All entries

Progressive Photorealistic Simplification

arXiv:2605.10409v1 Announce Type: new Abstract: Existing image simplification techniques often rely on Non-Photorealistic Rendering (NPR), transforming photographs into stylized sketches, cartoons, or

Projection-Free Functional Constrained Optimization for Risk Aversion and Sparsity Control

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DGX agent

arXiv:2210.05108v2 Announce Type: replace-cross Abstract: We study projection-free methods for functional constrained optimization with convex or smooth nonconvex objectives. Such problems arise in ap

researcharxiv-cs-lg

12 May 2026

Industry

Prompt - 'photo is from year [Write a random year here] ], it was called 'a few seconds before happiness' ......... wtf I used the year 1822 , what was going on then?

A Reddit discussion from r/ChatGPT where a user shared a prompt experiment asking an AI to generate a photo from a random year with the title 'a few seconds before happiness,' and then questioned what

industryr-chatgpt

Prompt-Activation Duality: Improving Activation Steering via Attention-Level Interventions

model-releasesarxiv-cs-ai

arXiv:2605.10664v1 Announce Type: cross Abstract: Activation steering controls language model behavior by adding directions to internal representations at inference time, but standard residual-stream

Prompt Estimation from Prototypes for Federated Prompt Tuning of Vision Transformers

arXiv:2510.25372v2 Announce Type: replace Abstract: Visual Prompt Tuning (VPT) of pre-trained Vision Transformers (ViTs) has proven highly effective as a parameter-efficient fine-tuning technique for

PromptDx: Differentiable Prompt Tuning for Multimodal In-Context Alzheimer's Diagnosis

arXiv:2605.08585v1 Announce Type: cross Abstract: Deep learning models in medical imaging typically operate as parametric memory, diagnosing patients by recalling fixed knowledge learned during traini

PromptGuard: Soft Prompt-Guided Unsafe Content Moderation for Text-to-Image Models

arXiv:2501.03544v5 Announce Type: replace-cross Abstract: Recent text-to-image (T2I) models have exhibited remarkable performance in generating high-quality images from text descriptions. However, the

Prophecy: Inferring Formal Properties from Neuron Activations

arXiv:2509.21677v2 Announce Type: replace Abstract: We present Prophecy, a tool for automatically inferring formal properties of feed-forward neural networks. Prophecy is based on the observation that

Applications

Prospective Compression in Human Abstraction Learning

arXiv:2605.09985v1 Announce Type: new Abstract: A core challenge in program synthesis is online library learning: the incremental acquisition of reusable abstractions under uncertainty about future ta

applicationsarxiv-cs-ai

ProteinOPD: Towards Effective and Efficient Preference Alignment for Protein Design

arXiv:2605.10189v1 Announce Type: cross Abstract: Designing proteins with desired functions or properties represents a core goal in synthetic biology and drug discovery. Recent advances in protein lan

Provable Anytime Ensemble Sampling Algorithms in Nonlinear Contextual Bandits

arXiv:2510.10730v2 Announce Type: replace-cross Abstract: We provide a unified algorithmic framework for ensemble sampling in nonlinear contextual bandits and develop corresponding regret bounds for t

Provable Sparse Inversion and Token Relabel Enhanced One-shot Federated Learning with ViTs

arXiv:2605.10748v1 Announce Type: cross Abstract: One-Shot Federated Learning, where a central server learns a global model in a single communication round, has emerged as a promising paradigm. Howeve

Tutorials

PruneTIR: Inference-Time Tool Call Pruning for Effective yet Efficient Tool-Integrated Reasoning

arXiv:2605.09931v1 Announce Type: cross Abstract: Tool-integrated reasoning (TIR) enables large language models (LLMs) to enhance their capabilities by interacting with external tools, such as code in

tutorialsarxiv-cs-ai

Pseudo-Deliberation in Language Models: When Reasoning Fails to Align Values and Actions

arXiv:2605.09893v1 Announce Type: cross Abstract: Large language models (LLMs) are often evaluated based on their stated values, yet these do not reliably translate into their actions, a discrepancy t

PumpSense: Real-Time Detection and Target Extraction of Crypto Pump-and-Dumps on Telegram

arXiv:2605.09431v1 Announce Type: new Abstract: Cryptocurrency pump-and-dump schemes coordinated via Telegram threaten market integrity. However, existing research addressing this specific threat has

Local Ai

PYTHALAB-MERA: Validation-Grounded Memory, Retrieval, and Acceptance Control for Frozen-LLM Coding Agents

arXiv:2605.08468v1 Announce Type: cross Abstract: Local LLM-based coding agents increasingly work in settings where correctness is earned through execution feedback, persistent state, and bounded repa

local-aiarxiv-cs-ai

Q-learning with Adjoint Matching

arXiv:2601.14234v3 Announce Type: replace-cross Abstract: We propose Q-learning with Adjoint Matching (QAM), a novel TD-based reinforcement learning (RL) algorithm that tackles a long-standing challen

QM-ToT: A Medical Tree of Thoughts Reasoning Framework for Quantized Model

arXiv:2504.12334v2 Announce Type: replace Abstract: Large language models (LLMs) face significant challenges in specialized biomedical tasks due to the inherent complexity of medical reasoning and the

QT-Net: Rethinking Evaluation of AI Models in Atomic Chemical Space

arXiv:2605.10458v1 Announce Type: new Abstract: Atomic properties such as partial charges or multipoles encode chemically meaningful information that can inform downstream molecular property predictio

Hardware

Qualcomm closed down 11.46% on Tuesday as chip stocks pull back from record AI-driven rally; Intel closed down 6.82%, Sandisk dropped 6%, and Micron 3.61% (Samantha Subin/CNBC)

Samantha Subin / CNBC: Qualcomm closed down 11.46% on Tuesday as chip stocks pull back from record AI-driven rally; Intel closed down 6.82%, Sandisk dropped 6%, and Micron 3.61% — Chip stocks dropped

hardwaretechmeme

Quantifying Concentration Phenomena of Mean-Field Transformers in the Low-Temperature Regime

arXiv:2605.10931v1 Announce Type: cross Abstract: Transformers with self-attention modules as their core components have become an integral architecture in modern large language and foundation models.

Quantifying the Utility of User Simulators for Building Collaborative LLM Assistants

arXiv:2605.09808v1 Announce Type: new Abstract: User simulators are increasingly leveraged to build interactive AI assistants, yet how to measure the quality of these simulators remains an open questi

Quantile-Coupled Flow Matching for Distributional Reinforcement Learning

arXiv:2605.08515v1 Announce Type: new Abstract: Unlike standard expected-return Reinforcement Learning (RL), Distributional RL (DRL) models the full return distribution, making it better-suited for un

safetyarxiv-cs-lg

Tutorials

Quantile Geometry Regularization for Distributional Reinforcement Learning

arXiv:2605.08182v1 Announce Type: cross Abstract: Quantile-based distributional reinforcement learning methods learn return distributions through sampled quantile regression, but their bootstrapped ta

tutorialsarxiv-cs-ai

Quantitative Clustering in Mean-Field Transformer Models

arXiv:2504.14697v3 Announce Type: replace Abstract: The evolution of tokens through deep transformer models can be modeled as an interacting particle system that has been shown to exhibit an asymptoti

Quantitative Error Feedback for Quantization Noise Reduction of Filtering over Graphs

arXiv:2506.01404v2 Announce Type: replace Abstract: This paper introduces an innovative error feedback framework designed to mitigate quantization noise in distributed graph filtering, where communica

Quantitative Local Convergence of Mean-Field Stein Variational Gradient Flow

arXiv:2605.09456v1 Announce Type: cross Abstract: Stein Variational Gradient Descent (SVGD) is a deterministic interacting-particle method for sampling from a target probability measure given access t

Quantitative Sobolev Approximation Bounds for Neural Operators with Empirical Validation on Burgers Equation

arXiv:2605.08170v1 Announce Type: new Abstract: Neural operators have emerged as a powerful tool for learning mappings between infinite-dimensional function spaces. However, their approximation proper

Quantum Circuit Simulation of Compartmental Drug Dynamics: Leveraging Variational Algorithms for Nonlinear Mixed-Effects Population Pharmacokinetics

arXiv:2605.09691v1 Announce Type: new Abstract: Population pharmacokinetic/pharmacodynamic (PK/PD) modeling traditionally relies on classical ordinary differential equations to simulate drug dynamics.

Quantum Transfer Learning Shows Improved Robustness in Low-Data Regimes

arXiv:2605.09118v1 Announce Type: cross Abstract: Transfer learning under limited data is a challenging setting, where models must adapt to new tasks with minimal supervision. Prior work has primarily

Quasi-Linear ICA for Motor Unit Decomposition during Dynamic Contractions

arXiv:2406.19581v2 Announce Type: replace-cross Abstract: Decomposing surface electromyography (EMG) into the spike trains of individual motor neurons is a long-standing inverse problem and a key step

Queryable LoRA: Instruction-Regularized Routing Over Shared Low-Rank Update Atoms

arXiv:2605.08423v1 Announce Type: cross Abstract: We present a data-adaptive method for parameter-efficient fine-tuning of large neural networks. Standard low-rank adaptation methods improve efficienc

QueST: Persistent Queries as Semantic Monitors for Drift Suppression in Long-Horizon Tracking

arXiv:2605.09513v1 Announce Type: new Abstract: Tracking points in videos is typically formulated as frame-to-frame correspondence, where each point is matched locally to the next frame. While this wo

Tools

Quoting Mitchell Hashimoto

The thing about 90% of TDMs [Technical Decision Makers] is that they're motivated primarily by NOT GETTING FIRED. These aren't people who browser Lobsters or push to GH on the weekend. These are peopl

toolssimon-willison

Tutorials

Quoting Mo Bitar

Now, if your CEO has never heard the phrase Ralph Loop, oh man, you are less than 30 days away from your next promotion. I'm not even exaggerating. Walk into his office, close the door, and say, hey c

tutorialssimon-willison

Qwen Goes Brrr: Off-the-Shelf RAG for Ukrainian Multi-Domain Document Understanding

model-releasesarxiv-cs-ai

arXiv:2605.10296v1 Announce Type: cross Abstract: We participated in the Fifth UNLP shared task on multi-domain document understanding, where systems must answer Ukrainian multiple-choice questions fr

Qwen-Image-2.0 Technical Report

arXiv:2605.10730v1 Announce Type: new Abstract: We present Qwen-Image-2.0, an omni-capable image generation foundation model that unifies high-fidelity generation and precise image editing within a si

R4Det: 4D Radar-Camera Fusion for High-Performance 3D Object Detection

arXiv:2603.11566v2 Announce Type: replace Abstract: 4D radar-camera sensing configuration has gained increasing importance in autonomous driving. However, existing 3D object detection methods that fus

Radar-Guided Polynomial Fitting for Metric Depth Estimation

arXiv:2503.17182v4 Announce Type: replace Abstract: We propose POLAR, a novel radar-guided depth estimation method that introduces polynomial fitting to efficiently transform scaleless depth predictio

Agents

RADAR: Redundancy-Aware Diffusion for Multi-Agent Communication Structure Generation

arXiv:2605.09907v1 Announce Type: new Abstract: Compared with individual agents, large language model based multi-agent systems have shown great capabilities consistently across diverse tasks, includi

agentsarxiv-cs-ai

RadThinking: A Dataset for Longitudinal Clinical Reasoning in Radiology

arXiv:2605.10761v1 Announce Type: new Abstract: Cancer screening is a reasoning task. A radiologist observes findings, compares them to prior scans, integrates clinical context, and reaches a diagnost

Applications

RAG-HAR: Retrieval Augmented Generation-based Human Activity Recognition

arXiv:2512.08984v2 Announce Type: replace-cross Abstract: Human Activity Recognition (HAR) underpins applications in healthcare, rehabilitation, fitness tracking, and smart environments, yet existing

applicationsarxiv-cs-ai

Agents

ran openai's 663 open roles through chatgpt to ask what one could infer about their strategy nothing particularly new, but it worked well

Yohei Nakajima analyzed OpenAI's 663 open job positions using ChatGPT to identify patterns and infer insights about the company's strategic direction and priorities. While the analysis didn't reveal g

agentsyohei-nakajima--x

Randomized PCA Forest for Unsupervised Outlier Detection

arXiv:2508.12776v3 Announce Type: replace-cross Abstract: We propose a novel unsupervised outlier detection method based on Randomized Principal Component Analysis (PCA). Motivated by the performance

Rapid Forest Fuel Load Estimation via Virtual Remote Sensing and Metric-Scale Feed-Forward 3D Reconstruction

arXiv:2605.10789v1 Announce Type: new Abstract: Accurate quantification of forest coverage and combustible biomass (fuel load) is critical for wildfire risk assessment and ecosystem management. Howeve

RareCP: Regime-Aware Retrieval for Efficient Conformal Prediction

arXiv:2605.08857v1 Announce Type: new Abstract: Recent advances in uncertainty quantification for time series forecasting show that conformal prediction can provide reliable prediction intervals, yet

Raster2Seq: Polygon Sequence Generation for Floorplan Reconstruction

arXiv:2602.09016v2 Announce Type: replace Abstract: Reconstructing a structured vector-graphics representation from a rasterized floorplan image is typically an important prerequisite for computationa

Local Ai

RAwR: Role-Aware Rewiring via Approximate Equitable Partition

arXiv:2605.09457v1 Announce Type: cross Abstract: While Graph Neural Networks (GNNs) have demonstrated significant efficacy in node classification tasks, where predictions rely on local neighborhood i

local-aiarxiv-cs-ai