research

Compositional Generalization via Structural Identification in a Category-Theoretic Framework

arXiv:2608.26465v1 Announce Type: new Abstract: Compositional generalization is usually evaluated through model accuracy. We instead ask which structural or lexical identifications make held-out COGS

Content type

All Blog X Post Paper YouTube Reddit GitHub

Clear filters

Research

Cone Extended Rayleigh Quotients for Directed Graph Learning: Minimax Spectral Certificates, Sensitivity, and Adaptive Control

DGX agent

arXiv:2608.27122v1 Announce Type: new Abstract: Directed graph learning naturally leads to trainable nonsymmetric propagation operators with distinct right and left spectral structures. Building on th

researcharxiv-cs-lg

28 Aug 2026

Constraint-Aware Physics-Informed Neural Networks for Static Shape Estimation of Co-Manipulative Continuum Robots

arXiv:2608.26273v1 Announce Type: cross Abstract: Static shape estimation of co-manipulative continuum robots (CCRs) is challenging because the continuum arms and manipulated flexible object form a cl

CounterVid: Counterfactual Video Generation for Mitigating Action and Temporal Hallucinations in Video-Language Models

arXiv:2601.04778v2 Announce Type: replace-cross Abstract: Video-language models (VLMs) achieve strong multimodal understanding but remain prone to hallucinations, especially when reasoning about actio

CPGRec+: A Balance-oriented Framework for Personalized Video Game Recommendations

arXiv:2604.14586v4 Announce Type: replace-cross Abstract: The rapid expansion of gaming industry requires advanced recommender systems tailored to its dynamic landscape. Existing Graph Neural Network

CritICL: Inference-Time Weak-to-Strong Generalization from Small Language Model Failure Modes

arXiv:2608.27455v1 Announce Type: new Abstract: Recent advances in inference-time scaling have significantly improved the reasoning performance of large language models (LLMs). However, these methods

Data-driven Koopman mode approximation: A neural power iteration algorithm

arXiv:2608.26943v1 Announce Type: cross Abstract: This paper proposes a novel data-driven algorithm to approximate the dominant eigenfunctions (aka.~modes) of the Koopman operator of nonlinear dynamic

Data-efficient crack quantification in lithium-ion cathodes using foundation model transfer

arXiv:2608.27162v1 Announce Type: cross Abstract: Battery lifetime is central to sustainable electrification, yet the particle cracking that drives lithium-ion cathode aging is hard to measure: quanti

Data Science Approaches to Evaluating Honours Candidates

arXiv:2608.26135v1 Announce Type: new Abstract: We present a modular data-science pipeline for estimating public sentiment towards individuals from fragmented, unstructured open-source intelligence (O

Decentralized Multitask Learning over Learned Task Graphs

arXiv:2608.26989v1 Announce Type: new Abstract: This paper investigates decentralized multitask learning over networks when the underlying task relationships are unknown. While existing graph-regulari

Decoupled I/O-Dominant Pipelines for Large-Scale Whole-Slide Image Embedding Extraction

arXiv:2608.27278v1 Announce Type: cross Abstract: Whole-slide images (WSIs) are central to computational pathology but are prohibitively large, making patch-based processing the practical unit for fou

Dependency-Aware Revocable Decoding for Efficient Diffusion Large Language Model Inference

arXiv:2608.26574v1 Announce Type: new Abstract: Diffusion large language models (dLLMs) offer a promising alternative to autoregressive generation by decoding multiple tokens in parallel through itera

Differentiable Jitter Correction using Deep Learning-based Image Quality Metric for Phase-Contrast Micro-CT

arXiv:2608.27034v1 Announce Type: new Abstract: This paper proposes a fully differentiable jitter correction method for X-ray phase-contrast micro computed tomography using a deep learning-based image

DINOcular: Self-Supervised Visuospatial Representations

arXiv:2608.27226v1 Announce Type: new Abstract: We introduce a self-supervised framework for learning joint visuospatial representations from RGB-D observations. While modern vision foundation models

Discovering Relationships in Data Lakes Using Large Language Models: An Industrial Case

arXiv:2608.26750v1 Announce Type: new Abstract: Data lakes rely on metadata to remain usable, yet this meta data is often limited or weakly informative for column relationship discovery, especially in

Dispersive Forward Tree Search for Optimal Control: Coverage, Complexity, and Computation

arXiv:2608.26314v1 Announce Type: new Abstract: Steering-based planners require solutions to state-to-state boundary value problems, which can be inaccessible for nonlinear platforms. Forward propagat

researcharxiv-cs-ro

District-Level Food Environment Indicators and Social Vulnerability in Sao Paulo

arXiv:2608.26299v1 Announce Type: cross Abstract: Urban food environments may reflect broader socioeconomic inequalities, but district-level evidence remains limited in Brazilian cities. This study ex

Dose-PlanNet: Physics Based Radiotherapy Dose Prediction with Deep Learning

arXiv:2608.26901v1 Announce Type: cross Abstract: Automating prostate radiotherapy treatment planning is dosimetrically complex, particularly for extreme hypofractionated regimens. In this study, we i

EditaLive! Unified Character Video Editing for Live Streaming

arXiv:2608.27123v1 Announce Type: new Abstract: Conventional video editing primarily focuses on scene-level content, whereas live streaming places greater emphasis on the human subject. However, direc

EEG-to-Report: An Annotation and Feature-Text Framework for Training Language Models on Clinical EEG

arXiv:2608.26153v1 Announce Type: new Abstract: Clinical electroencephalography (EEG) reporting remains largely manual and time-consuming, and current EEG software ecosystems do not produce the struct

Enforcing Dirichlet Boundary Conditions in Operator Learning

arXiv:2608.27256v1 Announce Type: cross Abstract: Operator learning in scientific machine learning is concerned with approximation of maps between infinite-dimensional function spaces; such maps frequ

Equal Ranking Quality, Different Decisions: Training Order-Consistent LLM Scorers

arXiv:2608.26762v1 Announce Type: new Abstract: Rerankers, reward models and multi-document QA scorers score candidate documents or responses in one LLM prompt, so each score depends on their order. S

FaultLens: Learning Compact Behavioral Test Suites for Generated Operational Programs

arXiv:2608.26746v1 Announce Type: cross Abstract: Generated operational programs are often validated with either a few hand-written examples or exhaustive regression suites. The former can miss sparse

Federated Adversarial Training with Transformers

arXiv:2206.02131v2 Announce Type: replace-cross Abstract: Federated learning (FL) has emerged to enable global model training over distributed clients' data while preserving its privacy. However, the

FLAT: FLow-Aligned Training of Unrolled Networks for MRI Reconstruction

arXiv:2512.03020v3 Announce Type: replace Abstract: Unrolled networks are widely used in Magnetic Resonance Imaging (MRI) reconstruction for their efficiency. Structured as a series of neural network

FOCUS & RePAIR: Mitigating Text Degeneration via Token-Level Guidance for Pruned Large Language Models

arXiv:2608.26676v1 Announce Type: cross Abstract: Pruning is a practical approach to compress large language models (LLMs), but it can amplify text degeneration, especially repetition loops, even when

Forward-Free Diffusion Language Models with BPTT-Free Looped Refinement

arXiv:2606.08357v2 Announce Type: replace Abstract: Diffusion language models generate text through iterative denoising, offering a powerful alternative to autoregressive generation. However, discrete

FU-Mamba: A Frequency-Enhanced Dynamic Scanning Framework for Oralscan Image Segmentation

arXiv:2608.26607v1 Announce Type: new Abstract: Oralscan image segmentation is essential for computer-aided diagnosis and treatment planning in digital dentistry. However, existing visual state space

G2D: Generative-to-Discriminative Collaborative Inference for Zero-Shot Image Classification

arXiv:2608.26744v1 Announce Type: new Abstract: Zero-shot classification needs efficient label retrieval and fine-grained visual reasoning, yet discriminative and generative vision-language models fai

GameWAM: A World Action Model for Video Games

arXiv:2608.26200v1 Announce Type: new Abstract: Modern video games combine first-person perception, rapid visual changes, persistent world state, and heterogeneous native controls. Existing game agent

Gaussian Processes and Reproducing Kernel Hilbert Spaces: Connections and Equivalences

arXiv:2506.17366v2 Announce Type: replace-cross Abstract: This monograph studies the relations between two approaches using positive definite kernels: probabilistic methods using Gaussian processes, a

Gaze Authentication: Factors Influencing Authentication Performance

arXiv:2509.10969v3 Announce Type: replace Abstract: This paper examines the key factors that influence the performance of state-of-the-art gaze-based authentication. Experiments were conducted on a la

Generative Monte Carlo Sampling for Constant-Cost Particle Transport

arXiv:2512.13965v1 Announce Type: cross Abstract: We present Generative Monte Carlo (GMC), a novel paradigm for particle transport simulation that integrates generative artificial intelligence directl

Generative Semantic Scene Completion

arXiv:2608.26737v1 Announce Type: new Abstract: Outdoor LiDAR semantic scene completion (SSC) recovers a dense semantic voxel grid from a scan observing 1% of the target volume, under class imbalance

Glass Surface Detection Grounded in 3D Visual Geometry

arXiv:2608.26752v1 Announce Type: new Abstract: Glass surface detection (GSD) is critical for scene understanding and reconstruction, and yet remains challenging due to the transparency and reflectivi

Graph-Based Pseudo-multimodal Contrastive Learning for 12-Lead ECG Representations

arXiv:2608.26964v1 Announce Type: new Abstract: 12-lead electrocardiogram (ECG) is a standard, non-invasive examination widely used for diagnosing coronary artery disease, where clinical interpretatio

Hadamard Flattening and Gaussian Pooling Sketch for Least Squares with Coordinate-wise Guarantee

arXiv:2608.26552v1 Announce Type: cross Abstract: Randomized sketch-and-solve algorithms accelerate overconstrained ell_2 regression by replacing the input with a smaller problem. Standard subspace em

High-Fidelity Face Content Recovery via Tamper-Resilient Versatile Watermarking

arXiv:2603.23940v2 Announce Type: replace-cross Abstract: The proliferation of AIGC-driven face manipulation and deepfakes poses severe threats to media provenance, integrity, and copyright protection

High Probability Derivative Bounds for Random tanh Neural Networks on a Hypercube

arXiv:2608.26526v1 Announce Type: new Abstract: We establish high-probability bounds for mixed input derivatives of wide random neural networks whose activation derivatives satisfy a factorial growth

hoBIT: A Profile-Aware Retrieval-Augmented Chatbot for University Academic Advising

arXiv:2608.26604v1 Announce Type: cross Abstract: In university academic advising, identical questions can require different answers depending on a student's department, admission cohort, and degree p

How AI Experiences Art: Emergent Aesthetic Structure in a Self-Supervised Multimodal Embedding Space

arXiv:2608.27121v1 Announce Type: cross Abstract: Aesthetics are an important part of the symbolism of artistic works. Although subjective, humans categorize art based on the emotion evoked regardless

How Language Models Organize and Structure Moral Knowledge

arXiv:2608.27402v1 Announce Type: cross Abstract: How do large language models (LLMs) organize moral knowledge? Models detect moral content broadly, but detection is a low bar. We ask whether they go

How LLMs Distort Our Written Language

arXiv:2603.18161v2 Announce Type: replace-cross Abstract: Large language models (LLMs) are used by over a billion people globally, most often to assist with writing. In this work, we demonstrate that

Importance Scoring of Transformer Attention Heads in Learning Tabular Data

arXiv:2608.27241v1 Announce Type: new Abstract: Computationally demanding and opaque deep learning models can be better understood and optimized by analyzing how they transform data. While deep transf

Improving LLM Interpretability with User-Centric Chain-of-Thought Reasoning

arXiv:2608.26166v1 Announce Type: cross Abstract: Advancing reasoning capabilities allow large language models (LLMs) to tackle increasingly complex problems, while reasoning traces - intermediate ste

Inductive Correlation Clustering with Graph Neural Networks

arXiv:2608.27153v1 Announce Type: new Abstract: Correlation Clustering (CC) is a natural formulation of clustering in combinatorial optimization, which uses a graph representation of the input and doe

Interpreting Latent Protein Language Model Features with Geometric Annotations

arXiv:2608.26419v1 Announce Type: cross Abstract: Protein language models (pLMs) encode information about protein sequences which enable downstream tasks such as structure prediction, but their intern

Investigating the Influence of Prompt and Response Languages on LLM Content Generation

arXiv:2608.26186v1 Announce Type: cross Abstract: This study examines how prompt and response language influence the behavior of large language models. Using five models, we evaluated answers to 68 no