Search: “framework”

Exact Stiefel Optimization for Probabilistic PLS: Closed-Form Updates, Error Bounds, and Calibrated Uncertainty

model-releasesarxiv-cs-lg

arXiv:2605.11607v1 Announce Type: cross Abstract: Probabilistic partial least squares (PPLS) is a central likelihood-based model for two-view learning when one needs both interpretable latent factors

Content type

All Blog X Post Paper YouTube Reddit GitHub

Clear filters

Safety

Fair Conformal Classification via Learning Representation-Based Groups

DGX agent

arXiv:2605.12195v1 Announce Type: new Abstract: Conformal prediction methods provide statistically rigorous marginal coverage guarantees for machine learning models, but such guarantees fail to accoun

safetyarxiv-cs-lg

13 May 2026

ForceFlow: Learning to Feel and Act via Contact-Driven Flow Matching

arXiv:2605.11048v1 Announce Type: new Abstract: Existing imitation learning methods enable robots to interact autonomously with the physical environment. However, contact-rich manipulation tasks remai

safetyarxiv-cs-ro

Generative AI for Visualizing Highway Construction Hazards Through Synthetic Images and Temporal Sequences

arXiv:2605.11276v1 Announce Type: new Abstract: Highway construction workers face a high risk of serious injury or death. Image-based training materials depicting hazardous scenarios are essential for

safetyarxiv-cs-cv

Generative climate downscaling enables high-resolution compound risk assessment by preserving multivariate dependencies

arXiv:2605.11531v1 Announce Type: cross Abstract: Physics-based climate projections using general circulation models are essential for assessing future risks, but their coarse resolution limits region

GRAFT-ATHENA: Self-Improving Agentic Teams for Autonomous Discovery and Evolutionary Numerical Algorithms

model-releasesarxiv-cs-lg

arXiv:2605.11117v1 Announce Type: new Abstract: Scientific discovery can be modeled as a sequence of probabilistic decisions that map physical problems to numerical solutions. Recent agentic AI system

Hierarchical LLM-Driven Control for HAPS-Assisted UAV Networks: Joint Optimization of Flight and Connectivity

arXiv:2605.11509v1 Announce Type: cross Abstract: Uncrewed aerial vehicles (UAVs) are increasingly deployed in complex networked environments, yet the joint optimization of multi-UAV motion control an

local-aiarxiv-cs-lg

IGV-RRT: Prior-Real-Time Observation Fusion for Active Object Search in Changing Environments

arXiv:2603.21887v2 Announce Type: replace Abstract: Object Goal Navigation (ObjectNav) in temporally changing indoor environments is challenging because object relocation can invalidate historical sce

applicationsarxiv-cs-ro

Integral Imprecise Probability Metrics

arXiv:2505.16156v3 Announce Type: replace-cross Abstract: Quantifying differences between probability distributions is fundamental to statistics and machine learning, primarily for comparing statistic

Interactive State Space Model with Cross-Modal Local Scanning for Depth Super-Resolution

arXiv:2605.11934v1 Announce Type: new Abstract: Guided depth super-resolution (GDSR) reconstructs HR depth maps from LR inputs with HR RGB guidance. Existing methods either model each modality indepen

researcharxiv-cs-cv

Keep What Audio Cannot Say: Context-Preserving Token Pruning for Omni-LLMs

arXiv:2605.11605v1 Announce Type: new Abstract: Omnimodal Large Language Models (Omni-LLMs) incur substantial computational overhead due to the large number of multimodal input tokens they process, ma

local-aiarxiv-cs-cv

Learning U-Statistics with Active Inference

arXiv:2605.11638v1 Announce Type: cross Abstract: U-statistics play a central role in statistical inference. In many modern applications, however, acquiring the labels required for U-statistics is cos

Martingale-Consistent Self-Supervised Learning

arXiv:2605.11846v1 Announce Type: new Abstract: Self-supervised learning (SSL) is often deployed under changing information, such as shorter histories, missing features, or partially observed images.

MaskTab: Scalable Masked Tabular Pretraining with Scaling Laws and Distillation for Industrial Classification

arXiv:2605.11408v1 Announce Type: cross Abstract: Tabular data forms the backbone of high-stakes decision systems in finance, healthcare, and beyond. Yet industrial tabular datasets are inherently dif

applicationsarxiv-cs-cl

Measuring Five-Nines Reliability: Sample-Efficient LLM Evaluation in Saturated Benchmarks

model-releasesarxiv-cs-lg

arXiv:2605.11209v1 Announce Type: new Abstract: While existing benchmarks demonstrate the near-perfect performance of large language models (LLMs) on various tasks, this apparent saturation often obsc

Agents

Multi-Variable Conformal Prediction: Optimizing Prediction Sets without Data Splitting

arXiv:2605.12341v1 Announce Type: cross Abstract: Conformal prediction constructs prediction sets with finite-sample coverage guarantees, but its calibration stage is structurally constrained to a sca

agentsarxiv-cs-lg

NexOP: Joint Optimization of NEX-Aware k-space Sampling and Image Reconstruction for Low-Field MRI

arXiv:2605.11583v1 Announce Type: cross Abstract: Modern low-field magnetic resonance imaging (MRI) technology offers a compelling alternative to standard high-field MRI, with portable, low-cost syste

applicationsarxiv-cs-cv

On the Importance of Multistability for Horizon Generalization in Reinforcement Learning

arXiv:2605.12206v1 Announce Type: new Abstract: In reinforcement learning (RL), agents acting in partially observable Markov decision processes (POMDPs) must rely on memory, typically encoded in a rec

Parameter-Efficient Adaptation of Pre-Trained Vision Foundation Models for Active and Passive Seismic Data Denoising

arXiv:2605.10953v1 Announce Type: cross Abstract: The demand for high-resolution subsurface imaging and continuous Earth monitoring has driven rapid growth in active and passive seismic data from dens

PG-3DGS: Optimizing 3D Gaussian Splatting to Satisfy Physics Objectives

arXiv:2605.11266v1 Announce Type: new Abstract: Recent advances in Gaussian Splatting have enabled fast, high-fidelity 3D scene generation, yet these methods remain purely visual and lack an understan

tutorialsarxiv-cs-cv

Physics-Informed Teacher-Student Ensemble Learning for Traffic State Estimation with a Varying Speed Limit Scenario

arXiv:2605.11346v1 Announce Type: new Abstract: Physics-informed deep learning (PIDL) neural networks have shown their capability as a useful instrument for transportation practitioners in utilizing t

local-aiarxiv-cs-lg

Agents

PIVOT: Bridging Planning and Execution in LLM Agents via Trajectory Refinement

arXiv:2605.11225v1 Announce Type: cross Abstract: Large language model (LLM)-based agents frequently generate seemingly coherent plans that fail upon execution due to infeasible actions, constraint vi

agentsarxiv-cs-lg

Post-ADC Inference: Valid Inference After Active Data Collection

arXiv:2605.11511v1 Announce Type: cross Abstract: The validity of statistical inference depends critically on how data are collected. When data gathered through active data collection (ADC) are reused

PresentAgent-2: Towards Generalist Multimodal Presentation Agents

model-releasesarxiv-cs-cl

arXiv:2605.11363v1 Announce Type: cross Abstract: Presentation generation is moving beyond static slide creation toward end-to-end presentation video generation with research grounding, multimodal med

Principled Design of Diffusion-based Optimizers for Inverse Problems

arXiv:2605.11506v1 Announce Type: new Abstract: Score-based diffusion models achieve state-of-the-art performance for inverse problems, but their practical deployment is hindered by long inference tim

researcharxiv-cs-cv

PriorZero: Bridging Language Priors and World Models for Decision Making

arXiv:2605.12289v1 Announce Type: new Abstract: Leveraging the rich world knowledge of Large Language Models (LLMs) to enhance Reinforcement Learning (RL) agents offers a promising path toward general

Probabilistic Modeling of Latent Agentic Substructures in Deep Neural Networks

arXiv:2509.06701v2 Announce Type: replace Abstract: We develop a theory of intelligent agency grounded in probabilistic modeling for neural models. Agents are represented as outcome distributions with

Ray-Aware Pointer Memory with Adaptive Updates for Streaming 3D Reconstruction

arXiv:2605.05749v2 Announce Type: replace Abstract: Dense 3D reconstruction from continuous image streams requires both accurate geometric aggregation and stable long-term memory management. Recent fe

local-aiarxiv-cs-cv

Real-Scale Island Area and Coastline Estimation using Only its Place Name or Coordinates

arXiv:2605.11267v1 Announce Type: new Abstract: Accurate measurement of island area and coastline length is crucial for coastal zone monitoring and oceanographic analysis. However, traditional measure

safetyarxiv-cs-cv

Reflect then Learn: Active Prompting for Information Extraction Guided by Introspective Confusion

arXiv:2508.10036v2 Announce Type: replace Abstract: Large Language Models (LLMs) show remarkable potential for few-shot information extraction (IE), yet their performance is highly sensitive to the ch

tutorialsarxiv-cs-cl

Revisiting Shadow Detection from a Vision-Language Perspective

arXiv:2605.11771v1 Announce Type: new Abstract: Shadow detection is commonly formulated as a vision-driven dense prediction problem, where models rely primarily on pixel-wise visual supervision to dis

Sampling More, Getting Less: Calibration is the Diversity Bottleneck in LLMs

arXiv:2605.11128v1 Announce Type: new Abstract: Diversity is essential for language-model applications ranging from creative generation to scientific discovery, yet modern LLMs often collapse into a n

local-aiarxiv-cs-cl

Agents

SB-BEVFusion: Enhancing the Robustness against Sensor Malfunction and Corruptions

arXiv:2605.11799v1 Announce Type: new Abstract: Multimodal sensor fusion has demonstrated remarkable performance improvements over unimodal approaches in 3D object detection for autonomous vehicles. T

agentsarxiv-cs-cv

SEMIR: Semantic Minor-Induced Representation Learning on Graphs for Visual Segmentation

arXiv:2605.12389v1 Announce Type: new Abstract: Segmenting small and sparse structures in large-scale images is fundamentally constrained by voxel-level, lattice-bound computation and extreme class im

Strategically Deceptive Model Deployment in Performative Prediction

arXiv:2506.09044v2 Announce Type: replace Abstract: Machine Learning systems are increasingly deployed in decision-making settings that shape user behavior and, in turn, the data on which future decis

tutorialsarxiv-cs-lg

Structural Interpretations of Protein Language Model Representations via Differentiable Graph Partitioning

arXiv:2605.10985v1 Announce Type: new Abstract: Protein language models such as ESM-2 learn rich residue representations that achieve strong performance on protein function prediction, but their featu

local-aiarxiv-cs-lg

Tackling Fake Forgetting through Uncertainty Quantification

arXiv:2501.19403v3 Announce Type: replace Abstract: Machine unlearning seeks to remove the influence of specified data from a trained model. While the unlearning accuracy provides a widely used metric

The Challenge and Reward of Fair Play in Narrative: A Computational Approach

arXiv:2507.13841v2 Announce Type: replace Abstract: Good storytelling involves surprise -- unpredictability in how the story unfolds -- and sense-making, the requirement that the story forms a coheren

researcharxiv-cs-cl

Hardware

To Err Is Human; To Annotate, SILICON? Toward Robust Reproducibility in LLM Annotation

arXiv:2412.14461v4 Announce Type: replace Abstract: Unstructured text data annotation is foundational to management research. LLMs offer a cost-effective and scalable alternative to human annotation,

hardwarearxiv-cs-cl

UnfoldLDM: Degradation-Aware Unfolding with Iterative Latent Diffusion Priors for Blind Image Restoration

arXiv:2511.18152v3 Announce Type: replace Abstract: Deep unfolding networks (DUNs) combine the interpretability of model-based methods with the learning ability of deep networks, yet remain limited fo

Vector Scaffolding: Inter-Scale Orchestration for Differentiable Image Vectorization

arXiv:2605.11913v1 Announce Type: new Abstract: Differentiable vector graphics have enabled powerful gradient-based optimization of vector primitives directly from raster images. However, existing fra

researcharxiv-cs-cv

When and How to Canonize: A Generalization Perspective

arXiv:2605.11008v1 Announce Type: new Abstract: While invariant architectures are standard for processing symmetric data, there is growing interest in achieving invariance by applying group averaging

tutorialsarxiv-cs-lg

Why Conclusions Diverge from the Same Observations: Formalizing World-Model Non-Identifiability via an Inference

arXiv:2605.12255v1 Announce Type: cross Abstract: When people share the same documents and observations yet reach different conclusions, the disagreement often shifts into a judgment that the other pa

applicationsarxiv-cs-lg

A cell-decomposition based path planner for 3D navigation in constrained workspaces

arXiv:2605.10086v1 Announce Type: new Abstract: This paper proposes a cell decomposition algorithm for binary occupancy grids that ensures mutual complete visibility from each cell to at least one adj

researcharxiv-cs-ro

A Cold Diffusion Approach for Percussive Dereverberation

arXiv:2605.10256v1 Announce Type: cross Abstract: Most recent advances in audio dereverberation focus almost exclusively on speech, leaving percussive and drum signals largely unexplored despite their

applicationsarxiv-cs-ai

A Market-Rule-Informed Neural Network for Efficient Imbalance Electricity Price Forecasting

arXiv:2605.09061v1 Announce Type: cross Abstract: Accurate and efficient imbalance electricity price forecasting is critical for industrial energy trading systems, especially as battery assets and aut

A Real-Calibrated Synthetic-First Data Engine

arXiv:2605.09699v1 Announce Type: cross Abstract: Modern computer vision systems increasingly encounter performance limitations in data-scarce domains, where collecting large-scale, high-quality label

applicationsarxiv-cs-cv

A Simulated Federated Analysis of MS-Induced Brain Lesions

arXiv:2605.08223v1 Announce Type: new Abstract: Federated techniques such as federated learning and federated analysis have emerged as a powerful paradigm for enabling multi-center research on sensiti

applicationsarxiv-cs-lg