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arXiv:2605.08464v1 Announce Type: new Abstract: The manifold hypothesis (MH) is often used to explain how machine learning can overcome the curse of dimensionality. However, the MH is only applicable

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Threat Modelling using Domain-Adapted Language Models: Empirical Evaluation and Insights

DGX agent

arXiv:2605.10808v1 Announce Type: cross Abstract: Large Language Models(LLMs) are increasingly explored for cybersecurity applications such as vulnerability detection. In the domain of threat modellin

researcharxiv-cs-ai

12 May 2026

Toward General and Robust LLM-enhanced Text-attributed Graph Learning

arXiv:2504.02343v2 Announce Type: replace Abstract: Recent advancements in Large Language Models (LLMs) and the proliferation of Text-Attributed Graphs (TAGs) across various domains have positioned LL

Towards Robust Sequential Decomposition for Complex Image Editing

arXiv:2605.09233v1 Announce Type: cross Abstract: Recent advances in visual generative models have enabled high-fidelity image editing guided by human instructions. However, these models often struggl

TransmissiveGS: Residual-Guided Disentangled Gaussian Splatting for Transmissive Scene Reconstruction and Rendering

arXiv:2605.10705v1 Announce Type: new Abstract: Transmissive scenes are ubiquitous in daily life, yet reconstructing and rendering them remains highly challenging due to the inherent entanglement betw

TSNN: A Non-parametric and Interpretable Framework for Traffic Time Series Forecasting

arXiv:2605.09208v1 Announce Type: new Abstract: Although many complex models were proposed to analyze time series data, some studies have demonstrated remarkable performance with simpler structures. A

Unlocking air traffic flow prediction through microscopic aircraft-state modeling

arXiv:2605.10083v1 Announce Type: new Abstract: Short-term air traffic flow prediction in terminal airspace is essential for proactive air traffic management. Existing approaches predominantly model t

VAE with Hyperspherical Coordinates: Improving Anomaly Detection from Hypervolume-Compressed Latent Space

arXiv:2601.18823v3 Announce Type: replace Abstract: Variational autoencoders (VAE) encode data into lower-dimensional latent vectors before decoding those vectors back to data. Once trained, one can h

Weakly Supervised Concept Learning for Object-centric Visual Reasoning

arXiv:2605.08201v1 Announce Type: cross Abstract: Neurosymbolic systems promise to combine deep neural network's (DNN) processing of raw sensor inputs with few-shot performance of symbolic artificial

What If We Let Forecasting Forget? A Sparse Bottleneck for Cross-Variable Dependencies

arXiv:2605.08289v1 Announce Type: cross Abstract: Multivariate time series forecasting is critical in many real-world systems, and thus modeling cross-channel dependencies is essential. Although exist

What Time Is It? How Data Geometry Makes Time Conditioning Optional for Flow Matching

arXiv:2605.08344v1 Announce Type: new Abstract: Recent work has shown that models flow matching models can be trained without explicit time conditioning, challenging the standard view that the interpo

Yield Curve Forecasting using Machine Learning and Econometrics: A Comparative Analysis

arXiv:2605.09842v1 Announce Type: new Abstract: While machine learning has revolutionized many fields such as natural language processing (NLP) and computer vision, its impact on time-series forecasti

Accelerated Relax-and-Round for Concave Coverage Problems

arXiv:2605.06900v1 Announce Type: cross Abstract: We present an accelerated relax-and-round algorithm for concave coverage problems, which generalize the classic maximum coverage problem. Building on

Approximation Error Upper and Lower Bounds for Holder Class with Transformers

arXiv:2605.07463v1 Announce Type: new Abstract: We explore the expressive power of Transformers by establishing precise approximation error upper and lower bounds for Holder class. Specifically, a new

AT-VLA: Adaptive Tactile Injection for Enhanced Feedback Reaction in Vision-Language-Action Models

arXiv:2605.07308v1 Announce Type: new Abstract: Vision-Language-Action (VLA) models have significantly advanced the capabilities of robotic agents in executing diverse tasks; however, they still face

applicationsarxiv-cs-ro

AudioFace: Language-Assisted Speech-Driven Facial Animation with Multimodal Language Models

arXiv:2605.07478v1 Announce Type: new Abstract: Speech-driven facial animation requires accurate correspondence between acoustic signals and facial motion, especially for articulation-related mouth mo

BeeVe: Unsupervised Acoustic State Discovery in Honey Bee Buzzing

arXiv:2605.07903v1 Announce Type: cross Abstract: Discovering structure in biological signals without supervision is a fundamental problem in computational intelligence, yet existing bioacoustic metho

Beyond Single Ground Truth: Reference Monism as Epistemic Injustice in ASR Evaluation

arXiv:2605.07084v1 Announce Type: new Abstract: Automatic speech recognition (ASR) evaluation compares system output to ground truth transcripts, with Word Error Rate (WER) quantifying the distance be

applicationsarxiv-cs-cl

Can LLMs Take Retrieved Information with a Grain of Salt?

arXiv:2605.06919v1 Announce Type: new Abstract: Large language models have demonstrated impressive retrieval-augmented capabilities. However, a crucial area remains underexplored: their ability to app

applicationsarxiv-cs-cl

Chordless cycle filtrations for dimensionality detection in complex networks via topological data analysis

arXiv:2509.08350v2 Announce Type: replace-cross Abstract: Many complex networks, ranging from social to biological systems, exhibit structural patterns consistent with an underlying hyperbolic geometr

Diffusion Path Samplers via Sequential Monte Carlo

arXiv:2601.21951v2 Announce Type: replace-cross Abstract: We develop diffusion-based samplers for target distributions known up to a normalising constant. To this end, we rely on the well-known diffus

Direct Reasoning Optimization: Token-Level Reasoning Reflectivity Meets Rubric Gates for Unverifiable Tasks

arXiv:2506.13351v3 Announce Type: replace-cross Abstract: Reinforcement learning (RL) training of large language models (LLMs) on unverifiable tasks is challenging even when a reasonable-quality refer

Discovering Learning-Friendly Generation Orders for Sequential Computation

arXiv:2506.23875v4 Announce Type: replace-cross Abstract: Sequential computation via autoregressive generation can make difficult tasks learnable, but the generation order of intermediate states stron

DynaRetarget: Dynamically-Feasible Retargeting using Sampling-Based Trajectory Optimization

arXiv:2602.06827v2 Announce Type: replace Abstract: In this paper, we introduce DynaRetarget, a complete pipeline for retargeting human motions to humanoid control policies. The core component of Dyna

applicationsarxiv-cs-ro

Emergence of Distortions in High-Dimensional Guided Diffusion Models

arXiv:2602.00716v4 Announce Type: replace-cross Abstract: Classifier-free guidance (CFG) is the de facto standard for conditional sampling in diffusion models, yet it often reduces sample diversity. U

Exploring CoCo Challenges in ML Engineering Teams: Insights From the Semiconductor Industry

arXiv:2605.07389v1 Announce Type: cross Abstract: The integration of machine learning (ML) into complex software systems has increased challenges in collaboration and communication (CoCo) of the teams

Fast and Effective Redistricting Optimization via Composite-Move Tabu Search

arXiv:2605.06682v1 Announce Type: new Abstract: Spatial redistricting is a practical combinatorial optimization problem that demands high-quality solutions, rapid turnaround, and flexibility to accomm

From Feasible to Practical: Pareto-Optimal Synthesis Planning

arXiv:2605.07521v1 Announce Type: new Abstract: Current computer-aided synthesis planning (CASP) methods often treat retrosynthesis as solved once a single feasible route is identified, focusing prima

From Surface Learning to Deep Understanding: A Grounded AI Tutoring System for Moodle

arXiv:2605.06963v1 Announce Type: cross Abstract: This demo paper describes the development of the AI Teaching & Learning Assistant, a modular Moodle plugin that leverages Retrieval-Augmented Generati

Hardware

GATO: GPU-Accelerated and Batched Trajectory Optimization for Scalable Edge Model Predictive Control

arXiv:2510.07625v2 Announce Type: replace Abstract: While Model Predictive Control (MPC) delivers strong performance across robotics applications, solving the underlying (batches of) nonlinear traject

hardwarearxiv-cs-ro

Geometric Analysis of Neural Regression Collapse via Intrinsic Dimension

arXiv:2510.01105v2 Announce Type: replace Abstract: Neural multivariate regression underpins a wide range of domains, including control, robotics, and finance, yet the geometry of its learned represen

Hybrid TF--IDF Logistic Regression and MLP Neural Baseline for Indonesian Three-Class Sentiment Analysis on Social Media Text

arXiv:2605.07793v1 Announce Type: new Abstract: This paper presents a compact three-class sentiment analysis study for Indonesian social media text. The task is formulated with positive, negative, and

applicationsarxiv-cs-cl

Safety

Inference-Time Attribute Distribution Alignment for Unconditional Diffusion

arXiv:2605.07456v1 Announce Type: new Abstract: Inference-time controllable generation is essential for real-world applications of unconditional diffusion models. However, most existing techniques foc

safetyarxiv-cs-lg

Intelligent Truck Matching in Full Truckload Shipments using Ping2Hex approach

arXiv:2605.07733v1 Announce Type: cross Abstract: Accurate truck-to-shipment matching using GPS data is foundational for full truckload supply chain visibility, enabling real-time tracking and accurat

LAMES: A Large-Scale and Artisanal Mining Environmental Segmentation Dataset

arXiv:2605.07740v1 Announce Type: new Abstract: Mining operations are of utmost importance to the economy of some nations. However, such operations result in land-use change, very high energy consumpt

Large Video Planner Enables Generalizable Robot Control

arXiv:2512.15840v2 Announce Type: replace-cross Abstract: General-purpose robots require decision-making models that generalize across diverse tasks and environments. Recent works build robot foundati

LLM hallucinations in the wild: Large-scale evidence from non-existent citations

arXiv:2605.07723v1 Announce Type: cross Abstract: Large language models (LLMs) are known to generate plausible but false information across a wide range of contexts, yet the real-world magnitude and c

Locally Near Optimal Piecewise Linear Regression in High Dimensions via Difference of Max-Affine Functions

arXiv:2605.06959v1 Announce Type: cross Abstract: This paper presents a parametric solution to piecewise linear regression through the Adaptive Block Gradient Descent (ABGD) algorithm. The heart of th

MinMax Recurrent Neural Cascades

arXiv:2605.06384v2 Announce Type: replace-cross Abstract: We show that the MinMax algebra provides a form of recurrence that is expressively powerful, efficiently implementable, and most importantly i

MIST: Multimodal Interactive Speech-based Tool-calling Conversational Assistants for Smart Homes

arXiv:2605.06897v1 Announce Type: cross Abstract: The rise of Internet of Things (IoT) devices in the physical world necessitates voice-based interfaces capable of handling complex user experiences. W

Multimodal synthesis of MRI and tabular data with diffusion in a joint latent space via cross-attention

arXiv:2605.06699v1 Announce Type: cross Abstract: We propose a multimodal latent diffusion model that jointly synthesizes volumetric magnetic resonance imaging (MRI) and tabular clinical data within a

Neural CDEs as Correctors for Learned Time Series Models

arXiv:2512.12116v3 Announce Type: replace Abstract: Learned time-series models, whether continuous or discrete, are widely used for forecasting the states of dynamical systems but suffer from error ac

OneViewAll: Semantic Prior Guided One-View 6D Pose Estimation for Novel Objects

arXiv:2605.07023v1 Announce Type: new Abstract: In many practical 6D object pose estimation scenarios, we often have access to only a single real-world RGB-D reference view per object, typically witho

RECON: Robust symmetry discovery via Explicit Canonical Orientation Normalization

arXiv:2505.13289v5 Announce Type: replace-cross Abstract: Real world data often exhibits unknown, instance-specific symmetries that rarely exactly match a transformation group G fixed a priori. Class-

Robust and Reliable AI for Predictive Quality in Semiconductor Materials Manufacturing with MLOps and Uncertainty Quantification

arXiv:2605.07752v1 Announce Type: new Abstract: Semiconductor materials manufacturing presents unique challenges for machine learning deployment due to evolving process conditions, equipment degradati

Script Sensitivity: Benchmarking Language Models on Unicode, Romanized and Mixed-Script Sinhala

arXiv:2601.14958v3 Announce Type: replace-cross Abstract: The performance of Language Models (LMs) on low-resource, morphologically rich languages like Sinhala remains largely unexplored, particularly

Simple KNN-Based Outlier Detection Achieves Robust Clustering

arXiv:2605.07130v1 Announce Type: new Abstract: Being robust to the presence of outliers is crucial for applying clustering algorithms in practice. In the extit{robust k-Means} problem (i.e., k-Means

Safety

Sparse Autoencoders as Plug-and-Play Firewalls for Adversarial Attack Detection in VLMs

arXiv:2605.07447v1 Announce Type: cross Abstract: Vision-language models (VLMs) have advanced rapidly and are increasingly deployed in real-world applications, especially with the rise of agent-based

safetyarxiv-cs-ai