Search: “tutorials”

Interpretable Stylistic Variation in Human and LLM Writing Across Genres, Models, and Decoding Strategies

arXiv:2604.14111v1 Announce Type: new Abstract: Large Language Models (LLMs) are now capable of generating highly fluent, human-like text. They enable many applications, but also raise concerns such a

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Tutorials

Kwame 2.0: Human-in-the-Loop Generative AI Teaching Assistant for Large Scale Online Coding Education in Africa

DGX agent

arXiv:2603.29159v2 Announce Type: replace Abstract: Providing timely and accurate learning support in large-scale online coding courses is challenging, particularly in resource-constrained contexts. W

tutorialsarxiv-cs-cl

16 Apr 2026

Modeling Student Learning with 3.8 Million Program Traces

arXiv:2510.05056v2 Announce Type: replace Abstract: As programmers write code, they often edit and retry multiple times, creating rich 'interaction traces' that reveal how they approach coding tasks a

Multitasking Embedding for Embryo Blastocyst Grading Prediction (MEmEBG)

arXiv:2604.13217v1 Announce Type: new Abstract: Reliable evaluation of blastocyst quality is critical for the success of in vitro fertilization (IVF) treatments. Current embryo grading practices prima

Nonparametric Sparse Online Learning of the Koopman Operator

arXiv:2405.07432v4 Announce Type: replace-cross Abstract: The Koopman operator provides a powerful framework for representing the dynamics of general nonlinear dynamical systems. However, existing dat

Optimizing Earth Observation Satellite Schedules under Unknown Operational Constraints: An Active Constraint Acquisition Approach

arXiv:2604.13283v1 Announce Type: cross Abstract: Earth Observation (EO) satellite scheduling (deciding which imaging tasks to perform and when) is a well-studied combinatorial optimization problem. E

PartNerFace: Part-based Neural Radiance Fields for Animatable Facial Avatar Reconstruction

arXiv:2604.13918v1 Announce Type: new Abstract: We present PartNerFace, a part-based neural radiance fields approach, for reconstructing animatable facial avatar from monocular RGB videos. Existing so

Selecting Feature Interactions for Generalized Additive Models by Distilling Foundation Models

arXiv:2604.13332v1 Announce Type: new Abstract: Identifying meaningful feature interactions is a central challenge in building accurate and interpretable models for tabular data. Generalized additive

Sparse Goodness: How Selective Measurement Transforms Forward-Forward Learning

arXiv:2604.13081v1 Announce Type: new Abstract: The Forward-Forward (FF) algorithm is a biologically plausible alternative to backpropagation that trains neural networks layer by layer using a local g

Tokenizing Semantic Segmentation with Run Length Encoding

arXiv:2602.21627v3 Announce Type: replace Abstract: This paper presents a new unified approach to semantic segmentation in both images and videos by using language modeling to output the masks as sequ

Unsupervised domain transfer: Overcoming signal degradation in sleep monitoring by increasing scoring realism

arXiv:2604.13988v1 Announce Type: new Abstract: Objective: Investigate whether hypnogram 'realism' can be used to guide an unsupervised method for handling arbitrary types of signal degradation in mob

X-Diffusion: Training Diffusion Policies on Cross-Embodiment Human Demonstrations

arXiv:2511.04671v2 Announce Type: replace-cross Abstract: Human videos are a scalable source of training data for robot learning. However, humans and robots significantly differ in embodiment, making

Characterizing higher-order representations through generative diffusion models explains human decoded neurofeedback performance

arXiv:2503.14333v4 Announce Type: replace-cross Abstract: Brains construct not only 'first-order' representations of the environment but also 'higher-order' representations about those representations

Data-driven Reachable Set Estimation with Tunable Adversarial and Wasserstein Distributional Guarantees

arXiv:2604.12654v1 Announce Type: cross Abstract: We study finite horizon reachable set estimation for unknown discrete-time dynamical systems using only sampled state trajectories. Rather than treati

Designing for Error Recovery in Human-Robot Interaction

arXiv:2604.12473v1 Announce Type: new Abstract: This position paper looks briefly at the way we attempt to program robotic AI systems. Many AI systems are based on the idea of trying to improve the pe

tutorialsarxiv-cs-ro

Direct Discrepancy Replay: Distribution-Discrepancy Condensation and Manifold-Consistent Replay for Continual Face Forgery Detection

arXiv:2604.12941v1 Announce Type: new Abstract: Continual face forgery detection (CFFD) requires detectors to learn emerging forgery paradigms without forgetting previously seen manipulations. Existin

Generating Effective CoT Traces for Mitigating Causal Hallucination

arXiv:2604.12748v1 Announce Type: new Abstract: Although large language models (LLMs) excel in complex reasoning tasks, they suffer from severe causal hallucination in event causality identification (

Heuristic Classification of Thoughts Prompting (HCoT): Integrating Expert System Heuristics for Structured Reasoning into Large Language Models

arXiv:2604.12390v1 Announce Type: new Abstract: This paper addresses two limitations of large language models (LLMs) in solving complex problems: (1) their reasoning processes exhibit Bayesian-like st

Instantiating Bayesian CVaR lower bounds in Interactive Decision Making Problems

arXiv:2604.12519v1 Announce Type: new Abstract: Recent work established a generalized-Fano framework for lower bounding prior-predictive (Bayesian) CVaR in interactive statistical decision making. In

Iterative Compositional Data Generation for Robot Control

arXiv:2512.10891v4 Announce Type: replace-cross Abstract: Collecting robotic manipulation data is expensive, making it impractical to acquire demonstrations for the combinatorially large space of task

Joint Flashback Adaptation for Forgetting-Resistant Instruction Tuning

arXiv:2505.15467v2 Announce Type: replace-cross Abstract: Large language models have achieved remarkable success in various tasks. However, it is challenging for them to learn new tasks incrementally

KoCo: Conditioning Language Model Pre-training on Knowledge Coordinates

arXiv:2604.12397v1 Announce Type: new Abstract: Standard Large Language Model (LLM) pre-training typically treats corpora as flattened token sequences, often overlooking the real-world context that hu

Listening Deepfake Detection: A New Perspective Beyond Speaking-Centric Forgery Analysis

arXiv:2604.12650v1 Announce Type: new Abstract: Existing deepfake detection research has primarily focused on scenarios where the manipulated subject is actively speaking, i.e., generating fabricated

LLM-Guided Semantic Bootstrapping for Interpretable Text Classification with Tsetlin Machines

arXiv:2604.12223v1 Announce Type: cross Abstract: Pretrained language models (PLMs) like BERT provide strong semantic representations but are costly and opaque, while symbolic models such as the Tsetl

LLM-Guided Task- and Affordance-Level Exploration in Reinforcement Learning

arXiv:2509.16615v2 Announce Type: replace Abstract: Reinforcement learning (RL) is a promising approach for robotic manipulation, but it can suffer from low sample efficiency and requires extensive ex

tutorialsarxiv-cs-ro

NoisePrints: Distortion-Free Watermarks for Authorship in Private Diffusion Models

arXiv:2510.13793v2 Announce Type: replace Abstract: With the rapid adoption of diffusion models for visual content generation, proving authorship and protecting copyright have become critical. This ch

RLGT: A reinforcement learning framework for extremal graph theory

arXiv:2602.17276v2 Announce Type: replace Abstract: Reinforcement learning (RL) is a subfield of machine learning that focuses on developing models that can autonomously learn optimal decision-making

RPRA: Predicting an LLM-Judge for Efficient but Performant Inference

arXiv:2604.12634v1 Announce Type: new Abstract: Large language models (LLMs) face a fundamental trade-off between computational efficiency (e.g., number of parameters) and output quality, especially w

Sample Complexity of Autoregressive Reasoning: Chain-of-Thought vs. End-to-End

arXiv:2604.12013v1 Announce Type: new Abstract: Modern large language models generate text autoregressively, producing tokens one at a time. To study the learnability of such systems, Joshi et al. (CO

SDFed: Bridging Local Global Discrepancy via Subspace Refinement and Divergence Control in Federated Prompt Learning

arXiv:2602.08590v4 Announce Type: replace Abstract: Vision-language pretrained models offer strong transferable representations, yet adapting them in privacy-sensitive multi-party settings is challeng

Siamese Foundation Models for Crystal Structure Prediction

arXiv:2503.10471v2 Announce Type: replace-cross Abstract: Predicting crystal structures from chemical compositions is a fundamental challenge in materials discovery, complicated by complex 3D geometri

Social Learning Strategies for Evolved Virtual Soft Robots

arXiv:2604.12482v1 Announce Type: cross Abstract: Optimizing the body and brain of a robot is a coupled challenge: the morphology determines what control strategies are effective, while the control pa

StructDiff: A Structure-Preserving and Spatially Controllable Diffusion Model for Single-Image Generation

arXiv:2604.12575v1 Announce Type: new Abstract: This paper introduces StructDiff, a generative framework based on a single-scale diffusion model for single-image generation. Single-image generation ai

SubFlow: Sub-mode Conditioned Flow Matching for Diverse One-Step Generation

arXiv:2604.12273v1 Announce Type: cross Abstract: Flow matching has emerged as a powerful generative framework, with recent few-step methods achieving remarkable inference acceleration. However, we id

TimeSAF: Towards LLM-Guided Semantic Asynchronous Fusion for Time Series Forecasting

arXiv:2604.12648v1 Announce Type: cross Abstract: Despite the recent success of large language models (LLMs) in time-series forecasting, most existing methods still adopt a Deep Synchronous Fusion str

Ultra-low-light computer vision using trained photon correlations

arXiv:2604.11993v1 Announce Type: new Abstract: Illumination using correlated photon sources has been established as an approach to allowing high-fidelity images to be reconstructed from noisy camera

Using Learning Progressions to Guide AI Feedback for Science Learning

arXiv:2603.03249v2 Announce Type: replace Abstract: Generative artificial intelligence (AI) offers scalable support for formative feedback, yet most AI-generated feedback relies on task-specific rubri

Why Did Apple Fall: Evaluating Curiosity in Large Language Models

arXiv:2510.20635v2 Announce Type: replace-cross Abstract: Curiosity serves as a pivotal conduit for human beings to discover and learn new knowledge. Recent advancements of large language models (LLMs

A Data-driven Loss Weighting Scheme across Heterogeneous Tasks for Image Denoising

arXiv:2301.06081v4 Announce Type: replace-cross Abstract: In a variational denoising model, weight in the data fidelity term plays the role of enhancing the noise-removal capability. It is profoundly

A Diffusion-Contrastive Graph Neural Network with Virtual Nodes for Wind Nowcasting in Unobserved Regions

arXiv:2604.10328v1 Announce Type: cross Abstract: Accurate weather nowcasting remains one of the central challenges in atmospheric science, with critical implications for climate resilience, energy se

A Mechanistic Analysis of Looped Reasoning Language Models

arXiv:2604.11791v1 Announce Type: cross Abstract: Reasoning has become a central capability in large language models. Recent research has shown that reasoning performance can be improved by looping an

A robust and adaptive MPC formulation for Gaussian process models

arXiv:2507.02098v2 Announce Type: replace-cross Abstract: In this paper, we present a robust and adaptive model predictive control (MPC) framework for uncertain nonlinear systems affected by bounded d

A Tale of Two Temperatures: Simple, Efficient, and Diverse Sampling from Diffusion Language Models

arXiv:2604.09921v1 Announce Type: new Abstract: Much work has been done on designing fast and accurate sampling for diffusion language models (dLLMs). However, these efforts have largely focused on th

A Unified Theory of Sparse Dictionary Learning in Mechanistic Interpretability: Piecewise Biconvexity and Spurious Minima

arXiv:2512.05534v4 Announce Type: replace-cross Abstract: As AI models achieve remarkable capabilities across diverse domains, understanding what representations they learn and how they encode concept

AbLWR:A Context-Aware Listwise Ranking Framework for Antibody-Antigen Binding Affinity Prediction via Positive-Unlabeled Learning

arXiv:2604.11272v1 Announce Type: cross Abstract: Accurate prediction of antibody-antigen binding affinity is fundamental to therapeutic design, yet remains constrained by severe label sparsity and th

ActDistill: General Action-Guided Self-Derived Distillation for Efficient Vision-Language-Action Models

arXiv:2511.18082v3 Announce Type: replace Abstract: Recent Vision-Language-Action (VLA) models have shown impressive flexibility and generalization, yet their deployment in robotic manipulation remain

Adoption and Effectiveness of AI-Based Anomaly Detection for Cross Provider Health Data Exchange

arXiv:2604.09630v1 Announce Type: cross Abstract: This study investigates the adoption and effectiveness of AI-based anomaly detection in cross-provider electronic health record (EHR) environments. It

Advancing Reasoning in Diffusion Language Models with Denoising Process Rewards

arXiv:2510.01544v2 Announce Type: replace Abstract: Diffusion-based large language models offer a non-autoregressive alternative for text generation, but enabling them to perform complex reasoning rem