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PQR: A Framework to Generate Diverse and Realistic User Queries that Elicit QA Agent Failures

arXiv:2605.16551v1 Announce Type: new Abstract: Evaluating LLM-based agents remains challenging because identifying meaningful failure cases often requires substantial human effort to design realistic

safetyarxiv-cs-cl

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Research

Preconditioned Norms: A Unified Framework for Steepest Descent, Quasi-Newton and Adaptive Methods

DGX agent

arXiv:2510.10777v3 Announce Type: replace Abstract: Optimization lies at the core of modern deep learning, yet existing methods often face a fundamental trade-off between adapting to problem geometry

researcharxiv-cs-lg

19 May 2026

Predictable Confabulations: Factual Recall by LLMs Scales with Model Size and Topic Frequency

arXiv:2605.18732v1 Announce Type: cross Abstract: While scaling laws govern aggregate large language model performance, no scaling law has linked factual recall to both model size and training-data co

Applications

Prediction-Intervention Games and Invariant Sets

arXiv:2605.16828v1 Announce Type: cross Abstract: We consider the following two-player game: using observational data, the leader chooses a prediction function for a response variable Y from given cov

applicationsarxiv-cs-ai

Prediction of Challenging Behaviors Associated with Profound Autism in a Classroom Setting Using Wearable Sensors

arXiv:2605.17618v1 Announce Type: new Abstract: Autism Spectrum Disorder (ASD) is characterized by challenges with social interaction and communication and by restricted or repetitive patterns of thou

safetyarxiv-cs-ai

Predictive Prefetching for Retrieval-Augmented Generation

arXiv:2605.17989v1 Announce Type: cross Abstract: Retrieval-Augmented Generation (RAG) improves factual grounding in large language models but suffers from substantial latency due to synchronous retri

researcharxiv-cs-ai

Preference Instability in Reward Models: Detection and Mitigation via Sparse Autoencoders

arXiv:2605.16339v1 Announce Type: new Abstract: Preference learning in large language models relies on reward models as proxies for human judgment. However, these models frequently exhibit preference

Prefix-Adaptive Block Diffusion for Efficient Document Recognition

arXiv:2605.16861v1 Announce Type: cross Abstract: Block Diffusion Models (BDMs) support parallel generation, flexible-length output, and KV caching, making them promising for efficient document parsin

researcharxiv-cs-ai

Presupposition and Reasoning in Conditionals: A Theory-Based Study of Humans and LLMs

arXiv:2605.18352v1 Announce Type: new Abstract: Presupposition projection in conditionals is central to theories of meaning and pragmatics, yet it remains largely unevaluated in large language models.

safetyarxiv-cs-cl

PriHA: A RAG-Enhanced LLM Framework for Primary Healthcare Assistant in Hong Kong

arXiv:2604.14215v2 Announce Type: replace-cross Abstract: To address the unsustainable rise in public health expenditures, the Hong Kong SAR Government is shifting its strategic focus to primary healt

PRIME: Physically-consistent Robotic Inertial and Motion Estimation for Legged and Humanoid Robots

model-releasesarxiv-cs-ro

arXiv:2605.17681v1 Announce Type: new Abstract: Humanoid and legged robots interact with the environment through intermittent contacts, making accurate motion estimation fundamentally dependent on rea

Principal Component Analysis for Lunar Crater Detection

arXiv:2605.17125v1 Announce Type: new Abstract: Optical navigation is a critical component for lunar orbiter and lander missions. Image-based crater identification has emerged as a promising technolog

researcharxiv-cs-cv

Principles of frugal inference and control

arXiv:2406.14427v4 Announce Type: replace Abstract: A central challenge for intelligent agents in an uncertain world is striking the right balance between utility maximization and resource use, not on

local-aiarxiv-cs-ai

Agents

Prior Knowledge Makes It Possible: From Sublinear Graph Algorithms to LLM Test-Time Methods

arXiv:2510.16609v3 Announce Type: replace-cross Abstract: Test-time augmentation, such as Retrieval-Augmented Generation (RAG) or tool use, critically depends on an interplay between a model's paramet

agentsarxiv-cs-ai

PRISMat: Policy-Driven, Permutation-Invariant Autoregressive Material Generation

arXiv:2605.16612v1 Announce Type: new Abstract: Rapid identification of candidate materials with target properties has become a key task in materials science. Machine learning has emerged as an altern

Privacy Policy Enforcement Guardrails for Data-Sensitive Retrieval-Augmented Generation

arXiv:2605.17034v1 Announce Type: cross Abstract: Standard PII filters often miss contextual data leakage in RAG systems, such as non-regulated attribute clusters that collectively identify individual

Privacy-Preserving Generation Fraud Detection for Distributed Photovoltaic Systems: A Solar Irradiance-Fused Federated Learning Framework

arXiv:2605.17039v1 Announce Type: new Abstract: The wide adoption of residential photovoltaic (PV) systems introduces new challenges for generation fraud detection (FD). Unlike traditional electricity

Privacy Preserving Reinforcement Learning with One-Sided Feedback

arXiv:2605.18246v1 Announce Type: cross Abstract: We study reinforcement learning (RL) in multi-dimensional continuous state and action spaces with one-sided feedback, where the agent receives partial

Probing for Representation Manifolds in Superposition

arXiv:2605.18537v1 Announce Type: cross Abstract: This paper introduces the Manifold Probe, a supervised method for discovering representation manifolds in superposition. The method generalizes linear

Probing SMEFT Operators through tar{t}tar{t} Production with Hyper-Graph Neural Networks at the LHC

arXiv:2605.18382v1 Announce Type: cross Abstract: We present a phenomenological study of tar{t}tar{t} production in proton-proton collisions at sqrt{s} = 13~TeV, using a Hyper-Graph Neural Network (H-

ProfBench: Multi-Domain Rubrics requiring Professional Knowledge to Answer and Judge

arXiv:2510.18941v2 Announce Type: replace-cross Abstract: Evaluating progress in large language models (LLMs) is often constrained by the challenge of verifying responses, limiting assessments to task

Hardware

Progressive Generalization Augmentation with Deeply Coupled RND-PPO and Domain-Prioritized Noise Injection for Robust Crop Management Reinforcement Learning

arXiv:2605.17428v1 Announce Type: cross Abstract: Our preliminary experiments on gym-DSSAT maize irrigation tasks revealed that +/-2 degrees C temperature noise causes an 11.9% reduction in economic r

hardwarearxiv-cs-ai

Prompt Compression in Diffusion Large Language Models: Evaluating LLMLingua-2 on LLaDA

arXiv:2605.17932v1 Announce Type: cross Abstract: Prompt compression reduces inference cost and context length in large language models, but prior evaluations focus primarily on autoregressive archite

Prompt2Fingerprint: Plug-and-Play LLM Fingerprinting via Text-to-Weight Generation

arXiv:2605.18474v1 Announce Type: cross Abstract: The widespread deployment and redistribution of large language models (LLMs) have made model provenance tracking a critical challenge. While existing

Tutorials

PromptDecipher: Supporting AI Tutor Authoring Through Editable Simulated Interactions

arXiv:2605.16605v1 Announce Type: cross Abstract: Chatbots have long been explored as tools to support learning, and recent advances in large language models have significantly expanded the availabili

tutorialsarxiv-cs-ai

Prompts Don't Protect: Architectural Enforcement via MCP Proxy for LLM Tool Access Control

arXiv:2605.18414v1 Announce Type: cross Abstract: Large language models increasingly operate as autonomous agents that select and invoke tools from large registries. We identify a critical gap: when u

Agents

Proof-Carrying Certificates for LLM Pipelines: A Trust-Boundary Architecture

arXiv:2605.16407v1 Announce Type: cross Abstract: We present a framework for verifying the deterministic structured computations surrounding a large language model rather than the model itself, extend

agentsarxiv-cs-cl

Propagation of Chaos in Contextual Flow Maps

arXiv:2605.16747v1 Announce Type: new Abstract: We develop a quantitative statistical theory of transformers in the large-context regime by adopting the abstraction of contextual flow maps (CFMs): dyn

PropGuard: Safeguarding LLM-MAS via Propagation-Aware Exploration and Remediation

arXiv:2605.16346v1 Announce Type: cross Abstract: LLM-based multi-agent systems (LLM-MAS) have become a promising paradigm for solving complex tasks through role specialization, tool use, memory, and

local-aiarxiv-cs-ai

PROTEA: Offline Evaluation and Iterative Refinement for Multi-Agent LLM Workflows

arXiv:2605.18032v1 Announce Type: cross Abstract: Multi-agent LLM workflows -- systems composed of multiple role-specific LLM calls -- often outperform single-prompt baselines, but they remain difficu

local-aiarxiv-cs-ai

Protection Is (Nearly) All You Need: Structural Protection Dominates Scoring in Globally Capped KV Eviction

model-releasesarxiv-cs-cl

arXiv:2605.18053v1 Announce Type: cross Abstract: We study KV cache eviction under a shared globally capped decode-time harness. Seven policies (LRU, H2O, SnapKV, StreamingLLM, Ada-KV, QUEST, Random)

Protein Fold Classification at Scale: Benchmarking and Pretraining

model-releasesarxiv-cs-lg

arXiv:2605.18552v1 Announce Type: new Abstract: Classifying protein topology is essential for deciphering biological function, but progress is held back by the lack of large-scale benchmarks that avoi

ProtoFlow: Mitigating Forgetting in Class-Incremental Remote Sensing Segmentation via Low-Curvature Prototype Flow

arXiv:2604.03212v2 Announce Type: replace Abstract: Remote sensing segmentation in real deployment is inherently continual: new semantic categories emerge, and acquisition conditions shift across seas

researcharxiv-cs-cv

ProtoSiTex: Learning Semi-Interpretable Prototypes for Multi-label Text Classification

arXiv:2510.12534v4 Announce Type: replace Abstract: The rapid growth of user-generated text across digital platforms has intensified the need for interpretable models capable of fine-grained text clas

Provable Knowledge Acquisition and Extraction in One-Layer Transformers

model-releasesarxiv-cs-cl

arXiv:2508.00901v4 Announce Type: replace-cross Abstract: Large language models may encounter factual knowledge during pre-training yet fail to reliably use that knowledge after fine-tuning. Despite g

Provably Shorter Scratchpads in Hybrid DeltaNet-Attention Decoders

model-releasesarxiv-cs-lg

arXiv:2605.16640v1 Announce Type: new Abstract: We investigate the expressive power of hybrid recurrent-attention decoders, a class of architectures used in recent open-source language models such as

Proximal basin hopping: global optimization with guarantees

arXiv:2605.18364v1 Announce Type: new Abstract: Global optimization is a challenging problem, with plenty of algorithms displaying empirical success, but scarce theoretical backing. In this work, we p

Proximal-IMH: Proximal Posterior Proposals for Independent Metropolis-Hastings with Approximate Operators

arXiv:2602.21426v2 Announce Type: replace Abstract: We consider the problem of sampling from a posterior distribution arising in Bayesian inverse problems in science, engineering, and imaging. Our met

ProxyKV: Cross-Model Proxy Pruning for Efficient Long-Context LLM Inference

arXiv:2605.16360v1 Announce Type: cross Abstract: Efficient long-context inference in Large Language Models (LLMs) is severely constrained by the Key-Value (KV) cache memory wall, yet existing pruning

Prune, Update and Trim: Robust Structured Pruning for Large Language Models

arXiv:2605.18331v1 Announce Type: new Abstract: Large Language Models (LLMs) have experienced significant growth and development in recent years. However, performing inference on LLMs remains costly,

Public-Decay Homomorphic State Space Models for Private Sequence Inference

arXiv:2605.16647v1 Announce Type: cross Abstract: Fully homomorphic encryption (FHE) changes sequence-model design because rotations, encrypted products, ciphertext materialization, multiplicative dep

Agents

PULSE: Agentic Investigation with Passive Sensing for Proactive Intervention in Cancer Survivorship

arXiv:2605.17679v1 Announce Type: cross Abstract: Cancer survivors face elevated rates of depression, anxiety, and general emotional distress, yet the precise moments they most need support are often

agentsarxiv-cs-ai

PULSE: Generative Phase Evolution for Non-Stationary Time Series Forecasting

arXiv:2605.16793v1 Announce Type: new Abstract: Time series forecasting under non-stationarity faces a fundamental tension between capturing stable representations and adapting to distribution shifts.

safetyarxiv-cs-lg

pyforce-1.0.0: Python Framework for data-driven model Order Reduction of multi-physiCs problEms

arXiv:2605.18082v1 Announce Type: new Abstract: pyforce is a Python package implementing Data-Driven Reduced Order Modelling techniques for applications to multi-physics problems, mainly set in the Nu

Applications

PyHealth 2.0: A Comprehensive Open-Source Toolkit for Accessible and Reproducible Clinical Deep Learning

arXiv:2601.16414v2 Announce Type: replace-cross Abstract: Difficulty replicating baselines, high computational costs, and required domain expertise create persistent barriers to clinical AI research.

applicationsarxiv-cs-ai

PySIFT: GPU-Resident Deterministic SIFT for Deep Learning Vision Pipelines

arXiv:2605.17869v1 Announce Type: new Abstract: A widespread assumption in local feature research holds that classical handcrafted descriptors are accuracy-limited relics best replaced by learned alte

local-aiarxiv-cs-cv

Q-LocalAdam: Memory-Efficient Client-Side Adaptive Optimization for Edge Federated Learning

arXiv:2605.17552v1 Announce Type: new Abstract: Federated learning on edge devices must cope with non-IID client data and tight memory budgets. Adaptive optimizers like Adam stabilize training under d

QLIF-CAST: Quantum Leaky-Integrate-and-Fire for Time-Series Weather Forecasting

model-releasesarxiv-cs-lg

arXiv:2605.18333v1 Announce Type: cross Abstract: Accurate and efficient time-series forecasting remains a challenging problem for both classical and quantum neural architectures, particularly in mult