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Pedagogical Promise and Peril of AI: A Text Mining Analysis of ChatGPT Research Discussions in Programming Education

arXiv:2605.00361v1 Announce Type: cross Abstract: GenAI systems such as ChatGPT are increasingly discussed in programming education, but the ways in which the research literature conceptualizes and fr

applicationsarxiv-cs-ai

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Human DGX agent

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PepSpecBench: A Unified Evaluation Benchmark for Peptide Tandem Mass Spectrometry Prediction

DGX agent

arXiv:2605.01945v1 Announce Type: new Abstract: Tandem mass spectrometry provides a high-throughput framework for identifying and quantifying proteins in complex biological samples. In computational p

model-releasesarxiv-cs-lg

5 May 2026

Perceptual Flow Network for Visually Grounded Reasoning

arXiv:2605.02730v1 Announce Type: new Abstract: Despite the success of Large-Vision Language Models (LVLMs), general optimization objectives (e.g., standard MLE) fail to constrain visual trajectories,

safetyarxiv-cs-cv

Local Ai

Personalized Federated Learning for Gradient Alignment

arXiv:2605.02143v1 Announce Type: new Abstract: Personalized federated learning (pFL) aims to adapt models to client specific data distributions, yet it often fails to reliably preserve personalized i

local-aiarxiv-cs-lg

Perturb and Correct: Post-Hoc Ensembles using Affine Redundancy

arXiv:2605.01632v1 Announce Type: new Abstract: Models that are indistinguishable on in-distribution data can behave very differently under distribution shift. We introduce Perturb-and-Correct (P&C),

Perturbation Dose Responses in Recursive LLM Loops: Raw Switching, Stochastic Floors, and Persistent Escape under Append, Replace, and Dialog Updates

arXiv:2605.02236v1 Announce Type: cross Abstract: Recursive language-model loops often settle into recognizable attractor-like patterns. The practical question is how much injected text is needed to m

Phase-map synthesis from magnitude-only MR images using conditional score-based diffusion models with application in training of accelerated MRI reconstruction models

arXiv:2605.01185v1 Announce Type: new Abstract: Accelerated magnetic resonance imaging (MRI) enabled by the training of deep learning (DL)-based image recon. models requires large and diverse raw k-sp

PhaseNet++: Phase-Aware Frequency-Domain Anomaly Detection for Industrial Control Systems via Phase Coherence Graphs

arXiv:2605.00929v1 Announce Type: new Abstract: Multivariate time series anomaly detection in ICS has attracted growing attention due to the increasing threat of cyber-physical attacks on critical inf

Phone2Act: A Low-Cost, Hardware-Agnostic Teleoperation System for Scalable VLA Data Collection

arXiv:2605.01948v1 Announce Type: new Abstract: Collecting diverse, high-quality manipulation data for Vision-Language-Action (VLA) model training remains prohibitively expensive for many research gro

applicationsarxiv-cs-ro

Physics-Informed Neural Learning for State Reconstruction and Parameter Identification in Coupled Greenhouse Climate Dynamics

arXiv:2605.02524v1 Announce Type: new Abstract: Physics-informed neural networks (PINNs) have recently emerged as a promising framework for integrating data-driven learning with physical knowledge. In

Physiology-Aware Masked Cross-Modal Reconstruction for Biosignal Representation Learning

arXiv:2605.00973v1 Announce Type: new Abstract: Biosignals acquired from different locations on the body often provide temporally ordered views of the same underlying physiological process. However, m

Pi-Change: A Prior-Informed Multiple Change Point Detection Algorithm

arXiv:2605.01003v1 Announce Type: cross Abstract: Statistical change point (CP) detection methods typically rely on likelihood-based inference and ignore contextual information about plausible CP loca

Tutorials

PiCSRL: Physics-Informed Contextual Spectral Reinforcement Learning

arXiv:2603.26816v2 Announce Type: replace Abstract: High-dimensional low-sample-size (HDLSS) datasets constrain reliable environmental model development, where labeled data remain sparse. Reinforcemen

tutorialsarxiv-cs-lg

Pixel Perfect: Relational Image Quality Assessment with Spatially-Aware Distortions

arXiv:2605.02863v1 Announce Type: new Abstract: Traditional image quality assessment (IQA) methods rely on mean opinion scores (MOS), which are resource-intensive to collect and fail to provide interp

Pixel-to-4D: Camera-Controlled Image-to-Video Generation with Dynamic 3D Gaussians

arXiv:2601.00678v2 Announce Type: replace Abstract: Humans excel at forecasting the future dynamics of a scene given just a single image. Video generation models that can mimic this ability are an ess

applicationsarxiv-cs-cv

Planner Matters! An Efficient and Unbalanced Multi-agent Collaboration Framework for Long-horizon Planning

arXiv:2605.02168v1 Announce Type: cross Abstract: Language model (LM)-based agents have demonstrated promising capabilities in automating complex tasks from natural language instructions, yet they con

PointCSP: Cross-Sample Semantic Propagation and Stability Preservation in Self-Supervised Point Cloud Learning

model-releasesarxiv-cs-cv

arXiv:2605.01759v1 Announce Type: new Abstract: Scene-level point cloud self-supervised learning (PC-SSL) has demonstrated potential in enhancing the generalization capability of 3D vision models. Des

Polynomial-Time Optimal Group Selection via the Double-Commutator Eigenvalue Problem

arXiv:2605.00834v1 Announce Type: new Abstract: The algebraic diversity framework replaces temporal averaging over multiple observations with algebraic group action on a single observation for second-

Poodle: Seamlessly Scaling Down Large Language Models with Just-in-Time Model Replacement

arXiv:2512.05525v2 Announce Type: replace-cross Abstract: Businesses increasingly rely on large language models (LLMs) to automate simple repetitive tasks instead of developing custom machine learning

PPO guided Agentic Pipeline for Adaptive Prompt Selection and Test Case Generation

arXiv:2605.00942v1 Announce Type: cross Abstract: Developing effective test cases capable of thoroughly exercising large-scale software systems is inherently difficult, especially if such systems have

PRCD-MAP: Learning How Much to Trust Imperfect Priors in Causal Discovery

arXiv:2605.01669v1 Announce Type: cross Abstract: External priors of unknown reliability create a brittle trade-off in causal discovery: blind trust amplifies errors, blind rejection wastes signal. Re

safetyarxiv-cs-lg

Predicting Post Virality with Temporal Cross-Attention over Trend Signals

arXiv:2605.02358v1 Announce Type: new Abstract: Current models for predicting social media virality rely heavily on static textual and structural features, effectively ignoring the highly dynamic natu

applicationsarxiv-cs-lg

Prescriptive Scaling Laws for Data Constrained Training

arXiv:2605.01640v1 Announce Type: cross Abstract: Training compute is increasingly outpacing the availability of high-quality data. This shifts the central challenge from optimal compute allocation to

Pretraining A Large Language Model using Distributed GPUs: A Memory-Efficient Decentralized Paradigm

arXiv:2602.11543v2 Announce Type: replace Abstract: Pretraining large language models (LLMs) typically requires centralized clusters with thousands of high-memory GPUs (e.g., H100/A100). Recent decent

Pretraining on Sleep Data Improves non-Sleep Biosignal Tasks

arXiv:2605.02500v1 Announce Type: new Abstract: Sleep foundation models have recently demonstrated strong performance on in-domain polysomnography tasks, including sleep staging, apnea detection, and

PRIME: Protein Representation via Physics-Informed Multiscale Equivariant Hierarchies

arXiv:2605.01625v1 Announce Type: new Abstract: Proteins are inherently multiscale physical systems whose functional properties emerge from coordinated structural organization across multiple spatial

Principles and Guidelines for Randomized Controlled Trials in AI Evaluation

arXiv:2605.02050v1 Announce Type: cross Abstract: This work establishes a foundational framework for standardizing AI evaluation RCTs (sometimes called human uplift studies). Drawing on established ex

Probabilistic Modeling of Multi-rater Medical Image Segmentation for Diversity and Personalization

arXiv:2512.00748v2 Announce Type: replace Abstract: Lesion segmentation is inherently influenced by imaging uncertainty, arising from ill-defined lesion boundaries and inter-observer variability in di

Probe-Geometry Alignment: Erasing the Cross-Sequence Memorization Signature Below Chance

arXiv:2605.01699v1 Announce Type: new Abstract: Recent attacks show that behavioural unlearning of large language models leaves internal traces recoverable by adversarial probes. We characterise where

Profile-Specific 3DMM Regression from a Single Lateral Face Image

arXiv:2605.01746v1 Announce Type: new Abstract: Single-image 3D face reconstruction is a core problem in computer vision, with important clinical applications such as cephalometric landmark analysis i

Progress Ratio Embeddings: An Impatience Signal for Robust Length Control in Neural Text Generation

arXiv:2512.06938v2 Announce Type: replace Abstract: Modern neural language models achieve high accuracy in text generation, yet precise control over generation length remains underdeveloped. In this p

researcharxiv-cs-cl

Projection-Free Transformers via Gaussian Kernel Attention

arXiv:2605.02144v1 Announce Type: new Abstract: Self-attention in Transformers is typically implemented as softmax(QK^op/sqrt{d})V, where Q=XW_Q, K=XW_K, and V=XW_V are learned linear projections of t

ProPACT: A Proactive AI-Driven Adaptive Collaborative Tutor for Pair Programming

arXiv:2605.02703v1 Announce Type: cross Abstract: Effective pair programming depends on coordination of attention, cognitive effort, and joint regulation over time, yet most adaptive learning systems

safetyarxiv-cs-lg

Prosa: Rubric-Based Evaluation of LLMs on Real User Chats in Brazilian Portuguese

arXiv:2605.01630v1 Announce Type: new Abstract: Rankings produced by holistic LLM-as-a-judge scoring are sensitive to the bias of the chosen judge model. We show that switching to binary rubric scorin

Protein-Conditioned Multi-Objective Reinforcement Learning for Full-Length mRNA Design

arXiv:2605.01513v1 Announce Type: new Abstract: Designing therapeutic messenger RNA (mRNA) requires creating full-length transcripts that carefully balance stability, translation efficiency, and immun

safetyarxiv-cs-lg

ProtoFair: Fair Self-Supervised Contrastive Learning via Pseudo-Counterfactual Pairs

arXiv:2605.01971v1 Announce Type: new Abstract: Self-supervised learning methods learn high-quality visual representations, yet recent studies show that these representations often capture demographic

safetyarxiv-cs-cv

Provable Benefit of Curriculum in Transformer Tree-Reasoning Post-Training

arXiv:2511.07372v3 Announce Type: replace Abstract: Recent curriculum techniques in the post-training stage of LLMs have been empirically observed to outperform non-curriculum approaches in improving

Tutorials

Provably Learning Attention with Queries

arXiv:2601.16873v2 Announce Type: replace Abstract: We study the problem of learning Transformer-based sequence models with black-box access to their outputs. In this setting, a learner may adaptively

tutorialsarxiv-cs-lg

Pruning Federated Models through Loss Landscape Analysis and Client Agreement Scoring

arXiv:2405.10271v4 Announce Type: replace Abstract: The practical deployment of Federated Learning (FL) on resource-constrained devices is fundamentally limited by the high cost of training large mode

Psychologically Potent, Computationally Invisible: LLMs Generate Social-Comparison Triggers They Fail to Detect

arXiv:2605.01017v1 Announce Type: new Abstract: We introduce Xiaohongshu Social Comparison Reader Elicitation (XHS-SCoRE), a reader-grounded benchmark for detecting if a text-only Xiaohongshu (RedNote

PubMed-Ophtha: An open resource for training ophthalmology vision-language models on scientific literature

arXiv:2605.02720v1 Announce Type: cross Abstract: Vision-language models hold considerable promise for ophthalmology, but their development depends on large-scale, high-quality image-text datasets tha

researcharxiv-cs-cl

PuppetAI: A Customizable Platform for Designing Tactile-Rich Affective Robot Interaction

arXiv:2602.04787v2 Announce Type: replace-cross Abstract: We introduce PuppetAI, a modular soft robot interaction platform. This platform offers a scalable cable-driven actuation system and a customiz

applicationsarxiv-cs-ro

Q-RAG: Long Context Multi-step Retrieval via Value-based Embedder Training

arXiv:2511.07328v2 Announce Type: replace Abstract: Retrieval-Augmented Generation (RAG) methods enhance LLM performance by efficiently filtering relevant context for LLMs, reducing hallucinations and

QHyer: Q-conditioned Hybrid Attention-mamba Transformer for Offline Goal-conditioned RL

arXiv:2605.01862v1 Announce Type: new Abstract: Offline goal-conditioned RL (GCRL) learns goal-reaching policies from static datasets, but real-world datasets are often partially observable and histor

applicationsarxiv-cs-lg

Hardware

Quant VideoGen: Auto-Regressive Long Video Generation via 2-Bit KV-Cache Quantization

arXiv:2602.02958v4 Announce Type: replace Abstract: Despite rapid progress in autoregressive video diffusion, an emerging system algorithm bottleneck limits both deployability and generation capabilit

hardwarearxiv-cs-lg

Quantifying and Predicting Disagreement in Graded Human Ratings

arXiv:2605.01168v1 Announce Type: new Abstract: It is increasingly recognized that human annotators do not always agree, and such disagreement is inherent in many annotation tasks. However, not all in

researcharxiv-cs-cl

Quantifying Multimodal Capabilities: Formal Generalization Guarantees in Pairwise Metric Learning

arXiv:2605.01424v1 Announce Type: new Abstract: Multimodal learning leverages the integration of diverse data modalities to enhance performance in complex tasks. Yet, it frequently encounters incomple

applicationsarxiv-cs-lg

Quantum-inspired Techniques in Tensor Networks for Industrial Contexts

arXiv:2404.11277v2 Announce Type: replace-cross Abstract: In this paper we present a study of the applicability and feasibility of quantum-inspired algorithms and techniques in tensor networks for ind