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arXiv:2606.28194v1 Announce Type: new Abstract: While interpretable models such as concept bottleneck models (CBMs) and program synthesis methods enable verification of model decisions, their evaluati

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Agents

Orientation Matters: Learning Radiation Patterns of Multi-Rotor UAVs In-Flight to Enhance Communication Availability Modeling

DGX agent

arXiv:2604.02827v2 Announce Type: replace Abstract: The paper presents an approach for learning antenna Radiation Patterns (RPs) of a pair of heterogeneous quadrotor Uncrewed Aerial Vehicles (UAVs) by

agentsarxiv-cs-ro

29 Jun 2026

PixelU: A U-Shaped Transformer for Efficient End-to-End Pixel Diffusion

arXiv:2606.27760v1 Announce Type: new Abstract: End-to-end pixel-space diffusion models bypass the lossy compression of Latent Diffusion Models (LDMs) but struggle to jointly model low-frequency seman

Supersede: Diagnosing and Training the Memory-Update Gap in LLM Agents

model-releasesarxiv-cs-ai

arXiv:2606.27472v1 Announce Type: cross Abstract: Large language model (LLM) agents operate over long, multi-session interactions in which facts change: a user moves, a price updates, a plan is revise

The Simulacrum: Decision-Theoretic Pretraining for Near-Optimal Time-Series Forecasting and Inference

model-releasesarxiv-cs-ai

arXiv:2606.27711v1 Announce Type: cross Abstract: We introduce a neural network-based framework for learning time series estimators through a process we term decision-theoretic pretraining. Analysts s

Unified Zero-Shot Time Series Forecasting: A Darts Foundation

arXiv:2606.27438v1 Announce Type: new Abstract: Since its initial release in 2020, Darts has become a widely used open-source Python library for time series analysis. A series of foundation models hav

WARP-RM: A Warp-Augmented Relative Progress Reward Model for Data Curation

arXiv:2606.28320v1 Announce Type: new Abstract: Scaling imitation learning requires large datasets, yet human teleoperation inevitably produces mixed-quality demonstrations containing hesitations and

safetyarxiv-cs-ro

Ask, Solve, Generate: Self-Evolving Unified Multimodal Understanding and Generation via Self-Consistency Rewards

arXiv:2606.27376v1 Announce Type: new Abstract: Most unified large multimodal models (LMMs) that support both visual understanding and image generation still rely on curated post-training supervision,

Automated reproducibility assessments in the social and behavioral sciences using large language models

arXiv:2606.13670v2 Announce Type: replace Abstract: Reproducibility in the social and behavioral sciences is typically evaluated by independent researchers who reanalyze the original data to assess wh

researcharxiv-cs-ai

Computationally-efficient Graph Modeling with Refined Graph Random Features

arXiv:2510.07716v2 Announce Type: replace Abstract: We propose refined GRFs (GRFs++), a new class of Graph Random Features (GRFs) for efficient and accurate computations involving kernels defined on t

Agents

Content-Based Smart E-Mail Dispatcher Using Large Language Models

arXiv:2606.26593v1 Announce Type: new Abstract: Email communication has become an integral part of personal and professional life, but handling its vast volume is still a significant issue for large o

agentsarxiv-cs-ai

Agents

Narration-of-Thought: Inference-Time Scaffolding for Defeasible Ethical Reasoning in Large Language Models

arXiv:2606.26366v1 Announce Type: new Abstract: Standard chain-of-thought on moral dilemmas exhibits two failure modes: stakeholder collapse (the trace names at most one party with a stake in the outc

agentsarxiv-cs-ai

Orthogonal Hierarchical Decomposition for Structure-Aware Table Understanding with Large Language Models

arXiv:2602.01969v2 Announce Type: replace Abstract: Complex tables with multi-level headers, merged cells and heterogeneous layouts pose persistent challenges for LLMs in both understanding and reason

researcharxiv-cs-cl

Ribbon: Scalable Approximation and Robust Uncertainty Quantification

arXiv:2606.27269v1 Announce Type: cross Abstract: Reliably quantifying predictive uncertainty is difficult for complex, high-dimensional, or misspecified models. Both fully Bayesian and bootstrap resa

Safe Autoregressive Image Generation with Iterative Self-Improving Codebooks

arXiv:2606.27147v1 Announce Type: cross Abstract: Unlike diffusion-based models that operate in continuous latent spaces, autoregressive unified multimodal models produce images by sequentially predic

safetyarxiv-cs-ai

A welding penetration prediction model for laser welding process based on self-supervised learning using physics-informed neural networks

arXiv:2606.26059v1 Announce Type: new Abstract: The laser welding full-penetration is of critical importance, as it constitutes one of the fundamental factors in achieving defect-free welded joints. A

Are We There Yet? Exploring the Capabilities of MLLMs in Assistive AI Applications

arXiv:2606.25084v1 Announce Type: new Abstract: Multimodal Large Language Models (MLLMs) have redefined visual understanding by combining vision encoders with large-scale language models. This unified

Applications

AutoRelAnnotator: Calibrated Model Cascades for Cost-Efficient Relevance Evaluation in Sponsored Search

arXiv:2606.25871v1 Announce Type: cross Abstract: How can we generate high-quality relevance annotations at scale without the cost and delays of human labeling? Relevance annotations are the backbone

applicationsarxiv-cs-ai

Distribution Preference Optimization: A Fine-grained Perspective for LLM Unlearning

arXiv:2510.04773v2 Announce Type: replace Abstract: As Large Language Models (LLMs) demonstrate remarkable capabilities learned from vast corpora, concerns regarding data privacy and safety are receiv

Do Thinking Tokens Help with Safety?

model-releasesarxiv-cs-cl

arXiv:2606.25013v1 Announce Type: cross Abstract: Today's reasoning models use thinking tokens to attain stronger performance on benchmarks than their instruction-tuned counterparts. It is also genera

Enhancing Brain MRI Anomaly Detection and Reasoning with ROI Rethink and Synthetic Data

arXiv:2606.25894v1 Announce Type: new Abstract: Medical vision-language models typically generate diagnoses through single-pass inference without indicating which image regions support their conclusio

Learning to Erase Private Knowledge from Multi-Documents for Retrieval-Augmented Large Language Models

arXiv:2504.09910v2 Announce Type: replace Abstract: Retrieval-Augmented Generation (RAG) is a promising technique for applying LLMs to proprietary domains. However, retrieved documents may contain sen

researcharxiv-cs-cl

Limitations of SGD for Multi-Index Models Beyond Statistical Queries

arXiv:2602.05704v2 Announce Type: replace Abstract: Understanding the limitations of gradient methods, and stochastic gradient descent (SGD) in particular, is a central challenge in learning theory. T

Logit Distance Bounds Representational Similarity

arXiv:2602.15438v3 Announce Type: replace Abstract: For a broad family of discriminative models that includes autoregressive language models, identifiability results imply that if two models induce th

Physics Question Scene Graph: Fine-grained Evaluation of Physical Plausibility in Text-to-Video Generation

arXiv:2606.25306v1 Announce Type: new Abstract: Video generation models are increasingly capable of producing realistic videos, but they still struggle to generate videos that follow basic physical la

PVF:Understanding AI Vulnerability Against SDCs

arXiv:2405.01741v4 Announce Type: replace-cross Abstract: Reliability of AI systems is a fundamental concern for the successful deployment and widespread adoption of AI technologies. Unfortunately, th

Retrieval-Augmented Personalization with Foundation Models for Wearable Stress Detection

arXiv:2606.24985v1 Announce Type: new Abstract: Personalization in wearable-based stress detection remains challenging due to substantial inter-individual variability in physiological and behavioral r

Robust Linear Predictions: Analyses of Uniform Concentration, Fast Rates and Model Misspecification

arXiv:2201.01973v3 Announce Type: replace-cross Abstract: The problem of linear predictions has been extensively studied for the past century under pretty generalized frameworks. Recent advances in th

TokenMinds: Pretrained User Tokens and Embeddings for User Understanding in Large Recommender Systems

arXiv:2606.25147v1 Announce Type: cross Abstract: User modeling in industrial recommender systems typically produces dense embeddings, which suffer from representational constraints inherent to fixed-

CrossFusion: A Multi-Scale Cross-Attention Convolutional Fusion Model for Cancer Survival Prediction

arXiv:2503.02064v2 Announce Type: replace-cross Abstract: Cancer survival prediction from whole slide images (WSIs) is a challenging task in computational pathology due to the large size, irregular sh

Does Mixture-of-Experts Actually Help Inference on Consumer and Edge Hardware? An Empirical Study

model-releasesarxiv-cs-ai

arXiv:2606.21428v2 Announce Type: replace-cross Abstract: Mixture-of-Experts (MoE) language models are often described as ideal for resource-constrained inference. Each token activates only a small su

DynaWM: Dynamics-Aware Distillation with World Model and Momentum Targets for Smooth Locomotion over Continuous Stairs

arXiv:2606.24089v1 Announce Type: cross Abstract: Recent advances in control have enabled bipedal-wheeled robots to traverse slopes and single-step obstacles, yet long staircase traversal remains chal

researcharxiv-cs-ai

LangMAP: A Language-Adaptive Approach to Tokenization

model-releasesarxiv-cs-cl

arXiv:2606.23566v2 Announce Type: replace Abstract: Language-specific tokenizers improve tokenization quality and the downstream performance of models on those languages. However, using such a tokeniz

LLM-MINE: Large Language Model based Alzheimer's Disease and Related Dementias Phenotypes Mining from Clinical Notes

arXiv:2603.13673v2 Announce Type: replace Abstract: Accurate extraction of Alzheimer's Disease and Related Dementias (ADRD) phenotypes from electronic health records (EHR) is critical for early-stage

researcharxiv-cs-ai

Mixtures Closest to a Given Measure: A Semidefinite Programming Approach

arXiv:2509.22879v2 Announce Type: replace-cross Abstract: Mixture models, such as Gaussian mixture models, are widely used in machine learning to represent complex data distributions. A key challenge,

Tutorials

A large-scale foundation model enables simulation-to-real adaptation for nuclear magnetic resonance-based molecular structure analysis

arXiv:2606.20756v1 Announce Type: cross Abstract: Nuclear Magnetic Resonance (NMR) spectroscopy is a powerful tool for molecular structure analysis, and spectral artificial intelligence offers great p

tutorialsarxiv-cs-lg

Adversarial observations in probabilistic State-Space Models for robust Reinforcement Learning

arXiv:2606.20880v1 Announce Type: cross Abstract: Decision-making under partial or adversarial observability requires accurate inference of the environment's latent state and its associated uncertaint

safetyarxiv-cs-lg

Bracing for Impact: Robust Humanoid Push Recovery and Locomotion with Reduced Order Models

arXiv:2505.11495v2 Announce Type: replace Abstract: Push recovery during locomotion will facilitate the deployment of humanoid robots in human-centered environments. In this paper, we present a unifie

researcharxiv-cs-ro

Tutorials

Decision-Focused Learning: When and Why Traditional Prediction Models Fail

arXiv:2606.21773v1 Announce Type: new Abstract: Plugging predictions of unknown parameters into downstream optimization problems, often referred to as the ``predict-then-optimize'' paradigm, has long

tutorialsarxiv-cs-lg

Local Ai

Deep EM with Hierarchical Latent Label Modelling for Multi-Site Prostate Lesion Segmentation

arXiv:2603.14418v2 Announce Type: replace Abstract: Label variability is a major challenge for prostate lesion segmentation. In multi-site datasets, annotations often reflect centre-specific contourin

local-aiarxiv-cs-cv

Iterative Diffusion-Refined Neural Attenuation Fields for Multi-Source Stationary CT Reconstruction: NAF Meets Diffusion Model

arXiv:2511.14310v2 Announce Type: replace Abstract: Multi-source stationary computed tomography (CT) has recently attracted attention for its ability to achieve rapid image reconstruction, making it s

Local Ai

Modular Diffusion Models for Structured Visual Recognition

arXiv:2606.22702v1 Announce Type: new Abstract: Traditional supervised methods for structured visual recognition tasks -- such as object detection, segmentation, and scene graph generation -- often pr

local-aiarxiv-cs-cv

Music Playlist Captioning at Scale with Large Language Models

arXiv:2606.22460v1 Announce Type: cross Abstract: Music streaming services such as Deezer often recommend personalized playlists to users. Playlist captioning, which involves describing these playlist

NNiT: Width-Agnostic Neural Network Generation with Structurally Aligned Weight Spaces

arXiv:2603.00180v2 Announce Type: replace Abstract: Generative modeling of neural network parameters is often tied to architectures because standard parameter representations rely on known weight-matr

Predicting cognitive load in immersive driving scenarios with a hybrid CNN-RNN model

arXiv:2408.06350v2 Announce Type: replace-cross Abstract: One debatable issue in traffic safety research is that cognitive load from sec-ondary tasks reduces primary task performance, such as driving.

safetyarxiv-cs-lg

Applications

Reference-Free Assessment of Physical Consistency in World Model-based Video Generation

arXiv:2606.22363v1 Announce Type: cross Abstract: We introduce reference-free measures for evaluating the physical consistency of generated videos, combining relative and absolute approaches to assess

applicationsarxiv-cs-lg

Render-FM: Feedforward Model for Real-time Photorealistic Volumetric Rendering

arXiv:2505.17338v2 Announce Type: replace Abstract: Photorealistic volumetric rendering of CT scans greatly benefits clinical workflows, yet neural approaches such as Neural Radiance Fields (NeRF) and

Rethinking Object-Centric Representations for Video Dynamics Modeling

arXiv:2606.23436v1 Announce Type: new Abstract: Unsupervised video object tracking aims to decompose dynamic scenes into persistent, object-centric entities without manual annotations. Many recent app

safetyarxiv-cs-cv