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OLIVE: View-Augmented Latent Prediction with Waveform Reconstruction for Speech SSL

arXiv:2606.30356v1 Announce Type: new Abstract: We propose Online Latent prediction with Invariant Views and rEconstruction (OLIVE), a self-supervised speech representation learning framework that joi

researcharxiv-cs-cl

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OmniCoT: A Benchmark for Global and Multi-Step Panoramic Reasoning

DGX agent

arXiv:2606.30378v1 Announce Type: new Abstract: Multimodal Large Language Models (MLLMs) have demonstrated promising spatial reasoning capabilities, while these abilities remain underexplored in the e

model-releasesarxiv-cs-cv

30 Jun 2026

OmniDance: Multimodal Driven Dance Video Generation with Large-scale Internet Data

arXiv:2606.30019v1 Announce Type: new Abstract: Music-driven dance video generation aims to synthesize expressive human motion that is temporally aligned with music while maintaining high visual fidel

On design-unbiased algorithmic Machine Learning

arXiv:2606.28795v1 Announce Type: new Abstract: Machine Learning (ML) algorithms, such as k-Nearest Neighbours (kNN) or random forest, eschew the ideal of true data models in favour of predictive perf

On Local Population-Risk Certificates

arXiv:2606.19147v2 Announce Type: replace-cross Abstract: We develop finite-sample certificates for local population-risk increments (Pelta_v=R(heta_0+v)-R(heta_0)), (vinmathcal D). The primitive obje

On Surrogate Modeling of Static Response of AM Short-Fiber Thermoplastics Using Graph Neural Networks

arXiv:2606.28996v1 Announce Type: new Abstract: Short-fiber thermoplastic (SFT) composites are increasingly employed in lightweight aerospace and automotive structures owing to their favorable strengt

applicationsarxiv-cs-lg

On Test-Time Scaling for Vision-Language Models

arXiv:2606.28864v1 Announce Type: new Abstract: Test-time scaling is a paradigm where large models use additional compute at inference to achieve better performance, without changing model weights. Wh

On the Faithfulness of Post-Hoc Concept Bottleneck Models

arXiv:2606.30498v1 Announce Type: cross Abstract: Human decision-making interprets the world through high-level concepts, such as recognizing a bird by its belly color. To bridge the gap between opaqu

applicationsarxiv-cs-ai

On the Identifiability of Aided Inertial Navigation Under Measurement Delays: A Geometric Approach

arXiv:2606.29123v1 Announce Type: new Abstract: In aided inertial navigation, measurements from different sensors are often subject to unknown relative time delays. Consider a single aiding sensor who

researcharxiv-cs-ro

Agents

On the Necessity of a Liquid Substrate for Mesh Intelligence

arXiv:2606.28413v1 Announce Type: cross Abstract: A mesh of sovereign agents has no center: no shared clock, no shared model, and no coordinator to gather data or retrain. Its competence rests on each

agentsarxiv-cs-ai

On the Nonlinearity of Learning Rate Scaling for LLM Training

arXiv:2606.29158v1 Announce Type: cross Abstract: Learning-rate transfer can reduce the cost of training large language models: instead of sweeping learning rates at target scale, practitioners extrap

On the Policy Gradient Foundations of Group Relative Policy Optimization: Credit Assignment, Gradient Sparsity, and Rank Collapse

model-releasesarxiv-cs-lg

arXiv:2606.29238v1 Announce Type: new Abstract: Group Relative Policy Optimization (GRPO) eliminates the learned critic in PPO by using the mean reward of grouped rollouts as a baseline. We provide a

On the Vulnerability of Parameter-Level Defenses to Model Merging

model-releasesarxiv-cs-cv

arXiv:2606.30360v1 Announce Type: cross Abstract: The training-free integration of expert models via model merging has exposed significant security risks, enabling free-riders to combine specialized m

Local Ai

One Forward Beats Two: InnerZoom for Accurate and Efficient GUI Grounding

arXiv:2606.30084v1 Announce Type: new Abstract: MLLM-based GUI grounding methods commonly formulate target localization as autoregressive coordinate generation, enabling models to leverage the strong

local-aiarxiv-cs-cv

One Scene, Two Depths: Probing Geometric Ambiguity in Monocular Foundation Models

arXiv:2606.29600v1 Announce Type: cross Abstract: A faithful 3D world representation should account for layered geometry, where a single camera ray may contain multiple visible and geometrically valid

One-Step Gradient Delay is Not a Barrier for Large-Scale Asynchronous Pipeline Parallel LLM Pretraining

arXiv:2606.30634v1 Announce Type: new Abstract: Modern large-scale LLM pretraining benefits from utilizing Pipeline Parallelism; however, synchronous implementations leave GPUs idle during pipeline bu

Local Ai

Online Data Selection for Instruction Tuning via Gaussian Processes

arXiv:2606.30077v1 Announce Type: cross Abstract: With Large Language Model (LLM) pre-training and fine-tuning shifting its focus from data volume to data quality, quality data selection has emerged a

local-aiarxiv-cs-ai

Online Experiential Learning for Language Models

arXiv:2603.16856v2 Announce Type: replace Abstract: The prevailing paradigm for improving large language models relies on offline training with human annotations or simulated environments, leaving the

safetyarxiv-cs-cl

Tutorials

Ontology-Guided Reverse Thinking Makes Large Language Models Stronger on Knowledge Graph Question Answering

arXiv:2502.11491v3 Announce Type: replace-cross Abstract: Large language models (LLMs) have shown remarkable capabilities in natural language processing. However, in knowledge graph question answering

tutorialsarxiv-cs-ai

OP3DSG: Open-Vocabulary Part-Aware 3D Scene Graph Generation for Real-World Environments

model-releasesarxiv-cs-cv

arXiv:2606.29786v1 Announce Type: new Abstract: 3D scene graphs (3DSGs) provide a compact and structured abstraction of 3D environments. Although advances in foundation models have enabled open-vocabu

Open but Incompatible: A License Compatibility Analysis of Corpora for Low-Resource African Languages

arXiv:2606.28867v1 Announce Type: new Abstract: Creative Commons licenses dominate African NLP corpus releases, but their compatibility rules are rarely applied. CC-BY-SA and CC-BY-NC cannot be combin

applicationsarxiv-cs-cl

Open Problems in Constitutional Preference Reconstruction

arXiv:2606.30116v1 Announce Type: new Abstract: Pairwise preference data is widely used for training and evaluating language models (e.g., RLHF), but each datapoint records a choice, not the rationale

Tutorials

Open-Vocabulary and Referring Segmentation for 3D Gaussians Using 2D Detectors

arXiv:2606.30638v1 Announce Type: new Abstract: 3D Gaussian Splatting (3DGS) has emerged at the forefront of 3D scene reconstruction. Extending 3DGS with language-driven, open-vocabulary understanding

tutorialsarxiv-cs-cv

OpenSPM: An Environment-Transferable Robotic Key Spatial Pose Memory and Closed-Loop High-Frequency Flow-Matching Action Generation Model

arXiv:2606.29936v1 Announce Type: new Abstract: Open-environment tabletop robotic manipulation requires systems to possess semantic understanding, precise geometric pose estimation, and high-frequency

researcharxiv-cs-ro

Operating Regimes of Decentralized Learning Under Mobility and Bandwidth Constraints

arXiv:2606.28342v1 Announce Type: cross Abstract: Decentralized learning is a promising paradigm for collaborative training in mobile and pervasive systems, as it avoids a central coordinator and does

Optimization Dynamics Imprint Semantic Specificity in Contrastive Embedding Norms

arXiv:2606.30625v1 Announce Type: cross Abstract: Contrastive embedding models trained with scale-invariant losses are typically paired with distance metrics like cosine similarity, effectively ignori

Optimizer Memory Makes Shuffle Order a First-Order Source of Fine-Tuning Noise

arXiv:2606.29554v1 Announce Type: new Abstract: Shuffle order can be a larger source of fine-tuning noise than a memoryless analysis predicts: fixed-clock optimizer memory makes local equal-multiset c

Optimizing Expert-Designed Crystal Graph Networks for Band-Gap Prediction with an Autonomous LLM Research Loop

arXiv:2606.29717v1 Announce Type: cross Abstract: Predicting a material's properties from its structure is a central, fast-advancing problem in computational materials science. A decade of work has pr

Optimizing Image Preparation and Compression for Face Recognition within 1024 Bytes

arXiv:2606.30321v1 Announce Type: new Abstract: ICAO-compliant machine readable travel documents enable automated biometric face verification. The biometric reference is stored on an RFID chip include

Optimizing Nursing Care Taxi Dispatch Leveraging Integer Linear Programming Solvers and Machine Learning

arXiv:2606.29725v1 Announce Type: new Abstract: In this paper, we formulate a new vehicle dispatch optimization problem, called Nursing Care Taxi Dispatch, as a variant of the Vehicle Routing Problem,

applicationsarxiv-cs-lg

OptiMUS-0.3: Using Large Language Models to Model and Solve Optimization Problems at Scale

arXiv:2407.19633v4 Announce Type: replace Abstract: Optimization problems are pervasive in sectors from manufacturing and distribution to healthcare. However, most such problems are still solved heuri

ORCA: Open-ended Response Correctness Assessment for Audio Question Answering

arXiv:2512.09066v2 Announce Type: replace-cross Abstract: Reliable assessment of the abilities of large audio language models (LALMs) is essential to advancing the state of the art. As benchmarks rapi

Orca: The World is in Your Mind

arXiv:2606.30534v1 Announce Type: new Abstract: We introduce Orca, an initial instantiation of a general world foundation model. Orca learns a unified world latent space from multimodal world signals

OSWorld2.0: Benchmarking Computer Use Agents on Long-Horizon Real-World Tasks

arXiv:2606.29537v1 Announce Type: new Abstract: Existing computer-use benchmarks fail to capture the realism, complexity, and long-horizon demands of real-world computer use, limiting their ability to

Overcoming Dependent Censoring in the Evaluation of Survival Models

arXiv:2502.19460v4 Announce Type: replace-cross Abstract: Dependent censoring occurs when the event time and censoring time are not conditionally independent given the observed covariates. This compli

OWMDrive: Causality-Aware End-to-End Autonomous Driving via 4D Occupancy World Model

arXiv:2606.30421v1 Announce Type: new Abstract: Autonomous driving systems are steadily moving toward end-to-end paradigms to mitigate the limited adaptability of rule-based pipelines in complex traff

safetyarxiv-cs-cv

Pairwise Comparisons without Stochastic Transitivity: Model, Theory and Applications

arXiv:2501.07437v3 Announce Type: replace-cross Abstract: Most statistical models for pairwise comparisons, including the Bradley-Terry (BT) and Thurstone models and many extensions, make a relatively

applicationsarxiv-cs-lg

Parametric Skills

model-releasesarxiv-cs-cl

arXiv:2606.30015v1 Announce Type: new Abstract: Since intelligence fundamentally relies on efficient skill acquisition (Chollet, 2019), the ability to leverage skills is critical. For LLMs, skills, ma

PASTA: A Paraphrasing And Self-Training Approach for Knowledge Updating in LLMs

arXiv:2606.28898v1 Announce Type: new Abstract: Knowledge updating in pre-trained Large Language Models (LLMs) remains an important challenge. While continual training provides a potential avenue for

researcharxiv-cs-cl

PCGD: Physics-Guided Conditional Graph Diffusion for TCAD Device Simulation

model-releasesarxiv-cs-lg

arXiv:2606.29272v1 Announce Type: new Abstract: Technology computer-aided design (TCAD) semiconductor device simulation is fundamentally constrained by the high computational cost of iteratively solvi

PCP-GAN: Property-Constrained Pore-scale image reconstruction via conditional Generative Adversarial Networks

arXiv:2510.19465v2 Announce Type: replace Abstract: Obtaining truly representative pore-scale images that match bulk formation properties remains a fundamental challenge in subsurface characterization

Persona-Trained Monte Carlo: Estimating Market-Outcome Distributions via Swarms of Persona-Conditioned Neural Policy Bots in a Limit Order Book

arXiv:2606.29556v1 Announce Type: new Abstract: We propose Persona-Trained Monte Carlo (PTMC), a method for estimating distributions of market-outcome statistics by repeatedly simulating limit-order-b

safetyarxiv-cs-lg

Personalized Additive Modeling for Multi-level Federated Learning

arXiv:2405.16472v2 Announce Type: replace Abstract: Contemporary AI faces the challenge of balancing generality with user-specific personalization. In federated learning (FL), this challenge is amplif

Personalizing MLLMs via Reinforced Multimodal Reference Game

arXiv:2606.28845v1 Announce Type: new Abstract: Personalizing Multimodal Large Language Models (MLLMs) aims to recognize users' unique concepts from visual data and provide personalized responses. Alt

Perspectives on Latent Factor Indeterminacy and its Implications for Data Representation

arXiv:2606.28854v1 Announce Type: cross Abstract: The common factor analytic model is related to Helmholtz and Boltzmann machines, can be conceived as a linear autoencoder, or can be thought of as a s

Pessimism's Paradox: Conservative Offline Training Amplifies Reward Hacking During Online Adaptation in Reasoning Models

arXiv:2606.30627v1 Announce Type: cross Abstract: Conservative offline training is widely advocated as a safe foundation for subsequent online adaptation: if a policy stays close to well-supported beh

safetyarxiv-cs-ai

PGE-SAM: Prompt-Guided Feature Enhancement for Interactive Segmentation under Degradation

model-releasesarxiv-cs-cv

arXiv:2606.30477v1 Announce Type: new Abstract: Segment Anything Model (SAM) has revolutionized promptable image segmentation with strong zero-shot generalization. However, its performance degrades su

PHF: Privileged Hidden Flow for On-Policy Self-Distillation

arXiv:2606.29340v1 Announce Type: new Abstract: On-policy self-distillation (OPSD) trains a reasoning model on rollouts sampled from its own policy by matching a privileged teacher that also sees veri

safetyarxiv-cs-ai