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Topology Structure Optimization of Reservoirs Using GLMY Homology

arXiv:2509.11612v3 Announce Type: replace Abstract: Reservoir is an efficient network for time series processing. It is well known that network structure is one of the determinants of its performance.

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Torch Geometric Pool: the PyTorch library for pooling in Graph Neural Networks

DGX agent

arXiv:2512.12642v2 Announce Type: replace Abstract: Torch Geometric Pool (tgp) is a pooling library built on top of PyTorch Geometric. Graph pooling methods differ in how they assign nodes to supernod

model-releasesarxiv-cs-lg

21 Apr 2026

Toward Consistent World Models with Multi-Token Prediction and Latent Semantic Enhancement

arXiv:2604.06155v2 Announce Type: replace-cross Abstract: Whether Large Language Models (LLMs) develop coherent internal world models remains a core debate. While conventional Next-Token Prediction (N

Toward Efficient Influence Function: Dropout as a Compression Tool

arXiv:2509.15651v2 Announce Type: replace Abstract: Assessing the impact the training data on machine learning models is crucial for understanding the behavior of the model, enhancing the transparency

Toward Reusability of AI Models Using Dynamic Updates of AI Documentation

arXiv:2604.17626v1 Announce Type: cross Abstract: This work addresses the challenge of disseminating reusable artificial intelligence (AI) models accompanied by AI documentation (a.k.a., AI model card

Towards a Data-Parameter Correspondence for LLMs: A Preliminary Discussion

model-releasesarxiv-cs-lg

arXiv:2604.17384v1 Announce Type: new Abstract: Large language model optimization has historically bifurcated into isolated data-centric and model-centric paradigms: the former manipulates involved sa

Tutorials

Towards a Foundation-Model Paradigm for Aerodynamic Prediction in Three-dimensional Design

arXiv:2604.18062v1 Announce Type: new Abstract: Accurate machine-learning models for aerodynamic prediction are essential for accelerating shape optimization, yet remain challenging to develop for com

tutorialsarxiv-cs-lg

Towards Deep Encrypted Training: Low-Latency, Memory-Efficient, and High-Throughput Inference for Privacy-Preserving Neural Networks

arXiv:2604.16834v1 Announce Type: cross Abstract: Privacy-preserving machine learning (PPML) has become increasingly important in applications where sensitive data must remain confidential. Homomorphi

Towards Disentangled Preference Optimization Dynamics Beyond Likelihood Displacement

arXiv:2604.18239v1 Announce Type: new Abstract: Preference optimization is widely used to align large language models (LLMs) with human preferences. However, many margin-based objectives suppress the

Towards E-Value Based Stopping Rules for Bayesian Deep Ensembles

arXiv:2604.18089v1 Announce Type: new Abstract: Bayesian Deep Ensembles (BDEs) represent a powerful approach for uncertainty quantification in deep learning, combining the robustness of Deep Ensembles

Towards Fully Parameter-Free Stochastic Optimization: Grid Search with Self-Bounding Analysis

model-releasesarxiv-cs-lg

arXiv:2604.16888v1 Announce Type: new Abstract: Parameter-free stochastic optimization aims to design algorithms that are agnostic to the underlying problem parameters while still achieving convergenc

Towards Generalizable Deepfake Image Detection with Vision Transformers

arXiv:2604.17376v1 Announce Type: new Abstract: In today's day and age, we face a challenge in detecting deepfake images because of the fast evolution of modern generative models and the poor generali

Towards Initialization-dependent and Non-vacuous Generalization Bounds for Overparameterized Shallow Neural Networks

arXiv:2604.00505v2 Announce Type: replace Abstract: Overparameterized neural networks often show a benign overfitting property in the sense of achieving excellent generalization behavior despite the n

Applications

Towards Intelligent Legal Document Analysis: CNN-Driven Classification of Case Law Texts

arXiv:2604.17674v1 Announce Type: new Abstract: Legal practitioners and judicial institutions face an ever-growing volume of case-law documents characterised by formalised language, lengthy sentence s

applicationsarxiv-cs-cl

Towards Joint Quantization and Token Pruning of Vision-Language Models

arXiv:2604.17320v1 Announce Type: new Abstract: Deploying Vision-Language Models (VLMs) under aggressive low-bit inference remains challenging because inference cost is dominated by the long visual-to

Towards Real-Time ECG and EMG Modeling on mu NPUs

arXiv:2604.18067v1 Announce Type: new Abstract: The miniaturisation of neural processing units (NPUs) and other low-power accelerators has enabled their integration into microcontroller-scale wearable

Towards Real-World Document Parsing via Realistic Scene Synthesis and Document-Aware Training

arXiv:2603.23885v3 Announce Type: replace Abstract: Document parsing has recently advanced with multimodal large language models (MLLMs) that directly map document images to structured outputs. Tradit

Towards Reliable Testing of Machine Unlearning

arXiv:2604.16536v1 Announce Type: new Abstract: Machine learning components are now central to AI-infused software systems, from recommendations and code assistants to clinical decision support. As re

Towards Robust Text-to-Image Person Retrieval: Multi-View Reformulation for Semantic Compensation

arXiv:2604.18376v1 Announce Type: new Abstract: In text-to-image person retrieval tasks, the diversity of natural language expressions and the implicitness of visual semantics often lead to the proble

safetyarxiv-cs-cv

Agents

Towards Self-Improving Error Diagnosis in Multi-Agent Systems

arXiv:2604.17658v1 Announce Type: cross Abstract: Large Language Model (LLM)-based Multi-Agent Systems (MAS) enable complex problem-solving but introduce significant debugging challenges, characterize

agentsarxiv-cs-cl

Towards Symmetry-sensitive Pose Estimation: A Rotation Representation for Symmetric Object Classes

arXiv:2604.18208v1 Announce Type: new Abstract: Symmetric objects are common in daily life and industry, yet their inherent orientation ambiguities that impede the training of deep learning networks f

researcharxiv-cs-cv

Towards Trustworthy Depression Estimation via Disentangled Evidential Learning

arXiv:2604.16579v1 Announce Type: new Abstract: Automated depression estimation is highly vulnerable to signal corruption and ambient noise in real-world deployment. Prevailing deterministic methods p

safetyarxiv-cs-lg

Towards Universal Skeleton-Based Action Recognition

arXiv:2604.17013v1 Announce Type: new Abstract: With the development of robotics, skeleton-based action recognition has become increasingly important, as human-robot interaction requires understanding

safetyarxiv-cs-cv

TowerDataset: A Heterogeneous Benchmark for Transmission Corridor Segmentation with a Global-Local Fusion Framework

arXiv:2604.16848v1 Announce Type: new Abstract: Fine-grained semantic segmentation of transmission-corridor point clouds is fundamental for intelligent power-line inspection. However, current progress

ToxiFrench: Benchmarking and Enhancing Language Models via CoT Fine-Tuning for French Toxicity Detection

arXiv:2508.11281v3 Announce Type: replace Abstract: Detecting toxic content using language models is crucial yet challenging. While substantial progress has been made in English, toxicity detection in

Train Separately, Merge Together: Modular Post-Training with Mixture-of-Experts

arXiv:2604.18473v1 Announce Type: new Abstract: Extending a fully post-trained language model with new domain capabilities is fundamentally limited by monolithic training paradigms: retraining from sc

safetyarxiv-cs-lg

Training for Compositional Sensitivity Reduces Dense Retrieval Generalization

arXiv:2604.16351v1 Announce Type: cross Abstract: Dense retrieval compresses texts into single embeddings ranked by cosine similarity. While efficient for recall, this interface is brittle for identit

Training-inference input alignment outweighs framework choice in longitudinal retinal image prediction

arXiv:2604.16955v1 Announce Type: new Abstract: Quantitative prediction of future retinal appearance from longitudinal imaging would support clinical decisions in progressive macular disease that curr

safetyarxiv-cs-cv

Training Language Models to Use Prolog as a Tool

arXiv:2512.07407v2 Announce Type: replace Abstract: Language models frequently produce plausible yet incorrect reasoning traces that are difficult to verify. We investigate fine-tuning models to use P

Trajectory-Restricted Optimization Conditions and Geometry-Aware Linear Convergence

arXiv:2604.17067v1 Announce Type: cross Abstract: Linear convergence of first-order methods is typically characterized by global optimization conditions whose constants reflect worst-case geometry of

Transition-Matrix Regularization for Next Dialogue Act Prediction in Counselling Conversations

arXiv:2604.18539v1 Announce Type: new Abstract: This paper studies how empirical dialogue-flow statistics can be incorporated into Next Dialogue Act Prediction (NDAP). A KL regularization term is prop

TransXion: A High-Fidelity Graph Benchmark for Realistic Anti-Money Laundering

model-releasesarxiv-cs-lg

arXiv:2604.17420v1 Announce Type: new Abstract: Money laundering poses severe risks to global financial systems, driving the widespread adoption of machine learning for transaction monitoring. However

Applications

Tree of Concepts: Interpretable Continual Learners in Non-Stationary Clinical Domains

arXiv:2604.17089v1 Announce Type: new Abstract: Continual learning aims to update models under distribution shift without forgetting, yet many high-stakes deployments, such as healthcare, also require

applicationsarxiv-cs-lg

Tri-Modal Fusion Transformers for UAV-based Object Detection

arXiv:2604.16630v1 Announce Type: new Abstract: Reliable UAV object detection requires robustness to illumination changes, motion blur, and scene dynamics that suppress RGB cues. Thermal long-wave inf

TriangleMix: Accelerating Prefilling via Decoding-time Contribution Sparsity

arXiv:2507.21526v3 Announce Type: replace Abstract: Large Language Models (LLMs) incur quadratic attention complexity with input length, creating a major time bottleneck in the prefilling stage. Exist

Local Ai

TrianguLang: Geometry-Aware Semantic Consensus for Pose-Free 3D Localization

arXiv:2603.08096v3 Announce Type: replace Abstract: Localizing objects and parts from natural language in 3D space is essential for robotics, AR, and embodied AI, yet existing methods face a trade-off

local-aiarxiv-cs-cv

Triples and Knowledge-Infused Embeddings for Clustering and Classification of Scientific Documents

arXiv:2601.08841v2 Announce Type: replace Abstract: The increasing volume and complexity of scientific literature demand robust methods for organizing and understanding research documents. In this stu

TriTS: Time Series Forecasting from a Multimodal Perspective

arXiv:2604.16748v1 Announce Type: new Abstract: Time series forecasting plays a pivotal role in critical sectors such as finance, energy, transportation, and meteorology. However, Long-term Time Serie

Applications

Trustworthy deep domain adaptation for wearable photoplethysmography signal analysis with decision-theoretic uncertainty quantification

arXiv:2604.17480v1 Announce Type: new Abstract: In principle, deep generative models can be used to perform domain adaptation; i.e. align the input feature representations of test data with that of a

applicationsarxiv-cs-lg

Local Ai

Trustworthy Endoscopic Super-Resolution

arXiv:2604.18001v1 Announce Type: new Abstract: Super-resolution (SR) models are attracting growing interest for enhancing minimally invasive surgery and diagnostic videos under hardware constraints.

local-aiarxiv-cs-cv

TSegAgent: Zero-Shot Tooth Segmentation via Geometry-Aware Vision-Language Agents

arXiv:2603.19684v2 Announce Type: replace Abstract: Automatic tooth segmentation and identification from intra-oral scanned 3D models are fundamental problems in digital dentistry, yet most existing a

researcharxiv-cs-cv

TSM-Pose: Topology-Aware Learning with Semantic Mamba for Category-Level Object Pose Estimation

arXiv:2604.16954v1 Announce Type: new Abstract: Category-level object pose estimation is fundamental for embodied intelligence, yet achieving robust generalization to unseen instances remains challeng

TSVer: A Benchmark for Fact Verification Against Time-Series Evidence

arXiv:2511.01101v2 Announce Type: replace Abstract: Reasoning over temporal and numerical data, such as time series, is a crucial aspect of fact-checking. While many systems have recently been develop

Local Ai

TWGuard: A Case Study of LLM Safety Guardrails for Localized Linguistic Contexts

arXiv:2604.16542v1 Announce Type: cross Abstract: Safety guardrails have become an active area of research in AI safety, aimed at ensuring the appropriate behavior of large language models (LLMs). How

local-aiarxiv-cs-cl

UDM-GRPO: Stable and Efficient Group Relative Policy Optimization for Uniform Discrete Diffusion Models

arXiv:2604.18518v1 Announce Type: new Abstract: Uniform Discrete Diffusion Model (UDM) has recently emerged as a promising paradigm for discrete generative modeling; however, its integration with rein

Tutorials

UGD: An Unsupervised Geometric Distance for Evaluating Real-world Noisy Point Cloud Denoising

arXiv:2604.16976v1 Announce Type: new Abstract: Point cloud denoising is a fundamental and crucial challenge in real-world point cloud applications. Existing quantitative evaluation metrics for point

tutorialsarxiv-cs-cv

Uncertainty Quantification in PINNs for Turbulent Flows: Bayesian Inference and Repulsive Ensembles

arXiv:2604.17156v1 Announce Type: new Abstract: Physics-informed neural networks (PINNs) have emerged as a promising framework for solving inverse problems governed by partial differential equations (

Uncovering Logit Suppression Vulnerabilities in LLM Safety Alignment

arXiv:2405.13068v4 Announce Type: replace-cross Abstract: Large language models (LLMs) have revolutionized various applications, making robust safety alignment essential to prevent harmful outputs. Cu

safetyarxiv-cs-lg