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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

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Human DGX agent

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

DGX agent

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

model-releasesarxiv-cs-ai

19 May 2026

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

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

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

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

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

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

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

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

QQJ: Quantifying Qualitative Judgment for Scalable and Human-Aligned Evaluation of Generative AI

arXiv:2605.17382v1 Announce Type: new Abstract: The rapid progress of generative artificial intelligence has exposed fundamental limitations in existing evaluation methodologies, particularly for open

safetyarxiv-cs-ai

QSTRBench: a New Benchmark to Evaluate the Ability of Language Models to Reason with Qualitative Spatial and Temporal Calculi

arXiv:2605.18380v1 Announce Type: new Abstract: We introduce an extensive qualitative spatial and temporal reasoning (QSTR) benchmark for evaluating large language models (LLMs). We pose questions con

Applications

QuadLink: Autoregressive Quad-Dominant Mesh Generation via Point-Relation Learning

arXiv:2605.16813v1 Announce Type: cross Abstract: The generation of production-ready quad-dominant meshes is a cornerstone of modern 3D content creation. Generating anisotropic quad-dominant meshes fr

applicationsarxiv-cs-cv

QuadraSHAP: Stable and Scalable Shapley Values for Product Games via Gauss-Legendre Quadrature

arXiv:2605.05870v2 Announce Type: replace Abstract: We study the efficient computation of Shapley values for product games -- cooperative games in which the coalition value factorizes as a product of

Quality-guided UAV Surface Exploration for 3D Reconstruction

arXiv:2511.20353v3 Announce Type: replace Abstract: Reasons for mapping an unknown environment with autonomous robots are wide-ranging, but in practice, they are often overlooked when developing plann

agentsarxiv-cs-ro

QuantFPFlow: Quantum Amplitude Estimation for Fokker--Planck Policy Optimisation in Continuous Reinforcement Learning

arXiv:2605.16429v1 Announce Type: cross Abstract: We introduce extbf{QuantFPFlow}, a reinforcement learning framework that integrates quantum amplitude estimation into the Fokker--Planck~(FP) formulat

safetyarxiv-cs-ai

Quantitative Linear Logic for Neuro-Symbolic Learning and Verification

arXiv:2605.13845v2 Announce Type: cross Abstract: Differentiable Logics are deployed in neuro-symbolic learning tasks as a way of embedding logical constraints in the training objective of neural netw

Quantum Sidecar Architectures for Hybrid AI Training and Inference: Stateful Protected Registers, Stateless Reset-and-Reprepare Circuits and Quantum Weight-State Outlook

arXiv:2605.18031v1 Announce Type: cross Abstract: We propose a quantum sidecar architecture family for future hybrid AI training and inference. The central idea is not to store an entire Transformer i

researcharxiv-cs-ai

Applications

QuChaTeR: A Hybrid Quantum-Chaotic Temporal Framework for Earthquake Prediction

arXiv:2605.16454v1 Announce Type: new Abstract: Seismic prediction remains challenging due to the highly nonlinear and chaotic dynamics of earthquake signals. While classical deep learning models such

applicationsarxiv-cs-lg

QuCo-RAG: Quantifying Uncertainty from the Pre-training Corpus for Dynamic Retrieval-Augmented Generation

arXiv:2512.19134v2 Announce Type: replace Abstract: Dynamic Retrieval-Augmented Generation adaptively determines when to retrieve during generation to mitigate hallucinations in large language models

Query-Aware Learnable Graph Pooling Tokens as Prompt for Large Language Models

arXiv:2501.17549v2 Announce Type: replace Abstract: Graph-structured data plays a vital role in numerous domains, such as social networks, citation networks, commonsense reasoning graphs and knowledge

Query-Conditioned Knowledge Alignment for Reliable Cross-System Medical Reasoning

arXiv:2605.18570v1 Announce Type: new Abstract: Cross-domain knowledge alignment is essential for integrating heterogeneous medical systems, yet existing approaches typically treat entity alignment as

safetyarxiv-cs-ai

Queue Length Regret Bounds for Contextual Queueing Bandits

arXiv:2601.19300v2 Announce Type: replace Abstract: We introduce contextual queueing bandits, a new context-aware framework for scheduling while simultaneously learning unknown service rates. Individu

Qumus: Realization of An Embodied AI Quantum Material Experimentalist

arXiv:2605.18407v1 Announce Type: cross Abstract: While modern Large Language Models (LLMs) and agentic artificial intelligence (AI) have demonstrated transformative capabilities in digital domains, t

agentsarxiv-cs-ai

R2V Agent: Teaching SLMs When to Ask for Help

arXiv:2605.16604v1 Announce Type: new Abstract: Efficient agentic systems should incur expensive frontier-model costs only on decisions where a cheaper local model is likely to fail. Existing LLM casc

Tutorials

RaBiT: Residual-Aware Binarization Training for Accurate and Efficient LLMs

arXiv:2602.05367v2 Announce Type: replace Abstract: Efficient deployment of large language models (LLMs) requires extreme quantization, forcing a critical trade-off between low-bit efficiency and perf

tutorialsarxiv-cs-ai

Rad-VLSM: A Cross-Modal Framework with Semantics-Assisted Prompting for Medical Segmentation and Diagnosis

arXiv:2605.18130v1 Announce Type: new Abstract: Medical image segmentation is more clinically valuable when it supports diagnosis rather than merely producing lesion masks. However, diagnostically rel

safetyarxiv-cs-cv

RadGame: An AI-Powered Platform for Radiology Education

arXiv:2509.13270v2 Announce Type: replace-cross Abstract: We introduce RadGame, an AI-powered gamified platform for radiology education that targets two core skills: localizing findings and generating

local-aiarxiv-cs-ai

RadGenome-Anatomy: A Large-Scale Anatomy-Labeled Chest Radiograph Dataset via Physically Grounded Volumetric Projection

arXiv:2605.17368v1 Announce Type: new Abstract: Anatomical structure labels for chest radiographs are essential for medical image segmentation and a broad range of downstream diagnostic tasks. However

researcharxiv-cs-cv

Radial-Angular Geometry for Reliable Update Diagnosis in Noisy-Label Learning

model-releasesarxiv-cs-cv

arXiv:2605.17429v1 Announce Type: cross Abstract: Noisy-label methods often estimate sample reliability from forward-space signals such as loss, confidence, or entropy. These signals indicate whether

Tutorials

RadJEPA: Radiology Encoder for Chest X-Rays via Joint Embedding Predictive Architecture

arXiv:2601.15891v2 Announce Type: replace Abstract: Recent advances in medical vision language models guide the learning of visual representations; however, this form of supervision is constrained by

tutorialsarxiv-cs-cv

RAG-based EEG-to-Text Translation Using Deep Learning and LLMs

arXiv:2605.17503v1 Announce Type: new Abstract: The decoding of linguistic information from electroencephalography (EEG) signals remains an extremely challenging problem in brain-computer interface (B

researcharxiv-cs-ai