Search: “tutorials”

Four Ways to Grow a Classifier and Why One of Them Cannot Learn

arXiv:2610.00180v1 Announce Type: cross Abstract: Constructive classifiers add structure while they train: a level to a tree, a unit to a hidden layer, a split at a leaf. This paper asks what each of

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Tutorials

From Redundancy to Minimality: Fixed-Point-Guided Hierarchical Reduction of Learned Piecewise-Linear Dynamics

DGX agent

arXiv:2610.01369v1 Announce Type: new Abstract: Understanding a nonlinear dynamical system from time series requires not only reproducing its trajectories, but also identifying a simple representation

tutorialsarxiv-cs-lg

2 Oct 2026

Group-Invariant Statistics Determine Embedding Geometry: Harmonic Analysis of Representations from Bach to the Night Sky

arXiv:2610.00647v1 Announce Type: new Abstract: The representations that language models learn for concepts such as months, weekdays, and places display consistent geometric structure: circles and sad

HAWK: Rethinking Multimodal Drafting for Speculative Decoding

arXiv:2610.00623v1 Announce Type: new Abstract: Speculative decoding has achieved substantial lossless speedups for LLMs, but remains less effective for large vision-language models (LVLMs), where lig

How many labelers do you have? A closer look at gold-standard labels

arXiv:2206.12041v3 Announce Type: replace-cross Abstract: The construction of most supervised learning datasets revolves around collecting multiple labels for each instance, then aggregating the label

Information propagation dynamics in Deep Graph Networks

arXiv:2410.10464v3 Announce Type: replace Abstract: Graphs are a highly expressive abstraction for modeling entities and their relations, such as molecular structures, social networks, and traffic net

IQS-BO: In-Context Query Selection for Bayesian Optimisation

arXiv:2610.01269v1 Announce Type: new Abstract: Bayesian Optimisation (BO) is a powerful framework for the optimisation of expensive black-box functions, but typically requires refitting a surrogate a

Learn the Directions, Normalize the Gains: Post-Training Normalization for LoRA

arXiv:2610.02067v1 Announce Type: new Abstract: While Low-Rank Adaptation (LoRA) enables efficient task specialization, its learned updates can compromise capabilities beyond the target task. We ident

Learning to Ask: Information Acquisition for SLM-LLM Collaboration, under a budget

arXiv:2610.01236v1 Announce Type: new Abstract: Collaboration between a small language model (SLM) and a large language model (LLM) offers an opportunity to combine the efficiency of smaller models wi

Learning Transferable Skills using Goal-Conditioned Bisimulation

arXiv:2610.00676v1 Announce Type: cross Abstract: Unsupervised skill discovery has emerged as a promising approach for leveraging reward-free datasets to pretrain general-purpose policies. However, cu

MotionPersona: Real-Time Locomotion Control across Personas, Bodies, and Styles

arXiv:2506.00173v2 Announce Type: replace-cross Abstract: We present MotionPersona, a generative framework for character-aware locomotion control, in which the motion for a command depends on the capt

On the Relationship between Model Quantization and Model Inversion Attacks

arXiv:2610.00382v1 Announce Type: cross Abstract: Model quantization reduces the numerical precision of neural network weights and activations to lower storage and computational costs. Model inversion

PI-AMFM: Permutation-Invariant Learning for Variable-Cardinality AM-FM Mode Decomposition in Biomedical Signal Analysis

arXiv:2610.00819v1 Announce Type: cross Abstract: Physiological recordings often contain nonstationary oscillatory components whose number and dynamics vary across signals. Amplitude- and frequency-mo

Prediction-powered Neural Architecture Search

arXiv:2610.01317v1 Announce Type: new Abstract: Evaluating candidate architectures in neural architecture search (NAS) faces an inherent trade-off: on the one hand, reliable performance labels are lim

Program Learning with Verifiable Rewards: Symbolic Backpropagation for Post-Training LLMs

arXiv:2608.28421v2 Announce Type: replace Abstract: Post training a language model to reason means updating its weights. Supervised finetuning and reinforcement learning both place the acquired capabi

Questionnaire-Guided Disaggregation of Energy Appliance Use for Domestic Smart Meter Data

arXiv:2610.01297v1 Announce Type: new Abstract: Ireland's smart metering programme records electricity use at 30-minute resolution, with smart meters installed in over 80% of households as of late 202

Reason in Style: Discovering and Controlling Style in Language Models

arXiv:2610.00724v1 Announce Type: cross Abstract: Language models learn content and style jointly, making stylistic variation in their outputs difficult to identify and control. We study whether recur

RelICL: Training-free Relational Learning with Tabular Foundation Models

arXiv:2610.01725v1 Announce Type: new Abstract: Tabular foundation models achieve state-of-the-art performance on single-table tasks without any training. Recent work suggests that they are also well-

Seeing the City or Recognizing the Place? What Street-View Imagery Adds Beyond Existing Urban Data in VLM Urban Sensing

arXiv:2610.00031v1 Announce Type: new Abstract: Street-view imagery is increasingly used to infer urban attributes, but predictive accuracy alone does not reveal how much a photograph contributes beyo

SuperMotion: Source-Preserving Denoising for Text-Driven Human Motion Editing

arXiv:2610.01517v1 Announce Type: new Abstract: Text-driven human motion editing aims to realize a requested change while preserving compatible source content. Existing diffusion editors rely largely

The hidden advantage of mask resampling: a theory of masked autoencoders

arXiv:2610.01578v1 Announce Type: cross Abstract: Why can masked prediction learn useful representations that unmasked reconstruction misses? We study this question in a high-dimensional model of a ma

Two Routes to the Middle: Placement Search and Brain Readouts Converge on Where Continual Learners Should Specialize

arXiv:2610.01590v1 Announce Type: cross Abstract: Continual learners that keep a task-specific adapter in every block of a pre-trained vision transformer accumulate storage linearly with the number of

UniWAM: Unified World-Action Model

arXiv:2610.02054v1 Announce Type: new Abstract: Vision-language-action models benefit from the understanding and reasoning capabilities of pretrained vision-language models, but action-only supervisio

Useful to Whom? Sample Value Is Defined Only Relative to the Learner

arXiv:2610.00221v1 Announce Type: cross Abstract: What kind of data does a model need in order to learn? Coreset selection makes this question concrete: under a budget, keep the samples most useful fo

AdaGEPA: Adaptive Feedback Allocation for Reflective Prompt Optimization

arXiv:2609.39927v1 Announce Type: new Abstract: Prompt optimization improves the performance of language-model systems on downstream tasks by refining their prompts. Classical methods evaluate prompts

tutorialsarxiv-cs-cl

Adaptive-GEPA: Make Your Harness Fit Heterogeneous Requests

arXiv:2609.38762v1 Announce Type: cross Abstract: Reflective optimizers such as GEPA improve language model prompts from execution traces and evaluator feedback; full-program extensions can also rewri

Advancing Optimal Subset Oracle via Learning Relaxation of Neural Set Functions

arXiv:2607.11555v2 Announce Type: replace Abstract: Learning neural set functions is pivotal to a wide range of important applications, including compound selection in AI-driven drug discovery and pro

Blackboard Intelligence Can Surpass Autoregressive on Globally Constrained Problems

arXiv:2609.38806v1 Announce Type: cross Abstract: Next-token prediction has driven remarkable progress in large language models, yet a growing body of evidence suggests that they can struggle on probl

tutorialsarxiv-cs-cl

Candidate Retention for Abductive Learning

arXiv:2609.39561v1 Announce Type: cross Abstract: Abductive learning combines neural perception with symbolic reasoning, using explanations generated by abduction to supervise the perception model. Mu

ChronoGraph: Functional 4D Scene Graphs with Vision-Language Models for Interaction Understanding and Grounded Planning

arXiv:2609.39665v1 Announce Type: new Abstract: Embodied agents must determine where to act, anticipate the resulting scene changes, and interpret observed outcomes to guide subsequent actions. This r

CNCGEN: A Dataset and Framework for Machining Process Planning and Toolpath Generation from B-rep Models

arXiv:2609.39738v1 Announce Type: new Abstract: Learning to generate machining process plans and toolpaths from B-rep CAD requires coupling discrete operation decisions with continuous tool motion as

CORD: Learning Reusable Degradation Representations Across Heterogeneous Physical Systems

arXiv:2609.39784v1 Announce Type: new Abstract: Can heterogeneous physical degradation systems benefit from joint pretraining and move beyond system-specific prognostics toward reusable cross-system r

Coverage Before Control: Route-Instruction Grounding and Steering for Controllable Retrosynthesis

arXiv:2609.39955v1 Announce Type: new Abstract: Single-step retrosynthesis models are commonly evaluated by their ability to recover recorded reactions. In practice, chemists may need to choose among

CoVisco: Codec-Native Vision Encoder with Native Token Compression for Unified Image-Video Understanding

arXiv:2609.39924v1 Announce Type: cross Abstract: Vision-language models face a fundamental scaling bottleneck: the number of visual tokens grows with both temporal duration and spatial resolution, ma

Curating Synthetic Data for Task-Specific Visual Perception

arXiv:2609.38476v1 Announce Type: new Abstract: Synthetic data are most valuable where general-purpose datasets cannot provide the domain-specific priors a task requires, and where manual annotation i

DE-VAE: Revealing Uncertainty in Parametric and Inverse Projections with Variational Autoencoders using Differential Entropy

arXiv:2508.12145v5 Announce Type: replace Abstract: Recently, autoencoders (AEs) have gained interest for creating parametric and invertible projections of multidimensional data. Parametric projection

Decoupling Multi-Contrast Super-Resolution: Self-Supervised Implicit Re-Representation for Unpaired Cross-Modal Synthesis

arXiv:2505.05855v4 Announce Type: replace Abstract: Multi-contrast super-resolution (MCSR) is crucial for enhancing MRI but current deep learning methods are limited. They typically require large, pai

Decoupling Spherical Reasoning from Dense Prediction for 360 Depth Estimation

arXiv:2609.38856v1 Announce Type: new Abstract: The equirectangular projection (ERP) is widely used for panoramic depth estimation, but its spatially varying distortion makes geometry-consistent featu

Dynamics-Inspired Diffusion for Foreground-Preserving Document Background Editing

arXiv:2601.21857v2 Announce Type: replace Abstract: We revisit diffusion-based generation for structured visual content and identify a fundamental limitation of existing approaches: foreground preserv

Experience-Driven Continual Learning of Terrain Traversability for Quadruped Robots

arXiv:2609.39755v1 Announce Type: new Abstract: Safe and efficient quadruped navigation over unfamiliar terrain requires predicting terrain-robot interaction before contact: geometry and visual appear

FAST-Sync: Fast Group Synchronization for any Matrix Lie Group

arXiv:2609.38594v1 Announce Type: new Abstract: Group synchronization (GS) is the problem of estimating a set of N unknown elements g_1,ldots, g_N in G in a group G, given noisy measurements of a subs

FIGS: Evaluating Multi-Turn Sycophancy Without Penalizing Empathy

arXiv:2609.39863v1 Announce Type: new Abstract: Large language models frequently fail to balance staying truthful with being supportive. They often exhibit sycophancy in responses to users, agreeing w

From Search to Signal: Online Post-Training in Automatic Heuristic Design

arXiv:2609.39383v1 Announce Type: cross Abstract: Large language model (LLM)-based automatic heuristic design (AHD) iteratively proposes and refines heuristics, pairing design rationales with executab

Hermes: Learning Contextual Reasoning Unlocks Test-Time Scaling

arXiv:2609.38332v1 Announce Type: cross Abstract: Test-time scaling improves model performance by allocating additional compute during inference. Using this compute effectively across multiple context

How to Reduce Localization Ambiguity? Geometry-Semantic Constrained BEV Representation Learning for Satellite-Ground Localization

arXiv:2609.39127v1 Announce Type: new Abstract: Satellite-ground localization estimates the planar position and yaw orientation of a ground camera within a geo-referenced satellite image. Most recent

InfoAgent: Traceable Generation and Repair of Evidence-Grounded Infographics

arXiv:2609.39380v1 Announce Type: new Abstract: Reliable infographic generation requires facts, symbols, and visual relations to remain consistent through rendering and revision. Correcting one elemen

Introduction to Computer Vision

arXiv:2609.39627v1 Announce Type: new Abstract: This book presents a code-first introduction to computer vision, spanning classical 2D image processing, classical 3D vision, and deep learning. Organiz

Learn-Then-Differentiate Gradient Estimation

arXiv:2609.38842v1 Announce Type: cross Abstract: Learn-then-differentiate (LTD) estimates gradients by fitting a model to simulation outputs and differentiating it. We develop a unified framework exp