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Optimal Rates for Pure {arepsilon}-Differentially Private Stochastic Convex Optimization with Heavy Tails

arXiv:2604.06492v1 Announce Type: new Abstract: We study stochastic convex optimization (SCO) with heavy-tailed gradients under pure epsilon-differential privacy (DP). Instead of assuming a bound on t

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Agents

ORACLE-SWE: Quantifying the Contribution of Oracle Information Signals on SWE Agents

DGX agent

arXiv:2604.07789v1 Announce Type: cross Abstract: Recent advances in language model (LM) agents have significantly improved automated software engineering (SWE). Prior work has proposed various agenti

agentsarxiv-cs-cl

10 Apr 2026

Agents

OrgForge: A Multi-Agent Simulation Framework for Verifiable Synthetic Corporate Corpora

arXiv:2603.14997v2 Announce Type: replace Abstract: Building and evaluating enterprise AI systems requires synthetic organizational corpora that are internally consistent, temporally structured, and c

agentsarxiv-cs-cl

Orion-Lite: Distilling LLM Reasoning into Efficient Vision-Only Driving Models

arXiv:2604.08266v1 Announce Type: new Abstract: Leveraging the general world knowledge of Large Language Models (LLMs) holds significant promise for improving the ability of autonomous driving systems

oslash Source Models Leak What They Shouldn't nrightarrow: Unlearning Zero-Shot Transfer in Domain Adaptation Through Adversarial Optimization

arXiv:2604.08238v1 Announce Type: new Abstract: The increasing adaptation of vision models across domains, such as satellite imagery and medical scans, has raised an emerging privacy risk: models may

OV-Stitcher: A Global Context-Aware Framework for Training-Free Open-Vocabulary Semantic Segmentation

arXiv:2604.08110v1 Announce Type: new Abstract: Training-free open-vocabulary semantic segmentation(TF-OVSS) has recently attracted attention for its ability to perform dense prediction by leveraging

OVS-DINO: Open-Vocabulary Segmentation via Structure-Aligned SAM-DINO with Language Guidance

arXiv:2604.08461v1 Announce Type: new Abstract: Open-Vocabulary Segmentation (OVS) aims to segment image regions beyond predefined category sets by leveraging semantic descriptions. While CLIP based a

OxEnsemble: Fair Ensembles for Low-Data Classification

arXiv:2512.09665v2 Announce Type: replace Abstract: We address the problem of fair classification in settings where data is scarce and unbalanced across demographic groups. Such low-data regimes are c

PAC-Bayesian Bounds on Constrained f-Entropic Risk Measures

arXiv:2510.11169v2 Announce Type: replace-cross Abstract: PAC generalization bounds on the risk, when expressed in terms of the expected loss, are often insufficient to capture imbalances between subg

researcharxiv-cs-lg

PANC: Prior-Aware Normalized Cut via Anchor-Augmented Token Graphs

arXiv:2602.06912v2 Announce Type: replace Abstract: Unsupervised segmentation from self-supervised ViT patches holds promise but lacks robustness: multi-object scenes confound saliency cues, and low-s

PanoSAM2: Lightweight Distortion- and Memory-aware Adaptions of SAM2 for 360 Video Object Segmentation

arXiv:2604.07901v1 Announce Type: new Abstract: 360 video object segmentation (360VOS) aims to predict temporally-consistent masks in 360 videos, offering full-scene coverage, benefiting applications,

Paragraph Segmentation Revisited: Towards a Standard Task for Structuring Speech

arXiv:2512.24517v2 Announce Type: replace Abstract: Automatic speech transcripts are often delivered as unstructured word streams that impede readability and repurposing. We recast paragraph segmentat

researcharxiv-cs-cl

Agents

ParkSense: Where Should a Delivery Driver Park? Leveraging Idle AV Compute and Vision-Language Models

arXiv:2604.07912v1 Announce Type: new Abstract: Finding parking consumes a disproportionate share of food delivery time, yet no system addresses precise parking-spot selection relative to merchant ent

agentsarxiv-cs-cv

ParseBench: A Document Parsing Benchmark for AI Agents

arXiv:2604.08538v1 Announce Type: new Abstract: AI agents are changing the requirements for document parsing. What matters is semantic correctness: parsed output must preserve the structure and

Part^{2}GS: Part-aware Modeling of Articulated Objects using 3D Gaussian Splatting

arXiv:2506.17212v2 Announce Type: replace Abstract: Articulated objects are common in the real world, yet modeling their structure and motion remains a challenging task for 3D reconstruction methods.

PASK: Toward Intent-Aware Proactive Agents with Long-Term Memory

model-releasesarxiv-cs-cl

arXiv:2604.08000v1 Announce Type: cross Abstract: Proactivity is a core expectation for AGI. Prior work remains largely confined to laboratory settings, leaving a clear gap in real-world proactive age

Path Regularization: A Near-Complete and Optimal Nonasymptotic Generalization Theory for Multilayer Neural Networks and Double Descent Phenomenon

arXiv:2503.02129v2 Announce Type: replace-cross Abstract: Path regularization has shown to be a very effective regularization to train neural networks, leading to a better generalization property than

safetyarxiv-cs-ai

Applications

PD-SOVNet: A Physics-Driven Second-Order Vibration Operator Network for Estimating Wheel Polygonal Roughness from Axle-Box Vibrations

arXiv:2604.06620v1 Announce Type: new Abstract: Quantitative estimation of wheel polygonal roughness from axle-box vibration signals is a challenging yet practically relevant problem for rail-vehicle

applicationsarxiv-cs-lg

PEER: Unified Process-Outcome Reinforcement Learning for Structured Empathetic Reasoning

arXiv:2508.09521v2 Announce Type: replace Abstract: Emotional support conversations require more than fluent responses. Supporters need to understand the seeker's situation and emotions, adopt an appr

safetyarxiv-cs-cl

PeReGrINE: Evaluating Personalized Review Fidelity with User Item Graph Context

model-releasesarxiv-cs-cl

arXiv:2604.07788v1 Announce Type: cross Abstract: We introduce PeReGrINE, a benchmark and evaluation framework for personalized review generation grounded in graph-structured user--item evidence. PeRe

Personalized RewardBench: Evaluating Reward Models with Human Aligned Personalization

arXiv:2604.07343v1 Announce Type: cross Abstract: Pluralistic alignment has emerged as a critical frontier in the development of Large Language Models (LLMs), with reward models (RMs) serving as a cen

Personalizing Text-to-Image Generation to Individual Taste

arXiv:2604.07427v1 Announce Type: new Abstract: Modern text-to-image (T2I) models generate high-fidelity visuals but remain indifferent to individual user preferences. While existing reward models opt

Phantasia: Context-Adaptive Backdoors in Vision Language Models

arXiv:2604.08395v1 Announce Type: new Abstract: Recent advances in Vision-Language Models (VLMs) have greatly enhanced the integration of visual perception and linguistic reasoning, driving rapid prog

Applications

Phantom: Physics-Infused Video Generation via Joint Modeling of Visual and Latent Physical Dynamics

arXiv:2604.08503v1 Announce Type: new Abstract: Recent advances in generative video modeling, driven by large-scale datasets and powerful architectures, have yielded remarkable visual realism. However

applicationsarxiv-cs-cv

PhyAVBench: A Challenging Audio Physics-Sensitivity Benchmark for Physically Grounded Text-to-Audio-Video Generation

model-releasesarxiv-cs-ai

arXiv:2512.23994v3 Announce Type: replace-cross Abstract: Text-to-audio-video (T2AV) generation is central to applications such as filmmaking and world modeling. However, current models often fail to

PhyEdit: Towards Real-World Object Manipulation via Physically-Grounded Image Editing

arXiv:2604.07230v2 Announce Type: replace Abstract: Achieving physically accurate object manipulation in image editing is essential for its potential applications in interactive world models. However,

Physical Adversarial Attacks on AI Surveillance Systems:Detection, Tracking, and Visible--Infrared Evasion

arXiv:2604.06865v1 Announce Type: cross Abstract: Physical adversarial attacks are increasingly studied in settings that resemble deployed surveillance systems rather than isolated image benchmarks. I

Physical Knot Classification Beyond Accuracy: A Benchmark and Diagnostic Study

arXiv:2603.23286v3 Announce Type: replace Abstract: Physical knot classification is a fine-grained task in which the intended cue is rope crossing structure, but high accuracy may still come from appe

Tutorials

Physically Plausible Human-Object Rendering from Sparse Views via 3D Gaussian Splatting

arXiv:2503.09640v2 Announce Type: replace-cross Abstract: Rendering realistic human-object interactions (HOIs) from sparse-view inputs is a challenging yet crucial task for various real-world applicat

tutorialsarxiv-cs-cv

Physics-Informed Functional Link Constrained Framework with Domain Mapping for Solving Bending Analysis of an Exponentially Loaded Perforated Beam

arXiv:2604.07025v1 Announce Type: cross Abstract: This article presents a novel and comprehensive approach for analyzing bending behavior of the tapered perforated beam under an exponential load. The

Physics-Informed Neural Networks for Joint Source and Parameter Estimation in Advection-Diffusion Equations

arXiv:2512.07755v3 Announce Type: replace-cross Abstract: Recent studies have demonstrated the success of deep learning in solving forward and inverse problems in engineering and scientific computing

Physics-Informed Spectral Modeling for Hyperspectral Imaging

arXiv:2508.21618v2 Announce Type: replace-cross Abstract: We present PhISM, a physics-informed deep learning architecture that learns without supervision to explicitly disentangle hyperspectral observ

Applications

PIArena: A Platform for Prompt Injection Evaluation

arXiv:2604.08499v1 Announce Type: cross Abstract: Prompt injection attacks pose serious security risks across a wide range of real-world applications. While receiving increasing attention, the communi

applicationsarxiv-cs-cl

PIKA: Expert-Level Synthetic Datasets for Post-Training Alignment from Scratch

model-releasesarxiv-cs-cl

arXiv:2510.06670v2 Announce Type: replace Abstract: High-quality instruction data is critical for LLM alignment, yet existing open-source datasets often lack efficiency, requiring hundreds of thousand

Pistachio: Towards Synthetic, Balanced, and Long-Form Video Anomaly Benchmarks

model-releasesarxiv-cs-ai

arXiv:2511.19474v4 Announce Type: replace-cross Abstract: Automatically detecting abnormal events in videos is crucial for modern autonomous systems, yet existing Video Anomaly Detection (VAD) benchma

Local Ai

PixelCAM: Pixel Class Activation Mapping for Histology Image Classification and ROI Localization

arXiv:2503.24135v3 Announce Type: replace Abstract: Weakly supervised object localization (WSOL) methods allow training models to classify images and localize ROIs. WSOL only requires low-cost image-c

local-aiarxiv-cs-cv

PlaneCycle: Training-Free 2D-to-3D Lifting of Foundation Models Without Adapters

arXiv:2603.04165v3 Announce Type: replace-cross Abstract: Large-scale 2D foundation models exhibit strong transferable representations, yet extending them to 3D volumetric data typically requires retr

Planning Task Shielding: Detecting and Repairing Flaws in Planning Tasks through Turning them Unsolvable

arXiv:2604.07042v1 Announce Type: new Abstract: Most research in planning focuses on generating a plan to achieve a desired set of goals. However, a goal specification can also be used to encode a pro

Planning with Minimal Disruption

arXiv:2508.15358v2 Announce Type: replace Abstract: In many planning applications, we might be interested in finding plans that minimally modify the initial state to achieve the goals. We refer to thi

Plasma GraphRAG: Physics-Grounded Parameter Selection for Gyrokinetic Simulations

model-releasesarxiv-cs-ai

arXiv:2604.06279v1 Announce Type: cross Abstract: Accurate parameter selection is fundamental to gyrokinetic plasma simulations, yet current practices rely heavily on manual literature reviews, leadin

Plug-and-Play Logit Fusion for Heterogeneous Pathology Foundation Models

arXiv:2604.07779v1 Announce Type: new Abstract: Pathology foundation models (FMs) have become central to computational histopathology, offering strong transfer performance across a wide range of diagn

PLUME: Latent Reasoning Based Universal Multimodal Embedding

arXiv:2604.02073v2 Announce Type: replace Abstract: Universal multimodal embedding (UME) maps heterogeneous inputs into a shared retrieval space with a single model. Recent approaches improve UME by g

PokeGym: A Visually-Driven Long-Horizon Benchmark for Vision-Language Models

arXiv:2604.08340v1 Announce Type: new Abstract: While Vision-Language Models (VLMs) have achieved remarkable progress in static visual understanding, their deployment in complex 3D embodied environmen

PolySLGen: Online Multimodal Speaking-Listening Reaction Generation in Polyadic Interaction

arXiv:2604.08125v1 Announce Type: new Abstract: Human-like multimodal reaction generation is essential for natural group interactions between humans and embodied AI. However, existing approaches are l

Agents

Predictive Representations for Skill Transfer in Reinforcement Learning

arXiv:2604.07016v1 Announce Type: new Abstract: A key challenge in scaling up Reinforcement Learning is generalizing learned behaviour. Without the ability to carry forward acquired knowledge an agent

agentsarxiv-cs-lg

Preventing Overfitting in Deep Image Prior for Hyperspectral Image Denoising

arXiv:2604.08272v1 Announce Type: new Abstract: Deep image prior (DIP) is an unsupervised deep learning framework that has been successfully applied to a variety of inverse imaging problems. However,

PriPG-RL: Privileged Planner-Guided Reinforcement Learning for Partially Observable Systems with Anytime-Feasible MPC

arXiv:2604.08036v1 Announce Type: cross Abstract: This paper addresses the problem of training a reinforcement learning (RL) policy under partial observability by exploiting a privileged, anytime-feas

safetyarxiv-cs-ro

Privacy Attacks on Image AutoRegressive Models

arXiv:2502.02514v5 Announce Type: replace Abstract: Image AutoRegressive generation has emerged as a new powerful paradigm with image autoregressive models (IARs) matching state-of-the-art diffusion m