Search: “tutorials”

SeqMaestro: From nucleotide sequences to biological hypotheses through interpretable machine learning

arXiv:2609.14882v1 Announce Type: cross Abstract: Nucleotide sequence analysis is central to problems spanning regulatory genomics, evolutionary biology, and phenotype prediction. Classical bioinforma

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Tutorials

Shallow Beliefs: Synthetic document finetuning does not inoculate against emergent misalignment from reward hacking

DGX agent

arXiv:2609.14998v1 Announce Type: new Abstract: Recent work shows that models that learn to reward hack on RL environments can become broadly misaligned, and that reframing reward hacking as acceptabl

tutorialsarxiv-cs-ai

15 Sept 2026

SkyAnchor: Updating Metric-scale Aerial 3D Gaussian Scenes from Unposed Ground-View Sequences

arXiv:2609.13903v1 Announce Type: new Abstract: We study how to update a pre-built aerial scene with a newly captured, unposed ground-view sequence. The aerial scene already contains a reliable metric

Stabilizing Performative Feedback Loops with Minimal Model Deployments

arXiv:2609.14065v1 Announce Type: new Abstract: When algorithmic predictions inform people's decisions, the models we deploy are performative and actively shape the data we see. This feedback loop bet

Stochastic Gradient Descent for Operator Learning in Hilbert Spaces: Convergence Rates and Minimax Lower Bounds

arXiv:2402.04691v5 Announce Type: replace-cross Abstract: This study investigates the use of stochastic gradient descent (SGD) to learn operators between general Hilbert spaces. We study weak and stro

Sylvas: Synergistic Learning Value based Device Scheduling in Federated Continual Learning

arXiv:2609.15763v1 Announce Type: cross Abstract: Federated continual learning (FCL) enables shared global models to continuously adapt to distributed and non-stationary data streams, making it import

Synthetic Data in Marketing Research: How to Evaluate and When to Trust

arXiv:2609.13995v1 Announce Type: new Abstract: Debate over synthetic data in marketing research has polarized between claims that large language models (LLMs) make human respondents obsolete and call

ThreshGuide: Class-Aware Labeled-Guided Thresholding for Semi-Supervised 3D Abdominal Multi-Organ Segmentation

arXiv:2609.14943v1 Announce Type: new Abstract: Pseudo-labeling is a strong paradigm for semi-supervised medical image segmentation, yet its effectiveness is highly sensitive to confidence thresholdin

TimeThink: Eliciting Compositional Reasoning in Timeseries Large Language Models

arXiv:2609.13457v1 Announce Type: new Abstract: Timeseries multimodal large language models (TS-MLLMs) have recently begun leveraging the reasoning capabilities of large language models (LLMs) for que

Towards Foundation Models for 3D Scene Understanding: Instance-Aware Self-Supervised Learning for Point Clouds

arXiv:2603.25165v3 Announce Type: replace Abstract: Recent advances in self-supervised learning (SSL) for point clouds have substantially improved 3D scene understanding without human annotations. Exi

Understanding User Preferences of a Slope-Aware Variable-Admittance Filter for a Robot Guide Dog

arXiv:2609.15362v1 Announce Type: new Abstract: This letter investigates how the parameters of a slope-aware variable-admittance filter influence user preferences in force-based interaction with a rob

tutorialsarxiv-cs-ro

What resources are needed for optimal learning of bosonic Gaussian states?

arXiv:2603.18136v2 Announce Type: replace-cross Abstract: Continuous-variable systems enable key quantum technologies in computation, communication, and sensing. Bosonic Gaussian states emerge natural

Where to Compute and How to Interact: Operator-Readable Adaptation with Gauge-Aware Transport

arXiv:2609.15620v1 Announce Type: new Abstract: Adaptive meshes enable neural operators for partial differential equations (PDEs) to allocate spatial samples and computation according to local physica

Aligning Language Models with Observational Data: Opportunities and Risks from a Causal Perspective

arXiv:2506.00152v2 Announce Type: replace Abstract: Large language models are being widely used across industries to generate text that contributes directly to key performance metrics, such as medicat

Benign Loss Landscapes Can Coexist with Worst-Case Hardness

arXiv:2609.13057v1 Announce Type: new Abstract: Deep neural networks are expressive enough to contain worst-case targets that can be evaluated in polynomial time but cannot be learned in polynomial ti

Beyond Ambiguous Visual Cues: Studying Physiological Disruptions and Cross-Modal Inconsistencies in Deepfake Videos

arXiv:2609.12668v1 Announce Type: new Abstract: Recent deepfake detection studies increasingly suggest remote photoplethysmography (rPPG) signals as an authenticity cue. However, existing benchmarks l

Classical and quantum kernel fusion for two-sample testing

arXiv:2511.20941v2 Announce Type: replace-cross Abstract: Two-sample tests have been extensively employed in various scientific fields and machine learning to discriminate whether two sets of samples

Cognition on Graph: Navigating Massive Knowledge Space via Cognitive Cycles and Bidirectional Graph-Text Synergy

arXiv:2609.12791v1 Announce Type: new Abstract: Retrieval-Augmented Generation (RAG) has empowered Large Language Models (LLMs) to tackle knowledge-intensive tasks. However, navigating global, heterog

Decoding Mixture Perception through Computational Modeling of Component Interactions

arXiv:2609.11958v1 Announce Type: new Abstract: Olfaction played an indispensable role throughout human evolution and civilization. Even in the contemporary era of advanced technology, olfaction remai

Deep learning methods for inverse problems using connections between proximal operators and Hamilton-Jacobi equations

arXiv:2512.23829v3 Announce Type: replace-cross Abstract: Inverse problems are important mathematical problems that seek to recover model parameters from noisy data. Since inverse problems are often i

Diffusion Models and Concept Formation

arXiv:2609.13047v1 Announce Type: cross Abstract: Humans organize knowledge into a taxonomy of concepts with nested levels of abstraction and a basic level at which people recognize and name objects w

Efficient AI Model Deployment Using Quantization Analysis Tool

arXiv:2609.11954v1 Announce Type: new Abstract: As deep learning models are increasingly deployed on resource constrained devices, the demand for efficient model optimization techniques continues to g

FLOAT Drone: A Fully-actuated Coaxial Aerial Robot for Close-Proximity Operations

arXiv:2503.00785v2 Announce Type: replace Abstract: How to endow aerial robots with the ability to operate in close proximity remains an open problem. The core challenges lie in the propulsion system'

tutorialsarxiv-cs-ro

FRIST: FMRI Representation Informed Shared-space Training Improves EEG-only Individual-Finger BCI Decoding

arXiv:2609.12298v1 Announce Type: new Abstract: Finger-level motor decoding is important for naturalistic brain-computer interface (BCI) control, yet individual-finger decoding from scalp electroencep

Pre-Trained Low-Rank Tensor Decomposition for Multi-Dimensional Image Recovery

arXiv:2609.12843v1 Announce Type: new Abstract: Recently, tensor decompositions are prevalent for multi-dimensional image representation, which learn the instance-specific structure of each image from

Recurrent Dynamic Range Extension

arXiv:2609.13135v1 Announce Type: cross Abstract: We present an approach to progressively extend the highlights of an image. Instead of reconstructing the full dynamic range of a complex scene directl

Repair Before Reinforce: Context-Augmented Knowledge Graph Reasoning for Multi-Hop Question Answering

arXiv:2609.12230v1 Announce Type: new Abstract: Question-answering often requires reasoning across multiple connected facts rather than retrieving a single isolated relation. Knowledge graphs (KGs) pr

Representation-based Masked Diffusion Model

arXiv:2609.12382v1 Announce Type: new Abstract: Masked Diffusion Models (MDMs) have emerged as a compelling paradigm for language modeling, offering the capability for efficient parallel text generati

SAGE-Loop: Reliable Closed-Loop LLM-Driven AutoML with Trial-and-Correction and Adaptive Ensembling

arXiv:2609.12455v1 Announce Type: new Abstract: Automated machine learning (AutoML) is reshaping data-driven science and industrial practice, and as large language models are introduced into AutoML, p

SCDM: Spatial-Contextual Disentanglement Mamba via Differential Inference for Efficient Image Classification

arXiv:2609.12825v1 Announce Type: new Abstract: State Space Models (SSMs), particularly VMamba, have emerged as efficient alternatives for modeling long-range dependencies in medical image analysis. H

Transfer Learning for Evolving Domains

arXiv:2609.13039v1 Announce Type: new Abstract: Transfer learning explores how to leverage knowledge from various tasks or domains (sources) to enhance predictive performance in related tasks or domai

DCO: Dynamic Cache Orchestration for LLM Accelerators through Predictive Management

arXiv:2512.07312v2 Announce Type: replace-cross Abstract: The rapid adoption of large language models (LLMs) is pushing AI accelerators toward increasingly powerful and specialized designs. Instead of

12 Sept 2026

From Queries to Narratives: Cultural Heritage Data Stories for Knowledge Graph Exploration and Quality Assessment

arXiv:2609.11403v1 Announce Type: new Abstract: Cultural-heritage KGs such as the NFDI4Culture-KG contain millions of triples about artworks, music, inscriptions, historical events, and the people and

12 Sept 2026

GEOSTEER: Geodesic Optimization for Activation Steering in Large Language Models

arXiv:2609.10658v1 Announce Type: new Abstract: Activation steering provides a lightweight way to control large language models (LLMs) by modifying their hidden activations at inference time. Among th

Importance Weighting for Unlabeled-unlabeled Learning under Distribution Shift

arXiv:2609.10994v1 Announce Type: new Abstract: Unlabeled-unlabeled (UU) learning allows us to learn a binary classifier from two sets of unlabeled data with different class-priors. It is a general fr

Improving Faint Object Detection for Space Situational Awareness with Variational Autoencoders

arXiv:2609.11269v1 Announce Type: new Abstract: We present a deep-learning pipeline for enhancing the detection of faint moving objects in optical space situational awareness (SSA) imagery through aut

Learn the Solid, Not the File: Canonical Inputs for Neural Networks on CAD Boundary Representations

arXiv:2609.11573v1 Announce Type: new Abstract: Boundary representation (B-rep) is the standard format used by modern CAD systems for parametric 3D models. It turns out, the exact same solid can be re

Project Qualia: Recovering Experiential Music Structure from Session Co-occurrence Data

arXiv:2609.10862v1 Announce Type: cross Abstract: This report presents results from Project Qualia, an ongoing effort to determine whether experiential similarity between songs, a structure not captur

Rethinking Handwritten Character Recognition

arXiv:2609.10572v1 Announce Type: new Abstract: Non-Latin handwritten character recognition (HCR) remains understudied. Dominant methods consider it as generic image classification, which uses model s

Supply Chain Analytics: A Data-Driven Approach

arXiv:2609.10563v1 Announce Type: cross Abstract: Modern supply chain networks increasingly rely on real-time data to navigate structural uncertainties, market volatility, and operational disruptions.

The information geometry of large language models is shared, learned, and controllable

arXiv:2609.11063v1 Announce Type: cross Abstract: Large language models learn similar behaviours, yet it remains unclear what structure they share or how to change one behaviour without disturbing oth

World in World: Explore the World with World Models

arXiv:2609.11548v1 Announce Type: new Abstract: Autoregressive video world models enable interactive, long-horizon exploration, but flexible control remains challenging. Exploring a source video from

A Statistical Approach to Estimating Sample Size of Machine Learning Models

arXiv:2609.09547v1 Announce Type: new Abstract: Sample size determination for machine learning (ML) prediction models is challenging because conventional power analysis typically requires the predicto

Asking the Right Questions: Ontology-Grounded Interpretable Embeddings for Biomedical Text

arXiv:2603.01690v3 Announce Type: replace-cross Abstract: While dense biomedical embeddings achieve strong performance, their opaque dimensions limit transparency in biomedical NLP. Recent question-ba

Bag of Tricks or Bag of Myths? Reducing Modeling Complexity with Task Knowledge in Explainable Suicide Risk Assessment

arXiv:2609.07766v1 Announce Type: cross Abstract: Assessing suicide risk from social media text is a small-data, high-stakes setting requiring not only severity prediction but also supporting evidence

BrainTaskonomy: Learning How to Pretrain and What to Transfer in fMRI Foundation Models

arXiv:2609.10518v1 Announce Type: new Abstract: fMRI foundation models increasingly aggregate heterogeneous data across brain states, cohorts, and acquisition settings, yet pretraining domains are com

Building Multilingual Bridges: Data Mixing as the Pillar of Generalization for In-Language Reasoning

arXiv:2609.10445v1 Announce Type: new Abstract: Reasoning language models have made substantial advances on a variety of complex tasks, yet their capabilities remain overwhelmingly English-centric: mo

Concept drift mitigation through community and spectral graph analysis for the detectionof cyberattacks in network traffic

arXiv:2609.09442v1 Announce Type: cross Abstract: In network traffic, legitimate behaviours and attack techniques evolve jointly - the phenomenon known as 'concept drift' [1]. Every detector is thereb