Search: “tutorials”

Model Predictive Control of Tensegrity Robots via Contact-Aware Graph Neural Dynamics Model

arXiv:2609.08958v1 Announce Type: new Abstract: Tensegrity robots offer lightweight, compliant mobility over challenging terrain but remain difficult to model and control due to complex contact-rich d

tutorialsarxiv-cs-ro

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Tutorials

Online Learning with LLM Experts from Limited Feedback

DGX agent

arXiv:2609.05820v1 Announce Type: new Abstract: We study adaptive routing of prompts to large language model (LLM) experts to maximize response quality in an online setting with limited feedback. We f

tutorialsarxiv-cs-lg

9 Sept 2026

ParetoTransport: Generative Optimization by Mass Transport Toward The Pareto Front

arXiv:2609.07706v1 Announce Type: new Abstract: Offline multi-objective optimization requires not only moving the objective vectors of candidate designs toward the Pareto front, but also distributing

Polarity-Asymmetric Structural Calibration for Link Sign Prediction

arXiv:2609.05896v1 Announce Type: cross Abstract: Link sign prediction (LSP) aims to infer the positive or negative polarity of unobserved links in signed networks. Signed Graph Neural Networks (SGNNs

Positive Sample Propagation along the Audio-Visual Event Line

arXiv:2104.00239v3 Announce Type: replace Abstract: Visual and audio signals often coexist in natural environments, forming audio-visual events (AVEs). Given a video, we aim to localize video segments

Re-calibrated Contrastive Loss for Transformation-Aware Prompt Conditioning in Vision-Language Models

arXiv:2609.06967v1 Announce Type: new Abstract: Ensuring effective transfer learning for vision-language models without compromising their generalization performance is crucial. However, many existing

Skeleton-Guided Sparse Anchors for Rotated Instance Segmentation in Cell Microscopy

arXiv:2401.09895v3 Announce Type: replace Abstract: One of the fundamental challenges in cell microscopy (MS) image analysis is instance segmentation (IS), particularly when segmenting cluster regions

TeMo: Temperature Modulation for Multimodal Contrastive Learning

arXiv:2609.07540v1 Announce Type: new Abstract: Contrastive learning approaches achieve strong performance by training models to bring similar samples closer while pushing dissimilar samples apart. A

Temporal Kolmogorov-Arnold Networks (T-KAN) for High-Frequency Limit Order Book Forecasting: Efficiency, Interpretability, and Alpha Decay

arXiv:2601.02310v2 Announce Type: replace Abstract: High-Frequency trading (HFT) environments are characterised by large volumes of limit order book (LOB) data, which is notoriously noisy and non-line

Text Has Curvature

arXiv:2602.13418v2 Announce Type: replace Abstract: Does natural language text have an intrinsic curvature? Language is increasingly modeled in curved geometries - hyperbolic spaces for hierarchy, mix

The Dynamics of Generalization in Deep Learning

arXiv:2504.16450v4 Announce Type: replace Abstract: We derive a differential equation that governs the evolution of the generalization gap when a model is trained by gradient descent-based methods. Th

The Rules-and-Facts Model for Simultaneous Generalization and Memorization in Neural Networks

arXiv:2603.25579v2 Announce Type: replace-cross Abstract: A key capability of modern neural networks is their capacity to simultaneously learn underlying rules and memorize specific facts or exception

Unifying Physics-Based Humanoid Interaction with a Context-Conditioned Interaction Prior

arXiv:2609.06591v1 Announce Type: new Abstract: Developing unified physics-based humanoid controllers that can navigate complex 3D scenes and manipulate objects remains a longstanding challenge. Exist

tutorialsarxiv-cs-ro

What Does Animal Re-Identification Learn? Linear Biological Concepts and Their Origins in Visual Representations

arXiv:2609.06020v1 Announce Type: new Abstract: Conservation increasingly relies on camera traps that collect more wildlife imagery than experts can manually analyze, making animal re-identification (

A Comparative Study of Counterfactual Explainers for Graph Neural Networks Enabling Multiple Types of Graph Edit

arXiv:2609.05113v1 Announce Type: new Abstract: Counterfactual explanations for graph-structured data seek to determine minimal and realistic modifications required in an input graph to alter a model'

Adapting from Downturns: Prediction of Long-Term Conversational-Skill Development in Mental-Health Crisis Counselors

arXiv:2609.04350v1 Announce Type: cross Abstract: How do people learn to become better conversationalists? This question is especially important in the context of mental-health counseling, where conve

An Analysis of Self-supervised Pre-training with Dependent Samples

arXiv:2609.05031v1 Announce Type: cross Abstract: Self-supervised learning relies on so-called data augmentations phi(x) of unlabeled datapoints x --- for example, masking random pixels in an image x

Attention-guided super-resolution of 4D flow MRI in carotid arteries

arXiv:2609.04891v1 Announce Type: cross Abstract: Four-dimensional (4D) flow magnetic resonance imaging (MRI) is a powerful non-invasive technique for visualizing and quantifying complex blood flow pa

Autoregressive Guidance of Deep Spatially Selective Filters using Bayesian Tracking for Efficient Extraction of Moving Speakers

arXiv:2603.23723v3 Announce Type: replace-cross Abstract: Deep spatially selective filters achieve high-quality enhancement with real-time capable architectures for stationary speakers of known direct

Better Understanding, Better Fixes? A Study of Hallucination in LLM-based Automated Program Repair

arXiv:2609.04909v1 Announce Type: cross Abstract: Large language models (LLMs) have significantly advanced automated program repair (APR), yet existing evaluations remain largely result-centric and pr

Beyond Homoscedasticity: Decoupled Uncertainty Optimization for Deep Imbalanced Regression

arXiv:2609.04995v1 Announce Type: new Abstract: Deep Imbalanced Regression (DIR) is pervasive in continuous prediction tasks across diverse modalities, such as age estimation, depth prediction, and pr

DART: Depth-as-Target Pretraining for Surgical Vision Foundation Models

arXiv:2609.04555v1 Announce Type: new Abstract: Vision foundation models (VFMs) are valuable in data-scarce domains such as surgery, where a single pretrained backbone can provide rich representations

Dual-Part Multi-Lateral Branched Network for Multi-Class Segmentation in Cardiovascular Catheterization Angiograms

arXiv:2609.04590v1 Announce Type: cross Abstract: Catheterisation image processing requires segmentation models that are fast, accurate and explainable. While most of the existing studies usually focu

Dynamic Heterogeneous Graph Representation Learning: A Survey

arXiv:2609.04779v1 Announce Type: cross Abstract: Graph representation learning (GRL) serves as a canonical paradigm for modeling complex networks. However, real-world AI systems inherently manifest a

Modular Deep Recurrent Neural Network: Application to Quadrotors

arXiv:2609.04339v1 Announce Type: new Abstract: A modular deep Recurrent Neural Network (RNN) is introduced to facilitate the process of deploying various architectures of RNNs, and to automatically c

Predicting Spatiotemporal Mobile Sensing-Based PM2.5 Concentrations Using Low-Rank Adapted Spatially Attentive Graph Neural Network

arXiv:2609.04693v1 Announce Type: new Abstract: Urban air quality can vary significantly along transit corridors, necessitating high-resolution monitoring. This work introduces a novel mobile-sensing

SMILE: Bridging Continuous Optimization and Discrete Symbolic Recovery

arXiv:2609.04639v1 Announce Type: new Abstract: Symbolic regression (SR) discovers closed-form mathematical expressions from data, offering interpretability beyond black-box models. Existing methods s

Too Rare to Learn: Prescribed Cyclone Tracks Degrade a Bay of Bengal Ocean Emulator

arXiv:2609.04635v1 Announce Type: new Abstract: Neural ocean emulators are being proposed for regional forecasting in cyclone-exposed coastal seas, and a natural design choice is to hand the network t

Topology-Aware Training and Spatial Diagnostics for Fiber Bundle Segmentation in Tracer Histology

arXiv:2609.04454v1 Announce Type: new Abstract: Anatomic tracer studies reveal how axon bundles project from an injection site, branch into smaller groups of axons, and course through the brain to rea

Towards a universal language of concepts: A survey

arXiv:2609.04528v1 Announce Type: new Abstract: Humans can learn and generalize novel concepts from sparse data because they express knowledge in rich structural formats. In this paper, we propose tha

Training Large Language Models for Small-Molecule Design with Synthetic Task Scaling

arXiv:2609.04735v1 Announce Type: new Abstract: Designing viable drug candidates requires searching a combinatorially large and rugged chemical space for molecules that satisfy multiple, often competi

TSMini: A Simple Yet Highly Effective Trajectory Similarity Learning Model

arXiv:2502.00285v3 Announce Type: replace-cross Abstract: Trajectory similarity is fundamental to many spatio-temporal data mining applications. Recent studies propose deep learning models to approxim

Ultrasound-Based Prediction of Cirrhosis Decompensation Using Large-Scale Computer Vision Models

arXiv:2609.04365v1 Announce Type: cross Abstract: Decompensation represents a critical transition in the course of cirrhosis, yet clinicians have limited non-invasive tools to reliably predict its ons

WeakMCN: Multi-task Collaborative Network for Weakly Supervised Referring Expression Comprehension and Segmentation

arXiv:2505.18686v3 Announce Type: replace Abstract: Weakly supervised referring expression comprehension(WREC) and segmentation(WRES) aim to learn object grounding based on a given expression using we

Who Should Grade My Work? Student Perspectives on Transparent AI-Assisted Writing Assessment in Higher Education

arXiv:2609.05346v1 Announce Type: new Abstract: The integration of GenAI tools into higher education assessment raises important questions about how students understand, interpret, and respond to AI-m

Why We Care About Understanding: Competence through Predictive Compression

arXiv:2609.04962v1 Announce Type: new Abstract: What is the relation between understanding and compression, and why does human understanding take such a heavily compressed form? Across information the

Beyond Retrieval: Progressive Latent Memory Evolution for Streaming Video Understanding

arXiv:2609.04131v1 Announce Type: new Abstract: Streaming video understanding requires multimodal large language models (MLLMs) to process continuous visual inputs and respond to user queries under st

Dancing with REEM-C: A robot-to-human physical-social communication study

arXiv:2408.05301v3 Announce Type: replace Abstract: Humans often work closely together and relay a wealth of information through physical interaction. Robots, on the other hand, are not yet able to wo

tutorialsarxiv-cs-ro

Data Market Design through Deep Learning

arXiv:2310.20096v2 Announce Type: replace-cross Abstract: The data market design problem is a problem in economic theory to find a set of signaling schemes (statistical experiments) to maximize expect

EPIC: Explicit Posterior Item Conditioning for Semantic ID Diffusion Recommendation

arXiv:2609.03522v1 Announce Type: cross Abstract: Semantic ID (SID) generative recommendation predicts the next item by generating a short tuple of discrete tokens. Recent masked-diffusion methods imp

From Nowcasting to Forecasting: Adapting a Reanalysis-Trained

arXiv:2609.03763v1 Announce Type: new Abstract: Accurate cloud-cover forecasts are important for temperature prediction, radiation forecasting, and solar-power operations. Short-range forecasting meth

Guide, Not Bind: Why Defeasible Priors Fail in Augmented Lagrangian Causal Discovery

arXiv:2609.03442v1 Announce Type: new Abstract: Differentiable causal discovery methods increasingly encode expert priors as forbidden-edge constraints enforced by an Augmented Lagrangian (ALM) penalt

Knowledge Acquisition During Pre-training? Large Language Models Learn Better With Auxiliary Views

arXiv:2609.04180v1 Announce Type: cross Abstract: Gaps remain in our understanding of how large language models (LLMs) acquire knowledge during pre-training. We posit that auxiliary views, reformulati

Listen to the Latents: Self-Correcting Speech Recognition in Large Audio Language Models Through Hidden-State Interactions

arXiv:2609.02940v1 Announce Type: cross Abstract: Recent automatic speech recognition (ASR) systems increasingly integrate large language models (LLMs) to leverage their semantic knowledge, either ext

LLMs Learn Better In-Context from Rules than from Examples

arXiv:2609.03213v1 Announce Type: new Abstract: Large language models (LLMs) exhibit in-context learning capabilities, where they can learn new tasks from prompt contexts without weight updates. We co

tutorialsarxiv-cs-cl

OBER+: Continuity-Aware Reporting and Traceable Continuous Improvement in Outcome-Based Education

arXiv:2609.03770v1 Announce Type: cross Abstract: Institutions practising outcome-based education compute learning outcome attainment routinely, while reviews of curriculum analytics report an absence

tutorialsarxiv-cs-cl

P-CORE: Self-Supervised Surface Consistency for Point-Based Neural Editing

arXiv:2609.03349v1 Announce Type: new Abstract: Advances in neural rendering have enabled high-fidelity multi-view reconstruction of 3D scenes. However, free-form non-rigid shape editing remains a sig

RuleMem: Active Rule Memory for Long-Term Conversational Agents

arXiv:2609.03915v1 Announce Type: new Abstract: Question answering agents in long-term conversations must reason over massive, temporally dispersed dialogue histories. However, existing memory mechani

tutorialsarxiv-cs-cl