Search: “tutorials”

Spectral Reach: Understanding Neural Scaling as Progress into the Spectral Tail

arXiv:2605.31244v1 Announce Type: new Abstract: Neural scaling laws describe predictable power-law relationships between model size, dataset size, compute, and performance. While these laws guide the

All Blog X Post Paper YouTube Reddit GitHub

Clear filters

Tutorials

SSR: Scaling Surefooted and Symmetric Humanoid Traversal to the Open World

DGX agent

arXiv:2605.30770v1 Announce Type: new Abstract: Extending humanoid traversal to the open world is key to practical deployment in human environments, but remains challenging. The robot must use vision

tutorialsarxiv-cs-ro

1 Jun 2026

STEP: Learning STructured Embeddings for Progressive Time Series

arXiv:2605.31061v1 Announce Type: cross Abstract: We present a novel method for learning interpretable representations of progressive time series, that is, data capturing irreversible state transition

SWIM: Single-Instance Whole-Body Imitation for swiMming

arXiv:2605.31120v1 Announce Type: cross Abstract: We propose a new method for synthesizing physically-based swimming motions. Physically-based character animation aims to generate physically valid, co

Text-guided Feature Disentanglement for Cross-modal Gait Recognition

arXiv:2605.30784v1 Announce Type: new Abstract: Gait recognition is a biometric technique that identifies individuals based on their walking patterns, offering advantages in long-range, non-intrusive

The relative strength of hierarchical structure and statistics differs across the measures in naturalistic reading

arXiv:2509.23195v2 Announce Type: replace Abstract: The hierarchical syntactic structure and non-hierarchical, statistical, or sequential factors have long been framed as rival theories in accounting

The Terminal Representation in Reinforcement Learning

arXiv:2605.31289v1 Announce Type: cross Abstract: Representation learning is a powerful tool for spatio-temporal abstraction within reinforcement learning (RL). Two well established approaches are thr

Trading Complexity for Expressivity Through Structured Generalized Linear Token Mixing

arXiv:2605.31367v1 Announce Type: cross Abstract: Token mixing layers play a key role in how language models can learn and generate long-range dependencies. Their efficiency relies on the necessary tr

Transforming and Encoding FTS for SAT Solving: What Helps, What Hurts (Extended Version)

arXiv:2605.30563v1 Announce Type: new Abstract: Factored tasks are a classical planning representation that extends SAS+ with limited forms of disjunctive preconditions, conditional effects, and angel

Understanding the Fundamental Design Decisions of Retrieval-Augmented Generation Systems

arXiv:2411.19463v3 Announce Type: replace-cross Abstract: Retrieval-Augmented Generation (RAG) has emerged as a critical technique for enhancing large language model (LLM) capabilities. However, pract

BioArc: Discovering Optimal Neural Architectures for Biological Foundation Models

arXiv:2512.00283v3 Announce Type: replace-cross Abstract: Foundation models have revolutionized various fields such as natural language processing (NLP) and computer vision (CV). While efforts have be

CamC2V: Context-aware Controllable Video Generation

arXiv:2504.06022v3 Announce Type: replace Abstract: Recently, image-to-video (I2V) diffusion models have demonstrated impressive scene understanding and generative quality, incorporating image conditi

Causal Intelligence for Constraint-Aware Intervention Design to Induce State Transitions

arXiv:2605.29008v1 Announce Type: new Abstract: Driving a system from one state to another through targeted interventions is a fundamental challenge in science, yet most predictive models offer limite

Cooperative Variance Estimation and Bayesian Neural Networks for Disentangling Aleatoric and Epistemic Uncertainties

arXiv:2505.02743v2 Announce Type: replace Abstract: Real-world data contains aleatoric uncertainty - irreducible noise arising from imperfect measurements or from incomplete knowledge about the data g

Deep Psychovisual Image Representations

arXiv:2605.29260v1 Announce Type: new Abstract: Psychovisual models suggest human vision decouples low-level feature extraction from higher cognition by first forming intermediate abstractions. In con

Detecting Unknown Objects via Energy-based Separation for Open World Object Detection

arXiv:2603.29954v2 Announce Type: replace Abstract: In this work, we tackle the problem of Open World Object Detection (OWOD). This challenging scenario requires the detector to incrementally learn to

DiScoFormer: Plug-In Density and Score Estimation with Transformers

arXiv:2511.05924v3 Announce Type: replace Abstract: Estimating probability density and its score from samples remains a core problem in generative modeling, Bayesian inference, and kinetic theory. Exi

Domain-Specific Data Synthesis for LLMs via Minimal Sufficient Representation Learning

arXiv:2605.30039v1 Announce Type: new Abstract: Large Language Models have demonstrated remarkable progress in general-purpose capabilities and can achieve strong performance in specific domains throu

Early Detection of Misinformation for Infodemic Management: A Domain Adaptation Approach

arXiv:2406.10238v2 Announce Type: replace Abstract: An infodemic refers to an enormous amount of true information and misinformation disseminated during a disease outbreak. Detecting misinformation at

ElegantVLA: Learning When to Think for Efficient Vision-Language-Action Models

arXiv:2605.29438v1 Announce Type: new Abstract: Vision-Language-Action (VLA) models are a powerful paradigm for generalist robotic control. However, their high computational cost and limited control f

Explaining Concept Shift with Interpretable Feature Attribution

arXiv:2505.20634v2 Announce Type: replace Abstract: Concept shift occurs when the distribution of labels conditioned on the features changes between domains, which can make even a well-tuned ML model

Faithful Embeddings of Irregular and Asynchronous Data for Online Log-NCDEs

arXiv:2605.30213v1 Announce Type: new Abstract: Continuous-time models are a natural choice for irregular and asynchronous data. A central design choice is how to embed discrete observations into cont

Feedback-to-Rubrics: Can We Learn Expert Criteria from Inline Comments?

arXiv:2605.29857v1 Announce Type: new Abstract: Large language models (LLMs) are increasingly used for writing and review support, but their usefulness depends on context-dependent criteria, such as e

Financially Guided Deep Portfolio Optimization

arXiv:2605.28853v1 Announce Type: cross Abstract: Portfolio optimization in real-world financial markets is notoriously difficult due to non-stationarity, noisy data, and high transaction costs. Stand

Gated Graph Attention Networks with Learnable Temperature

arXiv:2605.29803v1 Announce Type: new Abstract: Graph attention networks learn neighbor importance through data-dependent coefficients, but standard layers lack explicit control over unreliable featur

Geometry Matters: 3D Foundation Priors for Learning Semantic Correspondence

arXiv:2605.30093v1 Announce Type: new Abstract: Foundation features from self-supervised vision models and text-to-image diffusion models have proven effective for semantic correspondence estimation.

HD-Prot: A Protein Language Model for Joint Sequence-Structure Modeling with Continuous Structure Tokens

arXiv:2512.15133v2 Announce Type: replace-cross Abstract: Proteins inherently possess a consistent sequence-structure duality. The abundance of protein sequence data, which can be readily represented

Influence-Guided Symbolic Regression: Scientific Discovery via LLM-Driven Equation Search with Granular Feedback

arXiv:2605.29184v1 Announce Type: cross Abstract: Large Language Models (LLMs) offer a promising avenue for scientific discovery, yet their application to symbolic regression is often constrained by i

Knowing What to Solve Before How: Preplan Empowered LLM Mathematical Reasoning

arXiv:2605.30245v1 Announce Type: new Abstract: Current plan-based reasoning methods improve large language models (LLMs) by inserting a planning stage before execution, giving rise to the question ri

Large Depth Completion Model from Sparse Observations

arXiv:2605.30115v1 Announce Type: new Abstract: This work presents the Large Depth Completion Model (LDCM), a simple, effective, and robust framework for single-view metric depth estimation with spars

Latent Terms: Dense Retrievers Contain Trivially Extractable BM25-ready Zipfian Vocabularies

arXiv:2605.29384v1 Announce Type: cross Abstract: We propose Latent Terms, a method revealing that models trained for dense retrieval, whether single- or multi-vector, learn representations that can t

Learn from A Rationalist: Distilling Intermediate Interpretable Rationales

arXiv:2601.22531v2 Announce Type: replace-cross Abstract: Because of the pervasive use of deep neural networks (DNNs), especially in high-stakes domains, the interpretability of DNNs has received incr

Learnable Assessment Skills for LLM-based Automated Scoring: Rubric Construction via Iterative Optimization

arXiv:2605.29274v1 Announce Type: new Abstract: LLM-based automated scoring approaches near-human performance, but scaling to new tasks remains bottlenecked by the per-item human configuration of upst

Learning and Adaptation in Wire Arc Additive Manufacturing Bead Geometry Control

arXiv:2605.29144v1 Announce Type: new Abstract: Robotics Wire Arc Additive Manufacturing (WAAM) is governed by complex and nonlinear process dynamics coupling thermal field to the build geometry. The

Learning Robust and Task-Invariant Functional Representation from fMRI through Siamese Self-Supervised Learning

arXiv:2605.28990v1 Announce Type: new Abstract: Functional magnetic resonance imaging (fMRI) is a powerful tool for investigating human brain function. However, the high cost of data acquisition and t

Learning to Feel Materials from Multisensory Tactile Data via Interpretable Models

arXiv:2605.29572v1 Announce Type: new Abstract: Human tactile perception of materials relies on complex multisensory touch cues, yet the relationship between low-level tactile signals and perceptual r

Make LLM Learn to Synthesize from Streaming Experiences through Feedback

arXiv:2605.29940v1 Announce Type: new Abstract: Large language models (LLMs) have been widely adopted for synthetic data generation, significantly reducing annotation costs. However, most existing stu

MEMENTO: Leveraging Web as a Learning Signal for Low-Data Domains

arXiv:2605.29795v1 Announce Type: new Abstract: Real-world tasks often lack large labeled datasets, motivating extensive work on learning in low-data regimes. However, existing approaches such as few-

MonoDuo: Using One Robot Arm to Learn Bimanual Policies

arXiv:2605.29298v1 Announce Type: new Abstract: Bimanual coordination is essential for many real-world manipulation tasks, yet learning bimanual robot policies is limited by the scarcity of bimanual r

Multi-level Collaborative Distillation Meets Global Workspace Model: A Unified Framework for OCIL

arXiv:2508.08677v2 Announce Type: replace-cross Abstract: Online Class-Incremental Learning (OCIL) enables models to learn continuously from non-i.i.d. data streams. Since samples of the data streams

Neural Logistic Bandits

arXiv:2505.02069v2 Announce Type: replace Abstract: We study the problem of neural logistic bandits, where the main task is to learn an unknown reward function within a logistic link function using a

NeuROK: Generative 4D Neural Object Kinematics

arXiv:2605.30347v1 Announce Type: new Abstract: Data-driven approaches have revolutionized 3D vision, enabling transformers to effectively reconstruct and generate static 3D objects. However, generati

Not All Inputs Are Valid: Towards Open-Set Video Moment Retrieval Using Language

arXiv:2605.29812v1 Announce Type: new Abstract: Video Moment Retrieval (VMR) targets to retrieve the specific moment corresponding to a sentence query from an untrimmed video. Although recent works ha

OmniAID: Decoupling Semantic and Artifacts for Universal AI-Generated Image Detection in the Wild

arXiv:2511.08423v3 Announce Type: replace Abstract: A truly universal AI-Generated Image (AIGI) detector must simultaneously generalize across diverse generative models and varied semantic content. Cu

One Mask to Rule Them All: On Hidden Facts after Editing and How to Find Them

arXiv:2605.28839v1 Announce Type: new Abstract: Knowledge editing methods such as ROME and MEMIT update factual associations in transformer models by modifying MLP weights. While evaluated mainly by o

Practical Insights on Grasp Strategies for Mobile Manipulation in the Wild

arXiv:2504.12512v2 Announce Type: replace Abstract: Mobile manipulation robots are continuously advancing, with their grasping capabilities rapidly progressing. However, there are still significant ga

PrismFlow: Residual Dynamics for Flow Matching in Time-Series Generation

arXiv:2605.28867v1 Announce Type: cross Abstract: Generating high-quality time-series data is challenging because real-world signals often exhibit multimodal patterns and multiscale dynamics, includin

Reasoning that Travels: Dissecting How Chain-of-Thought Transfers Across Models

arXiv:2605.28913v1 Announce Type: new Abstract: Large reasoning models (LRMs) often generate extensive chain-of-thought (CoT) traces before producing a final answer. As explicit textual artifacts, the