Search: “tutorials”

Differentially Private Conformal Prediction

arXiv:2604.14621v1 Announce Type: cross Abstract: Conformal prediction (CP) has attracted broad attention as a simple and flexible framework for uncertainty quantification through prediction sets. In

DiscoTrace: Representing and Comparing Answering Strategies of Humans and LLMs in Information-Seeking Question Answering

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arXiv:2604.15140v1 Announce Type: new Abstract: We introduce DiscoTrace, a method to identify the rhetorical strategies that answerers use when responding to information-seeking questions. DiscoTrace

tutorialsarxiv-cs-cl

17 Apr 2026

Feature Extraction in the Remote Sensing Data Value Chain: A Systematic Review of Methods and Applications

arXiv:2510.18935v3 Announce Type: replace Abstract: Earth observation involves collecting, analyzing, and processing an ever-growing mass of data. This planetary data is crucial for addressing relevan

Federated Multi-Task Clustering

arXiv:2512.22897v3 Announce Type: replace Abstract: Spectral clustering has emerged as one of the most effective clustering algorithms due to its superior performance. However, most existing models ar

FRAGATA: Semantic Retrieval of HPC Support Tickets via Hybrid RAG over 20 Years of Request Tracker History

arXiv:2604.13721v1 Announce Type: cross Abstract: The technical support team of a supercomputing centre accumulates, over the course of decades, a large volume of resolved incidents that constitute cr

From Procedural Skills to Strategy Genes: Towards Experience-Driven Test-Time Evolution

arXiv:2604.15097v1 Announce Type: cross Abstract: This beta technical report asks how reusable experience should be represented so that it can function as effective test-time control and as a substrat

How to Correctly Make Mistakes: A Framework for Constructing and Benchmarking Mistake Aware Egocentric Procedural Videos

arXiv:2604.15134v1 Announce Type: new Abstract: Reliable procedural monitoring in video requires exposure to naturally occurring human errors and the recoveries that follow. In egocentric recordings,

How to Fine-Tune a Reasoning Model? A Teacher-Student Cooperation Framework to Synthesize Student-Consistent SFT Data

arXiv:2604.14164v1 Announce Type: new Abstract: A widely adopted strategy for model enhancement is to use synthetic data generated by a stronger model for supervised fine-tuning (SFT). However, for em

Hybrid Decision Making via Conformal VLM-generated Guidance

arXiv:2604.14980v1 Announce Type: cross Abstract: Building on recent advances in AI, hybrid decision making (HDM) holds the promise of improving human decision quality and reducing cognitive load. We

KnowRL: Exploring Knowledgeable Reinforcement Learning for Factuality

arXiv:2506.19807v4 Announce Type: replace-cross Abstract: Large Language Models (LLMs), particularly slow-thinking models, often exhibit severe hallucination, outputting incorrect content due to an in

Learning temporal embeddings from electronic health records of chronic kidney disease patients

arXiv:2601.18675v2 Announce Type: replace Abstract: We investigate whether temporal embedding models trained on longitudinal electronic health records can learn clinically meaningful representations w

Learning to Plan, Planning to Learn: Adaptive Hierarchical RL-MPC for Sample-Efficient Decision Making

arXiv:2512.17091v2 Announce Type: replace Abstract: We propose a new approach for solving planning problems with a hierarchical structure, fusing reinforcement learning and MPC planning. Our formulati

Non-intrusive Learning of Physics-Informed Spatio-temporal Surrogate for Accelerating Design

arXiv:2604.14424v1 Announce Type: new Abstract: Most practical engineering design problems involve nonlinear spatio-temporal dynamical systems. Multi-physics simulations are often performed to capture

Optimal algorithmic complexity of inference in quantum kernel methods

arXiv:2604.15214v1 Announce Type: cross Abstract: Quantum kernel methods are among the leading candidates for achieving quantum advantage in supervised learning. A key bottleneck is the cost of infere

Optimizing the Network Topology of a Linear Reservoir Computer

arXiv:2509.23391v3 Announce Type: replace-cross Abstract: Machine learning has become a fundamental approach for modeling, prediction, and control, enabling systems to learn from data and perform comp

Pruning Long Chain-of-Thought of Large Reasoning Models via Small-Scale Preference Optimization

arXiv:2508.10164v2 Announce Type: replace Abstract: Recent advances in Large Reasoning Models (LRMs) have demonstrated strong performance on complex tasks through long Chain-of-Thought (CoT) reasoning

ReSS: Learning Reasoning Models for Tabular Data Prediction via Symbolic Scaffold

arXiv:2604.13392v1 Announce Type: new Abstract: Tabular data remains prevalent in high-stakes domains such as healthcare and finance, where predictive models are expected to provide both high accuracy

Stateful Evidence-Driven Retrieval-Augmented Generation with Iterative Reasoning

arXiv:2604.14170v1 Announce Type: new Abstract: Retrieval-Augmented Generation (RAG) grounds Large Language Models (LLMs) in external knowledge but often suffers from flat context representations and

Unraveling the Mechanism of Drug Binding to SARS-CoV-2 RNA Pseudoknot with Thermodynamics-Driven Machine Learning

arXiv:2604.14906v1 Announce Type: cross Abstract: The SARS-CoV-2 RNA pseudoknot is a promising target for antiviral intervention, as it regulates the efficiency of -1 programmed ribosomal frameshiftin

World-Value-Action Model: Implicit Planning for Vision-Language-Action Systems

arXiv:2604.14732v1 Announce Type: cross Abstract: Vision-Language-Action (VLA) models have emerged as a promising paradigm for building embodied agents that ground perception and language into action.

A Practitioner's Guide to Kolmogorov-Arnold Networks

arXiv:2510.25781v4 Announce Type: replace Abstract: Kolmogorov-Arnold Networks (KANs), whose design is inspired-rather than dictated-by the Kolmogorov superposition theorem, have emerged as a structur

A Review of Diffusion-based Simulation-Based Inference: Foundations and Applications in Non-Ideal Data Scenarios

arXiv:2512.23748v2 Announce Type: replace Abstract: For complex simulation problems, inferring parameters often precludes the use of classical likelihood-based techniques due to intractable likelihood

Addressing Overthinking in Large Vision-Language Models via Gated Perception-Reasoning Optimization

arXiv:2601.04442v2 Announce Type: replace-cross Abstract: Large Vision-Language Models (LVLMs) have exhibited strong reasoning capabilities through chain-of-thought mechanisms that generate step-by-st

Diffusion Language Models for Speech Recognition

arXiv:2604.14001v1 Announce Type: new Abstract: Diffusion language models have recently emerged as a leading alternative to standard language models, due to their ability for bidirectional attention a

Empowering Targeted Neighborhood Search via Hyper Tour for Large-Scale TSP

arXiv:2510.20169v3 Announce Type: replace Abstract: Traveling Salesman Problem (TSP) is a classic NP-hard problem that has garnered significant attention from both academia and industry. While neural-

Generalization Guarantees on Data-Driven Tuning of Gradient Descent with Langevin Updates

arXiv:2604.13130v1 Announce Type: new Abstract: We study learning to learn for regression problems through the lens of hyperparameter tuning. We propose the Langevin Gradient Descent Algorithm (LGD),

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

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

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

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