We study decentralized stochastic smooth convex optimization, where M workers minimize an average objective using local stochastic gradients and neigh

Neural Langevin Machine: a local asymmetric learning rule can be creative

arXiv:2506.23546v2 Announce Type: replace-cross Abstract: Fixed points of recurrent neural networks can be leveraged to store and generate information. These fixed points can be captured by the Boltzm

Neural Radiated-Noise Fields for Unmanned Underwater Vehicle Noise Spectrum Prediction in Three-Dimensional Scenes

arXiv:2606.04008v1 Announce Type: cross Abstract: Radiated noise in unmanned underwater vehicles (UUVs) is an important indicator for characterizing acoustic signatures and evaluating platform perform

NLLog: Lightweight, Explainable SOC Anomaly Detection via Log-to-Language Rewriting

arXiv:2606.04957v1 Announce Type: cross Abstract: System-generated logs underpin security monitoring, yet their rigid template-based format hinders both automated analysis and human comprehension. We

Noisy memory encoding explains negative polarity illusions

arXiv:2606.04340v1 Announce Type: new Abstract: A sentence like 'The authors that no critics recommended have ever received acknowledgment for a best-selling novel' is sometimes rated as acceptable ev

Nonlocal Mean Field Schrodinger Bridge with Learned Interactions

arXiv:2606.04265v1 Announce Type: cross Abstract: The Schrodinger Bridge Problem constructs a stochastic process that connects an initial distribution to a terminal distribution with minimum energy. T

Novel Aspects of IEEE SA P3109 Arithmetic Formats for Machine Learning

arXiv:2606.04028v1 Announce Type: new Abstract: The IEEE P3109 draft standard defines a parameterized family of binary floating-point formats and associated operations, with a focus on facilitating ma

Offline-to-Online Learning in Linear Bandits

arXiv:2606.04305v1 Announce Type: new Abstract: We study online learning with an additional offline dataset in the stochastic linear bandit setting. Although this problem arises frequently in practice

On Forgetting and Stability of Score-based Generative models

arXiv:2601.21868v2 Announce Type: replace-cross Abstract: Understanding the stability and long-time behavior of generative models is a fundamental problem in modern machine learning. This paper provid

On Out-of-sample Embedding in UMAP

arXiv:2606.04451v1 Announce Type: new Abstract: Neighbor embedding algorithms reveal correlations in high-dimensional data by constructing an equivalent graph representation in a lower-dimensional spa

On the Expressive Power of Permutation-Equivariant Weight-Space Networks

arXiv:2602.01083v2 Announce Type: replace Abstract: Weight-space learning studies neural architectures that operate directly on the parameters of other neural networks. Motivated by the growing availa

OpenRFM: Dissecting Relational In-Context Learning

arXiv:2606.04320v1 Announce Type: cross Abstract: Relational Foundation Models (RFMs) promise a single pre-trained predictor that, given any relational database, returns predictions in one forward pas

Overclocking Electrostatic Generative Models

arXiv:2509.22454v2 Announce Type: replace Abstract: Electrostatic generative models such as PFGM++ have recently emerged as a powerful framework, achieving competitive performance in image synthesis.

Overview of the EReL@MIR 2025 Multimodal Document Retrieval Challenge (Track 1)

arXiv:2606.04240v1 Announce Type: cross Abstract: Retrieval over visually-rich documents, pages that interleave text with figures, tables, and charts, is essential for multimodal retrieval-augmented g

Physics-Informed Neural Engine Sound Modeling with Differentiable Pulse-Train Synthesis

arXiv:2603.09391v2 Announce Type: replace-cross Abstract: Engine sounds originate from sequential exhaust pressure pulses rather than sustained harmonic oscillations. While neural synthesis methods ty

Physics-Informed Neural Network Modeling of Biodegradable Contaminant Transport through GCL/SL Composite Liners

arXiv:2606.04392v1 Announce Type: cross Abstract: This study develops a two-domain physics-informed neural network framework for contaminant transport through a GCL/SL composite liner system, in which

Pinpoint: Grounded Worldwide Image Geolocation via Cross-Source Retrieval and Reranking

arXiv:2606.04133v1 Announce Type: new Abstract: Image geolocation aims to estimate where a photograph was taken from its visual content. At worldwide scale, this remains challenging because visual evi

Platonic Transformers: A Solid Choice For Equivariance

arXiv:2510.03511v3 Announce Type: replace-cross Abstract: While widespread, Transformers lack inductive biases for geometric symmetries common in science and computer vision. Existing equivariant meth

Prediction Under Imperfect Compression: A Theory of Approximate MDL

arXiv:2606.04834v1 Announce Type: new Abstract: Minimum Description Length (MDL) formalizes the principle of Occam's razor by optimizing the total description length: L(model)+L(data | model). For seq

Preserving Data Privacy in Learning Causal Structure with Fully Homomorphic Encryption

arXiv:2606.05129v1 Announce Type: cross Abstract: Preserving data privacy is an important topic in structural data management and data mining. However, the issue of privacy leakage in distributed caus

PureLight: Learning Complex Luminaires with Light Tracing

arXiv:2606.04319v1 Announce Type: cross Abstract: We propose a neural formulation for estimating the appearance of complex luminaires. We focus on challenging luminaires with complex light transport (

Query-based Cross-Modal Projector Bolstering Mamba Multimodal LLM

arXiv:2606.04719v1 Announce Type: new Abstract: The Transformer's quadratic complexity with input length imposes an unsustainable computational load on large language models (LLMs). In contrast, the S

R3G: A Reasoning-Retrieval-Reranking Framework for Vision-Centric Answer Generation

arXiv:2602.00104v3 Announce Type: replace-cross Abstract: Vision-centric retrieval for VQA requires retrieving images to supply missing visual cues and integrating them into the reasoning process. How

Radiomic Feature Selection Using Gradient Loss of Deep Neural Network for Lung Cancer Stage Detection

arXiv:2606.04453v1 Announce Type: new Abstract: Radiomics enables extraction of quantitative imaging biomarkers from medical images and has become an important tool for computer-aided cancer diagnosis

Read the Trace, Steer the Path: Trajectory-Aware Reinforcement Learning for Diffusion Language Models

arXiv:2606.04396v1 Announce Type: new Abstract: Diffusion large language models (dLLMs) generate responses by iteratively unmasking and revising many positions in parallel. This process leaves a rich

Read What You Hear: Reference-Free Hypotheses Evaluation with Acoustic Discrepancy

arXiv:2606.04680v1 Announce Type: cross Abstract: Automatic speech recognition systems commonly rely on reference transcriptions for evaluation, while reference-free approaches often depend on interna

Real-Time Automatic License Plate Recognition Using YOLOv8, SORT Tracking, and Temporal Data Interpolation

arXiv:2606.04684v1 Announce Type: cross Abstract: The real-time hardships of video processing seriously limit the usage of Automatic License Plate Recognition (ALPR) with application in dynamic traffi

Reasoning Shift: How Context Silently Shortens LLM Reasoning

arXiv:2604.01161v2 Announce Type: replace Abstract: Large language models (LLMs) exhibiting test-time scaling behavior, such as extended reasoning traces and self-verification, have demonstrated remar

ReConFuse: Reconstruction-Error Guided Semantic Fusion for AI-Generated Video Detection

arXiv:2606.04706v1 Announce Type: new Abstract: AI-generated videos are becoming increasingly realistic, raising serious concerns about misinformation, content authenticity, and media trust. Reliable