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Multi-Marginal Inverse Optimal Transport for Contrastive Learning Via Explicit Anchor-Positive-Negative Coupling

arXiv:2609.33741v1 Announce Type: cross Abstract: Inverse Optimal Transport (OT) based methods for representation learning learn representations such that the global OT coupling between a pair of data

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Tutorials

Not All Rollouts Are Worth Learning: On Trajectory Valuation for Post-Training Reinforcement Learning

DGX agent

arXiv:2609.35072v1 Announce Type: new Abstract: We consider the problem of trajectory valuation in reinforcement learning: how to identify and mitigate detrimental trajectories during online training.

tutorialsarxiv-cs-lg

29 Sept 2026

Observability-Informed Optimal Sensor Placement for Soft Robots

arXiv:2609.33681v1 Announce Type: new Abstract: This paper presents the application and experimental evaluation of a systematic method for optimal sensor placement in soft robots. Existing methods eit

On the Relation Between Interval Regret and Dynamic Regret

arXiv:2609.34423v1 Announce Type: new Abstract: Non-stationary online learning has attracted much attention in recent years, as static regret is insufficient to guide algorithm design in changing envi

Optimal Transport Dropout for Structured Predictive Uncertainty

arXiv:2609.33377v1 Announce Type: new Abstract: Deterministic neural networks and neural operators provide point predictions with no intrinsic measure of reliability. Yet, predictive uncertainty may s

Period Segmentation in Transition Network Analysis: A Topological Data Analysis Approach

arXiv:2609.32094v1 Announce Type: cross Abstract: Temporal dynamics in learning behavior can be revealed through period segmentation in Transition Network Analysis (TNA). Cristea et al. demonstrated t

Predictive Dual Smoothing for Column Generation

arXiv:2609.34740v1 Announce Type: new Abstract: Solving large-scale linear programs efficiently is an important challenge in many optimization settings. A key technique is column generation, which alt

QiYao-M: Multimodal Time Series Foundation Model with Role-Aware Modeling of Endogenous and Exogenous Modalities

arXiv:2609.34842v1 Announce Type: new Abstract: Existing multimodal time series foundation models (TSFMs) typically model heterogeneous modalities through largely shared mechanisms, overlooking the di

Retimed Bellman Flows: Escaping the Impossible Triangle of Velocity Bootstrapping

arXiv:2609.32828v1 Announce Type: new Abstract: Flow critics learn return distributions by transporting Gaussian noise to Bellman endpoints via continuous velocity fields. While velocity bootstrapping

Revitalizing Medical Time Series with Vision-Informed Retrieval: A Vision-Language Perspective

arXiv:2609.34652v1 Announce Type: new Abstract: Medical time series (MedTS) underpin many clinical classification tasks, yet existing methods usually represent them only as numerical sequences and und

RT-Super: Learning Tumor Segmentation from Longitudinal Images and Reports

arXiv:2609.35637v1 Announce Type: new Abstract: Multi-tumor segmentation is important for early cancer detection and allows radiologists to visualize, verify, and understand AI predictions. However, t

S^2COPE: Self-Supervised Concept Discovery via Preference Learning

arXiv:2606.14586v2 Announce Type: replace Abstract: Self-supervised learning can learn useful representations from unlabeled images. Yet these representations remain largely opaque, making it difficul

SeLMRoute: Probabilistic Semantic Evidence for Large Language Model Routing

arXiv:2609.34736v1 Announce Type: cross Abstract: Large language model (LLM) routing aims to select the most suitable model for each incoming query. Most existing routers learn this decision directly

Sharp training-conditional coverage for conformal prediction under covariate shift

arXiv:2609.33456v1 Announce Type: cross Abstract: Weighted split conformal prediction reweights calibration scores by the likelihood ratio between the test and training covariate distributions and gua

SphMind: Towards Robust, Training-Free VLM-based Spatial Reasoning with a 360 Camera

arXiv:2609.33462v1 Announce Type: new Abstract: Omnidirectional or 360 cameras provide embodied AI agents with a holistic, wide field-of-view (FoV) view of their surroundings, motivating the use of Mu

Staying on the Attractor: Supervising Neural Surrogates of 3D Turbulence Where They Leave It

arXiv:2609.32864v1 Announce Type: new Abstract: Neural surrogates are trained to predict 3D turbulent flows in place of direct numerical simulation (DNS). For chaotic flows, the goal is short-term poi

Style-Driven Data Synthesis and Degradation-Aware Enhancement for Ultrasound Image Restoration

arXiv:2609.35120v1 Announce Type: new Abstract: Low-cost handheld ultrasound devices can be widely deployed compared to professional hospital ultrasound machines. However, their images suffer from com

The Geometry of Logic: Stratification Induces Semantic Structure and Robust Reasoning

arXiv:2609.32927v1 Announce Type: new Abstract: Transformer-based language models perform well on symbolic tasks, yet it remains unclear whether they learn generalizable rules or rely on statistical s

The Hidden Perception Constraint in Task-Aware Compression

arXiv:2609.35684v1 Announce Type: cross Abstract: With the recent advancements of neural compressors, explicitly incorporating perception constraints into the design of compression schemes has gained

TRAP: Understanding and Mitigating Privacy Memorization in Language Models

arXiv:2609.32293v1 Announce Type: cross Abstract: Fine-tuning a language model on sensitive records can leave it able to reproduce them. We ask when this memorization arises and how to prevent it with

Uni-VLaT: Whole-Body Tactile Adaptation of VLA Policies for Humanoid Loco-Manipulation

arXiv:2609.35450v1 Announce Type: new Abstract: Physical contact often determines how a humanoid should respond during loco-manipulation, yet vision and proprioception alone are often insufficient to

Unifying Deep Predicate Invention with Pre-trained Foundation Models

arXiv:2512.17992v2 Announce Type: replace Abstract: Long-horizon robotic tasks are hard due to continuous state-action spaces and sparse feedback. Symbolic world models help by decomposing tasks into

UniPool: Learning Expert-to-Layer Ownership from Brief Global Access

arXiv:2605.06665v2 Announce Type: replace Abstract: Most Mixture-of-Experts (MoE) transformers preassign each expert to one layer for the whole of training. We study whether a brief global-access phas

Unlocking Few-Step Diffusion for Faithful Previews

arXiv:2609.34406v1 Announce Type: cross Abstract: Sampling latency compounds in diffusion workflows, where users generate and discard many candidates before keeping one. Surprisingly, the poor outputs

VGGT-Diff: Visual Geometry Meets Diffusion for Sparse-View Novel View Synthesis

arXiv:2609.33253v1 Announce Type: new Abstract: We present VGGT-Diff, a geometry-routed multi-view diffusion model for sparse-view novel view synthesis. Existing novel view synthesis (NVS) methods fac

VPTwin: Real-Sim-Real Video Prediction for Robotic Manipulation Planning

arXiv:2609.33104v1 Announce Type: new Abstract: While action-conditioned video prediction provides an intuitive world model for robotics, purely data-driven predictors often suffer from compounding er

W2Rep: Learning Visual Representations by Watching the World Change

arXiv:2609.35464v1 Announce Type: new Abstract: Images capture the world at one moment, whereas video reveals how it changes. Image self-supervision learns spatial structure from a single moment, whil

Weighting Schedules Govern What and When Score-Based Generative Models Learn from Multimodal Data

arXiv:2609.35322v1 Announce Type: new Abstract: Score-based generative models generate new samples by integrating a time-dependent drift that carries Gaussian noise onto the target distribution. In pr

What Should a Streaming Video Model Remember?

arXiv:2606.16353v2 Announce Type: replace Abstract: Streaming video understanding models must answer queries at any moment during an ongoing stream, using only what they have observed so far and under

When Can Attention Heads Be Statically Defined?

arXiv:2609.34650v1 Announce Type: new Abstract: Some attention heads learn similar patterns across inputs. Reusing these patterns could reduce training cost by avoiding repeated query-key score comput

When Does Depth Matter For In-Context Learning? Adaptive Inference in Deep Transformers

arXiv:2606.16694v2 Announce Type: replace Abstract: Transformers perform computations through many successive attention and feedforward blocks, allowing them to learn complex correlations between a la

XMatch: Enhancing Covariate-Aware Time Series Forecasting through Tree-Structured Exogenous Matching

arXiv:2609.34939v1 Announce Type: new Abstract: Future exogenous variables provide valuable information for forecasting endogenous time series. Existing covariate-aware methods primarily learn the dir

A Unified Account of Concepts and Chunks

arXiv:2609.30414v1 Announce Type: cross Abstract: Cognitive psychology has studied how people encode, use, and learn concepts that describe categories, and how they represent, recognize, and acquire c

tutorialsarxiv-cs-ai

Aurora-X: Built for Extreme Time Series Forecasting

arXiv:2609.31038v1 Announce Type: new Abstract: Time series foundation models (TSFMs) enable cross-domain forecasting, but their development as general-purpose forecasters remains constrained by under

Combining General and Domain-Specific Pretext Tasks for Brain MR Image Segmentation

arXiv:2609.30708v1 Announce Type: cross Abstract: A key challenge in medical image analysis is the scarcity of large annotated datasets for specific populations and diseases. As deep learning models r

tutorialsarxiv-cs-ai

Foundations of Reinforcement Learning and Interactive Decision Making

arXiv:2312.16730v2 Announce Type: replace Abstract: Interactive decision making is the problem of learning to act well in an unknown environment, using the data that one's own actions generate to cont

Geometric Feature Learning for Functional Data Valued on the Symmetric Positive Definite Manifold

arXiv:2609.30487v1 Announce Type: new Abstract: We here develop a functional neural network, termed MatFAE, for learning trajectories on the Riemannian manifold of symmetric positive definite (SPD) ma

I Act Therefore I Am: When Is JEPA's Action-Conditioning Enough to Learn Causal Mechanisms?

arXiv:2609.31161v1 Announce Type: new Abstract: Recent empirical and theoretical advances suggest that joint-embedding predictive architectures (JEPAs) may learn meaningful representations for action-

Imp-ACT: Adaptive Impedance Control and Action Chunking with Transformers to Learn Contact-Rich Manipulation from Demonstrations

arXiv:2609.31225v1 Announce Type: new Abstract: Contact-rich manipulation requires robots to balance accurate motion tracking with compliant interaction, yet most visual-action policies leave complian

Improving Molecular-Morphology Contrastive Pretraining using Deep-Learning-based Morphology Profiles

arXiv:2609.30433v1 Announce Type: new Abstract: Recent advancements in image-based profiling techniques have enabled the collection of high-volume cell morphology data, allowing new molecular embeddin

LLPR: Location-aware learning and physics-based reconstruction for raindrop removal from a single image

arXiv:2609.30758v1 Announce Type: new Abstract: Raindrops can cause occlusion and distortion in the background scenes due to their adherence to windows or camera lenses. Existing raindrop removal meth

Probabilistic Robustness-driven Universal Adversarial Perturbations with Explainability against Deep Reinforcement Learning-based Intrusion Detection System

arXiv:2609.30605v1 Announce Type: new Abstract: Deep reinforcement learning (DRL) enables adaptive intrusion detection in dynamic network environments but also exposes intrusion detection systems (IDS

Retrainable physics-integrated neural differentiable modeling of sintering across material systems

arXiv:2609.31518v1 Announce Type: cross Abstract: Sintering is widely used to manufacture ceramics, but coupled densification and grain growth, material-dependent kinetics, and sparse measurements com

SoGuDiff: Socially Guided Diffusion for Steerable, Norm-Grounded Robot Navigation

arXiv:2609.30560v1 Announce Type: new Abstract: Beyond collision avoidance, socially competent robot navigation requires adherence to implicit social conventions that vary across contexts, cultures, a

Spatial Information Bottleneck for Interpretable Visual Recognition

arXiv:2511.09239v2 Announce Type: replace Abstract: Deep neural networks typically learn spatially entangled representations that conflate discriminative foreground features with spurious background c

Stable initialization without the CLT

arXiv:2609.30633v1 Announce Type: new Abstract: Successful training of deep neural networks is highly dependent on the distribution of the initial weights. If the weights are too large, network traini

Structure-Guided Masked Autoencoders for Ultra-High Resolution Scientific Image Understanding

arXiv:2609.30682v1 Announce Type: new Abstract: Self-supervised pre-training with Vision Transformers, including Masked Autoencoders (MAE), is difficult to apply to gigapixel scientific images. Random

Why Clipping Matters in AdaGrad? Toward a High-Probability Theory under Generalized Smoothness

arXiv:2609.30276v1 Announce Type: new Abstract: We analyze the original same-step coordinate-wise AdaGrad under generalized smoothness and heavy-tailed noise with bounded variance. In this setting, lo