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Correcting Influence: Unboxing LLM Outputs with Orthogonal Latent Spaces

arXiv:2605.12809v1 Announce Type: cross Abstract: A critical step for reliable large language models (LLMs) use in healthcare is to attribute predictions to their training data, akin to a medical case

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Tutorials

Deep Learning as Neural Low-Degree Filtering: A Spectral Theory of Hierarchical Feature Learning

DGX agent

arXiv:2605.13612v1 Announce Type: new Abstract: Understanding how deep neural networks learn useful internal representations from data remains a central open problem in the theory of deep learning. We

tutorialsarxiv-cs-lg

14 May 2026

Differentiable Learning of Lifted Action Schemas for Classical Planning

arXiv:2605.13282v1 Announce Type: new Abstract: Classical planners can effectively solve very large deterministic MDPs represented in STRIPS or PDDL where states are sets of atoms over objects and rel

DiffST: Spatiotemporal-Aware Diffusion for Real-World Space-Time Video Super-Resolution

arXiv:2605.13182v1 Announce Type: new Abstract: Diffusion-based models have shown strong performance in video super-resolution (VSR) and video frame interpolation (VFI). However, their role in the cou

DiscoverLLM: From Executing Intents to Discovering Them

arXiv:2602.03429v2 Announce Type: replace Abstract: To handle ambiguous and open-ended requests, Large Language Models (LLMs) are increasingly trained to interact with users to surface intents they ha

Distribution Shift in Missing Data Imputation: A Risk-Based Perspective and Importance-Weighted Correction under MAR

arXiv:2602.06713v2 Announce Type: replace-cross Abstract: Missing data imputation, where a model is trained on observed data to estimate unobserved values, is a fundamental problem in machine learning

Dynamical Predictive Modelling of Cardiovascular Disease Progression Post-Myocardial Infarction via ECG-Trained Artificial Intelligence Model

arXiv:2605.13568v1 Announce Type: cross Abstract: Myocardial infarction (MI) is a leading cause of death, and its adverse outcomes are urgent to predict. Yet ECG-based prognostic models underperform b

Efficient distributional regression trees learning algorithms for calibrated non-parametric probabilistic forecasts

arXiv:2502.05157v3 Announce Type: replace Abstract: The perspective of developing trustworthy AI for critical applications in science and engineering requires machine learning techniques that are capa

EvoGround: Self-Evolving Video Agents for Video Temporal Grounding

arXiv:2605.13803v1 Announce Type: new Abstract: Video temporal grounding (VTG) takes an untrimmed video and a natural-language query as input and localizes the temporal moment that best matches the qu

GraphThinker: Reinforcing Temporally Grounded Video Reasoning with Event Graph Thinking

arXiv:2602.17555v3 Announce Type: replace Abstract: Video reasoning requires a fine-grained understanding of the temporal dependencies and event-level relations between objects and events in videos. C

How Do Transformers Learn to Associate Tokens: Gradient Leading Terms Bring Mechanistic Interpretability

arXiv:2601.19208v2 Announce Type: replace-cross Abstract: Semantic associations such as the link between 'bird' and 'flew' are foundational for language modeling as they enable models to go beyond mem

Inference-Time Dynamic Modality Selection for Incomplete Multimodal Classification

arXiv:2601.22853v3 Announce Type: replace Abstract: Multimodal deep learning (MDL) has achieved remarkable success across various domains, yet its practical deployment is often hindered by incomplete

Inline Critic Steers Image Editing

arXiv:2605.12724v1 Announce Type: cross Abstract: Instruction-based image editing exhibits heterogeneous difficulty not only across cases but also across regions of an image, motivating refinement app

Learning Local Constraints for Reinforcement-Learned Content Generators

arXiv:2605.13570v1 Announce Type: new Abstract: Constraint-based game content generators that learn local constraints from existing content, such as Wave Function Collapse (WFC), can generate visually

Min Generalized Sliced Gromov Wasserstein: A Scalable Path to Gromov Wasserstein

arXiv:2605.13753v1 Announce Type: cross Abstract: We propose min Generalized Sliced Gromov--Wasserstein (min-GSGW), a sliced formulation for the Gromov--Wasserstein (GW) problem using expressive gener

Modeling Heterophily in Multiplex Graphs: An Adaptive Approach for Node Classification

arXiv:2605.12699v1 Announce Type: cross Abstract: Existing multiplex graph models often assume homophily, where connected nodes tend to belong to the same class or share similar attributes. Consequent

Multi-Modal Guided Multi-Source Domain Adaptation for Object Detection

arXiv:2605.13140v1 Announce Type: new Abstract: General object detection (OD) struggles to detect objects in the target domain that differ from the training distribution. To address this, recent studi

Multimodal Graph-based Classification of Esophageal Motility Disorders

arXiv:2605.13623v1 Announce Type: new Abstract: Diagnosing esophageal motility disorders pose significant challenges due to the complexity of high-resolution impedance manometry (HRIM) data and variab

OceanCBM: A Concept Bottleneck Model for Mechanistic Interpretability in Ocean Forecasting

arXiv:2605.12639v1 Announce Type: new Abstract: Extreme ocean phenomena are challenging not only to predict but to diagnose, as accurate forecasts alone do not reveal the underlying physical drivers.

Optimization, Generalization and Differential Privacy Bounds for Gradient Descent on Kolmogorov-Arnold Networks

arXiv:2601.22409v3 Announce Type: replace-cross Abstract: Kolmogorov--Arnold Networks (KANs) have recently emerged as a structured alternative to standard MLPs, yet a principled theory for their train

Probabilistic Prediction Markets with Intermittent Contributions

arXiv:2510.13385v3 Announce Type: replace Abstract: Although both data availability and the demand for accurate forecasts are increasing, collaboration between stakeholders is often constrained by dat

Protein Circuit Tracing via Cross-layer Transcoders

arXiv:2602.12026v2 Announce Type: replace Abstract: Protein language models (pLMs) have emerged as powerful predictors of protein structure and function. However, the computational circuits underlying

Retrieval-Augmented Tutoring for Algorithm Tracing and Problem-Solving in AI Education

arXiv:2605.12988v1 Announce Type: new Abstract: Students learning algorithms often need support as they interpret traces, debug reasoning errors, and apply procedures across unfamiliar problem instanc

Retrieval is Cheap, Show Me the Code: Executable Multi-Hop Reasoning for Retrieval-Augmented Generation

arXiv:2605.12975v1 Announce Type: new Abstract: Retrieval-Augmented Generation (RAG) has become a standard approach for knowledge-intensive question answering, but existing systems remain brittle on m

Revisit What You See: Revealing Visual Semantics in Vision Tokens to Guide LVLM Decoding

arXiv:2506.09522v3 Announce Type: replace-cross Abstract: Large Vision Language Models (LVLMs) achieve strong performance across multimodal tasks by integrating visual perception with language underst

Scaling few-shot spoken word classification with generative meta-continual learning

arXiv:2605.13075v1 Announce Type: cross Abstract: Few-shot spoken word classification has largely been developed for applications where a small number of classes is considered, and so the potential of

Shortcut Mitigation via Spurious-Positive Samples

arXiv:2605.13340v1 Announce Type: new Abstract: Shortcut mitigation strategies commonly rely on training data annotations, group-balanced held-out data or the presence of all groups, i.e., all combina

Sparse Code Uplifting for Efficient 3D Language Gaussian Splatting

arXiv:2605.13600v1 Announce Type: new Abstract: 3D Language Gaussian Splatting (3DLGS) augments 3D Gaussian Splatting with language-aligned visual features for open-vocabulary 3D scene understanding.

The WidthWall: A Strict Expressivity Hierarchy for Hypergraph Neural Networks

arXiv:2605.13690v1 Announce Type: cross Abstract: Hypergraphs provide a natural framework to model higher-order interactions in scientific, social, and biological systems. Hypergraph neural networks (

Twincher: Bijective Representation Learning for Robust Inversion of Continuous Systems

arXiv:2605.13470v1 Announce Type: new Abstract: Recent advances in AI have been primarily driven by large-scale neural architectures that excel at function approximation, rather than by tailored induc

Uncovering Latent Pathological Signatures in Pulmonary CT via Cross-Window Knowledge Distillation

arXiv:2605.12562v1 Announce Type: cross Abstract: Multi-window CT imaging captures complementary pathological information across anatomical structures of differing densities, yet existing deep learnin

Unify Robot Actions in Camera Frame

arXiv:2511.17001v2 Announce Type: replace Abstract: Cross-embodiment robot learning requires a unified action representation with consistent semantics across robot platforms. Existing representations

tutorialsarxiv-cs-ro

VIPO: Value Function Inconsistency Penalized Offline Reinforcement Learning

arXiv:2504.11944v3 Announce Type: replace-cross Abstract: Offline reinforcement learning (RL) learns effective policies from pre-collected datasets, offering a practical solution for applications wher

Visual Accommodation: Rethinking Image Scale as a Learnable Variable for Object Detection

arXiv:2412.06341v2 Announce Type: replace-cross Abstract: We propose Ciliary-DETR (previous name: Elastic-DETR), a framework for test-time resolution adjustment analogous to biological accommodation.

When to Transfer: Adaptive Source Selection for Positive Transfer in Linear Models

arXiv:2510.16986v2 Announce Type: replace-cross Abstract: In many business settings, task-specific labeled data are scarce or costly to obtain, limiting supervised learning on a target task. A classic

Why is 'Chicago' Predictive of Deceptive Reviews? Using LLMs to Discover Language Phenomena from Lexical Cues

arXiv:2511.13658v2 Announce Type: replace-cross Abstract: Deceptive reviews mislead consumers, harm businesses, and undermine trust in online marketplaces. Machine learning classifiers can learn from

A Transfer Learning Evaluation of Deep Neural Networks for Image Classification

arXiv:2605.11989v1 Announce Type: new Abstract: Transfer learning is a machine learning technique that uses previously acquired knowledge from a source domain to enhance learning in a target domain by

CausalCine: Real-Time Autoregressive Generation for Multi-Shot Video Narratives

arXiv:2605.12496v1 Announce Type: new Abstract: Autoregressive video generation aims at real-time, open-ended synthesis. Yet, cinematic storytelling is not merely the endless extension of a single sce

DenseTRF: Texture-Aware Unsupervised Representation Adaptation for Surgical Scene Dense Prediction

arXiv:2605.11265v1 Announce Type: new Abstract: Dense prediction tasks in surgical computer vision, such as segmentation and surgical zone prediction, can provide valuable guidance for laparoscopic an

FERMI: Exploiting Relations for Membership Inference Against Tabular Diffusion Models

arXiv:2605.11527v1 Announce Type: new Abstract: Diffusion models are the leading approach for tabular data synthesis and are increasingly used to share sensitive records. Whether they actually protect

From Observations to States: Latent Time Series Forecasting

arXiv:2602.00297v2 Announce Type: replace Abstract: Deep learning has achieved strong performance in Time Series Forecasting (TSF). However, we identify a critical representation paradox, termed Laten

From Per-Image Low-Rank to Encoding Mismatch: Rethinking Feature Distillation in Vision Transformers

arXiv:2511.15572v2 Announce Type: replace Abstract: Feature-map knowledge distillation (KD) transfers internal representations well between comparably sized Vision Transformers (ViTs), but it often fa

GuidedVLA: Specifying Task-Relevant Factors via Plug-and-Play Action Attention Specialization

arXiv:2605.12369v1 Announce Type: new Abstract: Vision-Language-Action (VLA) models aim for general robot learning by aligning action as a modality within powerful Vision-Language Models (VLMs). Exist

tutorialsarxiv-cs-ro

L2P: Unlocking Latent Potential for Pixel Generation

arXiv:2605.12013v1 Announce Type: new Abstract: Pixel diffusion models have recently regained attention for visual generation. However, training advanced pixel-space models from scratch demands prohib

Learning density ratios in causal inference using Bregman-Riesz regression

arXiv:2510.16127v2 Announce Type: replace-cross Abstract: The ratio of two probability density functions is a fundamental quantity that appears in many areas of statistics and machine learning, includ

Learning Feature Encoder with Synthetic Anomalies for Weakly Supervised Graph Anomaly Detection

arXiv:2605.11749v1 Announce Type: new Abstract: Weakly supervised graph anomaly detection aims to unveil unusual graph instances, e.g., nodes, whose behaviors significantly differ from normal ones, gi

Learning What Can Be Picked: Active Reachability Estimation for Efficient Robotic Fruit Harvesting

arXiv:2603.23679v2 Announce Type: replace Abstract: Agriculture remains a cornerstone of global health and economic sustainability, yet labor-intensive tasks such as harvesting high-value crops contin

tutorialsarxiv-cs-ro

Mind the Pause: Disfluency-Aware Objective Tuning for Multilingual Speech Correction with LLMs

arXiv:2605.12242v1 Announce Type: new Abstract: Automatic Speech Recognition (ASR) transcripts often contain disfluencies, such as fillers, repetitions, and false starts, which reduce readability and

tutorialsarxiv-cs-cl