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PEMANT: Persona-Enriched Multi-Agent Negotiation for Travel

arXiv:2604.10475v1 Announce Type: new Abstract: Modeling household-level trip generation is fundamental to accurate demand forecasting, traffic flow estimation, and urban system planning. Existing stu

PepBenchmark: A Standardized Benchmark for Peptide Machine Learning

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DGX agent

arXiv:2604.10531v1 Announce Type: cross Abstract: Peptide therapeutics are widely regarded as the 'third generation' of drugs, yet progress in peptide Machine Learning (ML) are hindered by the absence

model-releasesarxiv-cs-ai

14 Apr 2026

Applications

Perceived Importance of Cognitive Skills Among Computing Students in the Era of AI

arXiv:2604.10730v1 Announce Type: cross Abstract: The availability and increasing integration of generative AI tools have transformed computing education. While AI in education presents opportunities,

applicationsarxiv-cs-ai

PERCEPT-Net: A Perceptual Loss Driven Framework for Reducing MRI Artifact Tissue Confusion

arXiv:2604.10439v1 Announce Type: new Abstract: Purpose: Existing deep learning-based MRI artifact correction models exhibit poor clinical generalization due to inherent artifact-tissue confusion, fai

Perception-aware Exploration for Consumer-grade UAVs

arXiv:2511.14393v2 Announce Type: replace Abstract: In our work, we extend the current state-of-the-art approach for autonomous multi-UAV exploration to consumer-level UAVs, such as the DJI Mini 3 Pro

agentsarxiv-cs-ro

Perception Is All You Need: A Neuroscience Framework for Low Cost Sensorless Gaze in HRI

arXiv:2604.09829v1 Announce Type: new Abstract: Gaze-following in child-robot interaction improves attention, recall, and learning, but requires expensive platforms ($30,000+), sensors, algorithms, an

researcharxiv-cs-ro

Perceptual Inductive Bias Is What You Need Before Contrastive Learning

arXiv:2506.01201v2 Announce Type: replace Abstract: David Marr's seminal theory of human perception stipulates that visual processing is a multi-stage process, prioritizing the derivation of boundary

safetyarxiv-cs-cv

Performance Characterization of Frequency-Selective Wireless Power Transfer Toward Scalable Untethered Magnetic Actuation

arXiv:2604.11645v1 Announce Type: cross Abstract: Frequency-selective wireless power transfer provides a feasible route to enable independent actuation and control of multiple untethered robots in a c

researcharxiv-cs-ro

Persistent Identity in AI Agents: A Multi-Anchor Architecture for Resilient Memory and Continuity

arXiv:2604.09588v1 Announce Type: new Abstract: Modern AI agents suffer from a fundamental identity problem: when context windows overflow and conversation histories are summarized, agents experience

Persona Non Grata: Single-Method Safety Evaluation Is Incomplete for Persona-Imbued LLMs

arXiv:2604.11120v1 Announce Type: new Abstract: Personality imbuing customizes LLM behavior, but safety evaluations almost always study prompt-based personas alone. We show this is incomplete: prompti

Phonological distances for linguistic typology and the origin of Indo-European languages

arXiv:2604.11565v1 Announce Type: new Abstract: We show that short-range phoneme dependencies encode large-scale patterns of linguistic relatedness, with direct implications for quantitative typology

PhyMix: Towards Physically Consistent Single-Image 3D Indoor Scene Generation with Implicit--Explicit Optimization

model-releasesarxiv-cs-cv

arXiv:2604.10125v1 Announce Type: new Abstract: Existing single-image 3D indoor scene generators often produce results that look visually plausible but fail to obey real-world physics, limiting their

Physical Commonsense Reasoning for Lower-Resourced Languages and Dialects: a Study on Basque

arXiv:2602.14812v3 Announce Type: replace Abstract: Physical commonsense reasoning represents a fundamental capability of human intelligence, enabling individuals to understand their environment, pred

Physics and causally constrained discrete-time neural models of turbulent dynamical systems

arXiv:2602.13847v3 Announce Type: replace-cross Abstract: We present a framework for constructing physics and causally constrained neural models of turbulent dynamical systems from data. We first form

Physics-informed AI Accelerated Retention Analysis of Ferroelectric Vertical NAND: From Day-Scale TCAD to Second-Scale Surrogate Model

arXiv:2603.06881v2 Announce Type: replace-cross Abstract: Ferroelectric field-effect transistors (FeFET)-based vertical NAND (Fe-VNAND) has emerged as a promising candidate to overcome z-scaling limit

Physics-Informed State Space Models for Reliable Solar Irradiance Forecasting in Off-Grid Systems

arXiv:2604.11807v1 Announce Type: cross Abstract: The stable operation of autonomous off-grid photovoltaic systems dictates reliance on solar forecasting algorithms that respect atmospheric thermodyna

Physics-Informed Synthetic Dataset and Denoising TIE-Reconstructed Phase Maps in Transient Flows Using Deep Learning

arXiv:2604.10610v1 Announce Type: cross Abstract: High-speed quantitative phase imaging enables non-intrusive visualization of transient compressible gas flows and energetic phenomena. However, phase

PICon: A Multi-Turn Interrogation Framework for Evaluating Persona Agent Consistency

arXiv:2603.25620v2 Announce Type: replace Abstract: Large language model (LLM)-based persona agents are rapidly being adopted as scalable proxies for human participants across diverse domains. Yet the

safetyarxiv-cs-cl

Pioneer Agent: Continual Improvement of Small Language Models in Production

arXiv:2604.09791v1 Announce Type: new Abstract: Small language models are attractive for production deployment due to their low cost, fast inference, and ease of specialization. However, adapting them

Playing Along: Learning a Double-Agent Defender for Belief Steering via Theory of Mind

arXiv:2604.11666v1 Announce Type: cross Abstract: As large language models (LLMs) become the engine behind conversational systems, their ability to reason about the intentions and states of their dial

Please Make it Sound like Human: Encoder-Decoder vs. Decoder-Only Transformers for AI-to-Human Text Style Transfer

model-releasesarxiv-cs-cl

arXiv:2604.11687v1 Announce Type: new Abstract: AI-generated text has become common in academic and professional writing, prompting research into detection methods. Less studied is the reverse: system

Local Ai

Plug-and-Play Dramaturge: A Divide-and-Conquer Approach for Iterative Narrative Script Refinement via Collaborative LLM Agents

arXiv:2510.05188v2 Announce Type: replace Abstract: Although LLMs have been widely adopted for creative content generation, a single-pass process often struggles to produce high-quality long narrative

local-aiarxiv-cs-ai

Applications

PnP-CM: Consistency Models as Plug-and-Play Priors for Inverse Problems

arXiv:2509.22736v2 Announce Type: replace-cross Abstract: Diffusion models have found extensive use in solving inverse problems, by sampling from an approximate posterior distribution of data given th

applicationsarxiv-cs-ai

Point2Pose: Occlusion-Recovering 6D Pose Tracking and 3D Reconstruction for Multiple Unknown Objects Via 2D Point Trackers

model-releasesarxiv-cs-cv

arXiv:2604.10415v1 Announce Type: new Abstract: We present Point2Pose, a model-free method for causal 6D pose tracking of multiple rigid objects from monocular RGB-D video. Initialized only from spars

Applications

POINTS-Long: Adaptive Dual-Mode Visual Reasoning in MLLMs

arXiv:2604.11627v1 Announce Type: new Abstract: Multimodal Large Language Models (MLLMs) have recently demonstrated remarkable capabilities in cross-modal understanding and generation. However, the ra

applicationsarxiv-cs-cv

PointSplat: Efficient Geometry-Driven Pruning and Transformer Refinement for 3D Gaussian Splatting

arXiv:2604.09903v1 Announce Type: new Abstract: 3D Gaussian Splatting (3DGS) has recently unlocked real-time, high-fidelity novel view synthesis by representing scenes using explicit 3D primitives. Ho

Poisoning with A Pill: Circumventing Detection in Federated Learning

arXiv:2407.15389v2 Announce Type: replace Abstract: Without direct access to the client's data, federated learning (FL) is well-known for its unique strength in data privacy protection among existing

PokeRL: Reinforcement Learning for Pokemon Red

model-releasesarxiv-cs-lg

arXiv:2604.10812v1 Announce Type: new Abstract: Pokemon Red is a long-horizon JRPG with sparse rewards, partial observability, and quirky control mechanics that make it a challenging benchmark for rei

Policy-Guided Threat Hunting: An LLM enabled Framework with Splunk SOC Triage

arXiv:2603.23966v3 Announce Type: replace-cross Abstract: With frequently evolving Advanced Persistent Threats (APTs) in cyberspace, traditional security solutions approaches have become inadequate fo

Policy Split: Incentivizing Dual-Mode Exploration in LLM Reinforcement with Dual-Mode Entropy Regularization

arXiv:2604.11510v1 Announce Type: cross Abstract: To encourage diverse exploration in reinforcement learning (RL) for large language models (LLMs) without compromising accuracy, we propose Policy Spli

Polyglot Teachers: Evaluating Language Models for Multilingual Synthetic Data Generation

model-releasesarxiv-cs-cl

arXiv:2604.11290v1 Announce Type: new Abstract: Synthesizing supervised finetuning (SFT) data from language models (LMs) to teach smaller models multilingual tasks has become increasingly common. Howe

PoreDiT: A Scalable Generative Model for Large-Scale Digital Rock Reconstruction

arXiv:2604.10171v1 Announce Type: new Abstract: This manuscript presents PoreDiT, a novel generative model designed for high-efficiency digital rock reconstruction at gigavoxel scales. Addressing the

researcharxiv-cs-ai

Position-Agnostic Pre-Projection for Transformer Attention: Nonlinear Feature Construction and Content Skip Before Q/K/V

arXiv:2604.10791v1 Announce Type: new Abstract: We propose two complementary modifications to transformer attention blocks. First, a non-linear pre-projection MLP is inserted between layer norm and Q/

Position: The Hidden Costs and Measurement Gaps of Reinforcement Learning with Verifiable Rewards

arXiv:2509.21882v2 Announce Type: replace-cross Abstract: Reinforcement learning with verifiable rewards (RLVR) is a practical, scalable way to improve large language models on math, code, and other s

Positive-Unlabelled Active Learning to Curate a Dataset for Orca Resident Interpretation

arXiv:2602.09295v2 Announce Type: replace Abstract: This work presents the largest curation of Southern Resident Killer Whale (SRKW) acoustic data to date, also containing other marine mammals in thei

Local Ai

Post-Processing Methods for Improving Accuracy in MRI Inpainting

arXiv:2510.15282v2 Announce Type: replace-cross Abstract: Magnetic Resonance Imaging (MRI) is the primary imaging modality used in the diagnosis, assessment, and treatment planning for brain pathologi

local-aiarxiv-cs-ai

PosterGen: Aesthetic-Aware Multi-Modal Paper-to-Poster Generation via Multi-Agent LLMs

arXiv:2508.17188v2 Announce Type: replace Abstract: Multi-agent systems built upon large language models (LLMs) have demonstrated remarkable capabilities in tackling complex compositional tasks. In th

PoTable: Towards Systematic Thinking via Plan-then-Execute Stage Reasoning on Tables

arXiv:2412.04272v5 Announce Type: replace-cross Abstract: In recent years, table reasoning has garnered substantial research interest, particularly regarding its integration with Large Language Models

Powerful Training-Free Membership Inference Against Autoregressive Language Models

arXiv:2601.12104v2 Announce Type: replace-cross Abstract: Fine-tuned language models pose significant privacy risks, as they may memorize and expose sensitive information from their training data. Mem

Precision Synthesis of Multi-Tracer PET via VLM-Modulated Rectified Flow for Stratifying Mild Cognitive Impairment

model-releasesarxiv-cs-cv

arXiv:2604.11176v1 Announce Type: new Abstract: The biological definition of Alzheimer's disease (AD) relies on multi-modal neuroimaging, yet the clinical utility of positron emission tomography (PET)

Predicting Associations between Solar Flares and Coronal Mass Ejections Using SDO/HMI Magnetograms and a Hybrid Neural Network

arXiv:2604.10016v1 Announce Type: cross Abstract: Solar eruptions, including flares and coronal mass ejections (CMEs), have a significant impact on Earth. Some flares are associated with CMEs, and som

Tutorials

Predicting integers from continuous parameters

arXiv:2602.10751v2 Announce Type: replace Abstract: We study the problem of predicting numeric labels that are constrained to the integers or to a subrange of the integers. For example, the number of

tutorialsarxiv-cs-lg

Preference-Agile Multi-Objective Optimization for Real-time Vehicle Dispatching

arXiv:2604.10664v1 Announce Type: new Abstract: Multi-objective optimization (MOO) has been widely studied in literature because of its versatility in human-centered decision making in real-life appli

Preference Learning Unlocks LLMs' Psycho-Counseling Skills

arXiv:2502.19731v2 Announce Type: replace Abstract: Applying large language models (LLMs) to assist in psycho-counseling is an emerging and meaningful approach, driven by the significant gap between p

PrefPaint: Enhancing Medical Image Inpainting through Expert Human Feedback

arXiv:2506.21834v2 Announce Type: replace Abstract: Inpainting, the process of filling missing or corrupted image parts, has broad applications in medical imaging. However, generating anatomically acc

Premier: Personalized Preference Modulation with Learnable User Embedding in Text-to-Image Generation

arXiv:2603.20725v2 Announce Type: replace Abstract: Text-to-image generation has advanced rapidly, yet it still struggles to capture the nuanced user preferences. Existing approaches typically rely on

safetyarxiv-cs-cv

Tutorials

Preventing Latent Rehearsal Decay in Online Continual SSL with SOLAR

arXiv:2604.10586v1 Announce Type: cross Abstract: This paper explores Online Continual Self-Supervised Learning (OCSSL), a scenario in which models learn from continuous streams of unlabeled, non-stat

tutorialsarxiv-cs-cv

Principles Do Not Apply Themselves: A Hermeneutic Perspective on AI Alignment

arXiv:2604.10673v1 Announce Type: new Abstract: AI alignment is often framed as the task of ensuring that an AI system follows a set of stated principles or human preferences, but general principles r