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Pedestrians play chicken with an autonomous vehicle

arXiv:2604.24384v1 Announce Type: new Abstract: Automated vehicles (AVs) are commonly programmed to yield unconditionally to pedestrians in the interest of safety. However, this design choice can give

safetyarxiv-cs-ro

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Peer Identity Bias in Multi-Agent LLM Evaluation: An Empirical Study Using the TRUST Democratic Discourse Analysis Pipeline

DGX agent

arXiv:2604.22971v1 Announce Type: cross Abstract: The TRUST democratic discourse analysis pipeline exposes its large language model (LLM) components to peer model identity through multiple structural

safetyarxiv-cs-ai

28 Apr 2026

PeeriScope: A Multi-Faceted Framework for Evaluating Peer Review Quality

arXiv:2604.24071v1 Announce Type: new Abstract: The increasing scale and variability of peer review in scholarly venues has created an urgent need for systematic, interpretable, and extensible tools t

researcharxiv-cs-cl

PEPS: Positional Encoding Projected Sampling -- Extended

arXiv:2604.24167v1 Announce Type: new Abstract: Implicit neural representations (INRs) are increasingly being used as tools to map coordinates to signals, encompassing applications from neural fields

tutorialsarxiv-cs-cv

Perfecting Aircraft Maneuvers with Reinforcement Learning

arXiv:2604.24338v1 Announce Type: new Abstract: This paper evaluates an advanced jet trainer's utilization of artificial intelligence (AI)-based aircraft aerobatic maneuvers with the intention of deve

Personality Shapes Gender Bias in Persona-Conditioned LLM Narratives Across English and Hindi: An Empirical Investigation

arXiv:2604.23600v1 Announce Type: new Abstract: Large Language Models (LLMs) are increasingly deployed in persona-driven applications such as education, customer service, and social platforms, where m

safetyarxiv-cs-cl

Personalized Worked Example Generation from Student Code Submissions using Pattern-based Knowledge Components

arXiv:2604.24758v1 Announce Type: cross Abstract: Adaptive programming practice often relies on fixed libraries of worked examples and practice problems, which require substantial authoring effort and

researcharxiv-cs-ai

Personalizing Causal Audio-Driven Facial Motion via Dynamic Multi-modal Retrieval

arXiv:2604.23692v1 Announce Type: cross Abstract: Audio-driven facial animation is essential for immersive digital interaction, yet existing frameworks fail to reconcile real-time streaming with high-

PExA: Parallel Exploration Agent for Complex Text-to-SQL

arXiv:2604.22934v1 Announce Type: new Abstract: LLM-based agents for text-to-SQL often struggle with latency-performance trade-off, where performance improvements come at the cost of latency or vice v

Phase-Separated Complex Hilbert PCA on Markerless 3D Pose Estimation Data: A Global Phase Network and Its Extension to a Continuous Field on the Body Surface

arXiv:2604.24415v1 Announce Type: cross Abstract: Quantitative analysis of the kinematic chain in sports motion is essential for performance evaluation and injury prevention. Conventional methods such

PhysCodeBench: Benchmarking Physics-Aware Symbolic Simulation of 3D Scenes via Self-Corrective Multi-Agent Refinement

arXiv:2604.23580v1 Announce Type: cross Abstract: Physics-aware symbolic simulation of 3D scenes is critical for robotics, embodied AI, and scientific computing, requiring models to understand natural

Agents

PhySE: A Psychological Framework for Real-Time AR-LLM Social Engineering Attacks

arXiv:2604.23148v1 Announce Type: new Abstract: The emerging threat of AR-LLM-based Social Engineering (AR-LLM-SE) attacks (e.g. SEAR) poses a significant risk to real-world social interactions. In su

agentsarxiv-cs-ai

Physics-Informed Temporal U-Net for High-Fidelity Fluid Interpolation

arXiv:2604.23372v1 Announce Type: cross Abstract: Reconstructing high-fidelity fluid dynamics from sparse temporal observations is quite challenging, mainly due to the chaotic and non-linear nature of

PhysLayer: Language-Guided Layered Animation with Depth-Aware Physics

arXiv:2604.23574v1 Announce Type: new Abstract: Existing image-to-video generation methods often produce physically implausible motions and lack precise control over object dynamics. While prior appro

safetyarxiv-cs-cv

Agents

PhysNote: Self-Knowledge Notes for Evolvable Physical Reasoning in Vision-Language Model

arXiv:2604.24443v1 Announce Type: new Abstract: Vision-Language Models (VLMs) have demonstrated strong performance on textbook-style physics problems, yet they frequently fail when confronted with dyn

agentsarxiv-cs-ai

PivotMerge: Bridging Heterogeneous Multimodal Pre-training via Post-Alignment Model Merging

arXiv:2604.22823v1 Announce Type: cross Abstract: Multimodal Large Language Models (MLLMs) rely on multimodal pre-training over diverse data sources, where different datasets often induce complementar

Pixel-Translation-Equivariant Quantum Convolutional Neural Networks via Fourier Multiplexers

arXiv:2604.06094v2 Announce Type: replace-cross Abstract: Convolutional neural networks owe much of their success to hard-coding translation equivariance. Quantum convolutional neural networks (QCNNs)

Planning Under Observation Mismatch for Traffic Signal Control via Adaptive Modular World Models

arXiv:2501.02548v2 Announce Type: replace-cross Abstract: Deploying learned decision-making systems often requires transferring to new sites where the sensing pipeline differs. In such cases, observat

researcharxiv-cs-ai

POCA: Pareto-Optimal Curriculum Alignment for Visual Text Generation

arXiv:2604.24171v1 Announce Type: new Abstract: Current visual text generation models struggle with the trade-off between text accuracy and overall image coherence. We find that achieving high text ac

safetyarxiv-cs-cv

Point Cloud Registration for Fusion between SPECT MPI and CTA Images

arXiv:2604.24524v1 Announce Type: new Abstract: Clinical fusion of Single Photon Emission Computed Tomography Myocardial Perfusion Imaging (SPECT MPI) and Computed Tomography Angiography (CTA) remains

Point-MF: One-step Point Cloud Generation from a Single Image via Mean Flows

arXiv:2604.24586v1 Announce Type: new Abstract: Single-image point cloud reconstruction must infer complete 3D geometry, including occluded parts, from a single RGB image. While diffusion-based recons

tutorialsarxiv-cs-cv

Hardware

PointTransformerX:Portable and Efficient 3D Point Cloud Processing without Sparse Algorithms

arXiv:2604.24169v1 Announce Type: new Abstract: 3D point cloud perception remains tightly coupled to custom CUDA operators for spatial operations, limiting portability and efficiency on non-NVIDIA, AM

hardwarearxiv-cs-cv

Polychromic Objectives for Reinforcement Learning

arXiv:2509.25424v5 Announce Type: replace-cross Abstract: Reinforcement learning fine-tuning (RLFT) is a dominant paradigm for improving pretrained policies for downstream tasks. These pretrained poli

safetyarxiv-cs-ai

POPI: Personalizing LLMs via Optimized Natural Language Preference Inference

arXiv:2510.17881v3 Announce Type: replace-cross Abstract: Large language models (LLMs) are typically aligned with population-level preferences, despite substantial variation across individual users. W

researcharxiv-cs-ai

PoseX: AI Defeats Physics Approaches on Protein-Ligand Cross Docking

model-releasesarxiv-cs-lg

arXiv:2505.01700v3 Announce Type: replace Abstract: Existing protein-ligand docking studies typically focus on the self-docking scenario, which is less practical in real applications. Moreover, some s

Position: Logical Soundness is not a Reliable Criterion for Neurosymbolic Fact-Checking with LLMs

arXiv:2604.04177v2 Announce Type: replace Abstract: As large language models (LLMs) are increasing integrated into fact-checking pipelines, formal logic is often proposed as a rigorous means by which

safetyarxiv-cs-cl

Agents

Poster: ClawdGo: Endogenous Security Awareness Training for Autonomous AI Agents

arXiv:2604.24020v1 Announce Type: cross Abstract: Autonomous AI agents deployed on platforms such as OpenClaw face prompt injection, memory poisoning, supply-chain attacks, and social engineering, yet

agentsarxiv-cs-ai

POUR: A Provably Optimal Method for Unlearning Representations via Neural Collapse

arXiv:2511.19339v2 Announce Type: replace Abstract: In computer vision, machine unlearning aims to remove the influence of specific visual concepts or training images without retraining from scratch.

PRAXIS: Integrating Program Analysis with Observability for Root-Cause Analysis

arXiv:2512.22113v2 Announce Type: replace-cross Abstract: Unresolved production cloud incidents cost an average of over $2M per hour. This paper introduces PRAXIS, an orchestrator that manages and dep

Predicting one-year clinical instability and mortality in heart failure patients using sequence modeling

arXiv:2511.16839v3 Announce Type: replace-cross Abstract: Heart failure (HF) discharge planning depends on identifying patients at risk of deterioration or death, yet accurate prediction from routinel

Predicting Wind Loads on Container Ships in Harbor Environments through Multi-Fidelity Modeling

model-releasesarxiv-cs-lg

arXiv:2604.22882v1 Announce Type: new Abstract: Modern container ships face higher wind loads due to increased windage areas, making accurate predictions of wind loads essential for mooring design. Ex

Pref-CTRL: Preference Driven LLM Alignment using Representation Editing

arXiv:2604.23543v1 Announce Type: cross Abstract: Test-time alignment methods offer a promising alternative to fine-tuning by steering the outputs of large language models (LLMs) at inference time wit

Prenatal Stress Detection from Electrocardiography Using Self-Supervised Deep Learning: Development and External Validation

arXiv:2602.03886v3 Announce Type: replace-cross Abstract: Prenatal psychological stress affects 15-25% of pregnancies and increases risks of preterm birth, low birth weight, and adverse neurodevelopme

Preserving Long-Tailed Expert Information in Mixture-of-Experts Tuning

arXiv:2604.23036v1 Announce Type: cross Abstract: Despite MoE models leading many benchmarks, supervised fine-tuning (SFT) for the MoE architectures remains difficult because its router layers are fra

safetyarxiv-cs-cl

Primitive Recursion without Composition: Dynamical Characterizations, from Neural Networks to Polynomial ODEs

model-releasesarxiv-cs-lg

arXiv:2604.24356v1 Announce Type: cross Abstract: What do recurrent neural networks, polynomial ODEs, and discrete polynomial maps each bring to computation, and what do they lack? All three operate o

Hardware

Prior-Agnostic Robust Forecast Aggregation

arXiv:2604.24517v1 Announce Type: new Abstract: Robust forecast aggregation combines the predictions of multiple information sources to perform well in the worst case across all possible information s

hardwarearxiv-cs-lg

Probabilistic Graphical Model using Graph Neural Networks for Bayesian Inversion of Discrete Structural Component States

arXiv:2604.23514v1 Announce Type: cross Abstract: The health condition of components in civil infrastructures can be described by various discrete states according to their performance degradation. In

Probe-Based Data Attribution: Discovering and Mitigating Undesirable Behaviors in LLM Post-Training

arXiv:2602.11079v3 Announce Type: replace-cross Abstract: We propose probe-based data attribution, a method that traces behavioral changes in post-trained language models to responsible training datap

Probing CLIP's Comprehension of 360-Degree Textual and Visual Semantics

arXiv:2604.24642v1 Announce Type: new Abstract: The dream of instantly creating rich 360-degree panoramic worlds from text is rapidly becoming a reality, yet a crucial gap exists in our ability to rel

safetyarxiv-cs-cv

Probing Visual Planning in Image Editing Models

arXiv:2604.22868v1 Announce Type: cross Abstract: Visual planning represents a crucial facet of human intelligence, especially in tasks that require complex spatial reasoning and navigation. Yet, in m

tutorialsarxiv-cs-ai

Process Supervision of Confidence Margin for Calibrated LLM Reasoning

arXiv:2604.23333v1 Announce Type: cross Abstract: Scaling test-time computation with reinforcement learning (RL) has emerged as a reliable path to improve large language models (LLM) reasoning ability

researcharxiv-cs-cl

ProEval: Proactive Failure Discovery and Efficient Performance Estimation for Generative AI Evaluation

arXiv:2604.23099v1 Announce Type: cross Abstract: Evaluating generative AI models is increasingly resource-intensive due to slow inference, expensive raters, and a rapidly growing landscape of models

safetyarxiv-cs-ai

Progressive Approximation in Deep Residual Networks: Theory and Validation

arXiv:2604.24154v1 Announce Type: cross Abstract: The Universal Approximation Theorem (UAT) guarantees universal function approximation but does not explain how residual models distribute approximatio

Projected Attainable Speed Space: A Driving Efficiency Metric Connecting Instantaneous Evaluation to Travel Time

model-releasesarxiv-cs-ro

arXiv:2604.24295v1 Announce Type: new Abstract: Inefficient driving behaviors, such as overly conservative yielding, remain a key obstacle to deployment of autonomous vehicles (AVs). Instantaneous dri

Propagation Structure-Semantic Transfer Learning for Robust Fake News Detection

arXiv:2604.23974v1 Announce Type: new Abstract: Fake news generally refers to false information that is spread deliberately to deceive people, which has detrimental social effects. Existing fake news

tutorialsarxiv-cs-cl

Protecting the Trace: A Principled Black-Box Approach Against Distillation Attacks

arXiv:2604.23238v1 Announce Type: cross Abstract: Frontier models push the boundaries of what is learnable at extreme computational costs, yet distillation via sampling reasoning traces exposes closed

safetyarxiv-cs-ai

Psychologically-Grounded Graph Modeling for Interpretable Depression Detection

model-releasesarxiv-cs-cl

arXiv:2604.24126v1 Announce Type: new Abstract: Automatic depression detection from conversational interactions holds significant promise for scalable screening but remains hindered by severe data sca

Pushing Radar Odometry Beyond the Pavement: Current Capabilities and Challenges

arXiv:2604.24674v1 Announce Type: new Abstract: Radar offers unique advantages for localization in unstructured environments, including robustness to weather, lighting, and airborne particulates. Whil

researcharxiv-cs-ro