Search: “models”

OPERA: A Reinforcement Learning--Enhanced Orchestrated Planner-Executor Architecture for Reasoning-Oriented Multi-Hop Retrieval

arXiv:2508.16438v4 Announce Type: replace-cross Abstract: Recent advances in large language models (LLMs) and dense retrievers have driven significant progress in retrieval-augmented generation (RAG).

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Research

Parameterized Hardness of Zonotope Containment and Neural Network Verification

DGX agent

arXiv:2509.22849v2 Announce Type: replace-cross Abstract: Neural networks with ReLU activations are a widely used model in machine learning. It is thus important to have a profound understanding of th

researcharxiv-cs-lg

19 May 2026

Patchwork: A compact representation for 3D polygonal shapes

arXiv:2605.16266v1 Announce Type: cross Abstract: We introduce Patchwork, a new general-purpose shape representation capable of modeling 2D and 3D geometry with a small number of parameters. Patchwork

Patients Speak, AI Listens: LLM-based Analysis of Online Reviews Uncovers Key Drivers for Urgent Care Satisfaction

arXiv:2503.20981v2 Announce Type: replace-cross Abstract: Investigating the public experience of urgent care facilities is essential for promoting community healthcare development. Traditional survey

PopPy: Opportunistically Exploiting Parallelism in Python Compound AI Applications

arXiv:2605.18697v1 Announce Type: cross Abstract: Compound AI applications, which compose calls to ML models using a general-purpose programming language like Python, are widely used for a variety of

PPAI: Enabling Personalized LLM Agent Interoperability for Collaborative Edge Intelligence

arXiv:2605.18067v1 Announce Type: new Abstract: Deploying large language model (LLM) on edge device enables personalized LLM agents for various users. The growing availability of diverse personalized

local-aiarxiv-cs-cl

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

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

local-aiarxiv-cs-lg

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

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

local-aiarxiv-cs-lg

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

Agents

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

Reasoning as Compression: Unifying Budget Forcing via the Conditional Information Bottleneck

arXiv:2603.08462v2 Announce Type: replace Abstract: ac{CoT} prompting improves LLM accuracy on complex tasks but often increases token usage and inference cost. Existing ``Budget Forcing'' methods red

Reasoning Before Diagnosis: Physician-Inspired Structured Thinking for ECG Classification

arXiv:2605.17308v1 Announce Type: new Abstract: Electrocardiogram (ECG) diagnosis in clinical practice relies on structured reasoning over multiple hierarchical aspects, including cardiac rhythm, cond

Representational Alignment with Chemical Induced Fit for Molecular Relational Learning

arXiv:2502.07027v2 Announce Type: replace-cross Abstract: Molecular Relational Learning (MRL) is widely applied in natural sciences to predict relationships between molecular pairs by extracting struc

Rethinking Generative Image Pretraining: How Far Are We From Scaling Up Next-Pixel Prediction?

arXiv:2511.08704v2 Announce Type: replace Abstract: This paper investigates the scaling properties of autoregressive next-pixel prediction, a simple, end-to-end yet under-explored framework for unifie

Scalable Uncertainty Reasoning in Knowledge Graphs

arXiv:2605.16568v1 Announce Type: new Abstract: Knowledge Graphs are pivotal for semantic data integration. The real-world data they model is often inherently uncertain. Within knowledge graphs, uncer

Scientific Logicality Enriched Methodology for LLM Reasoning: A Practice in Physics

arXiv:2605.17104v1 Announce Type: new Abstract: With the continuous advancement of reasoning abilities in Large Language Models (LLMs), their application to scientific reasoning tasks has gained signi

SD-Search: On-Policy Hindsight Self-Distillation for Search-Augmented Reasoning

arXiv:2605.18299v1 Announce Type: new Abstract: Search-augmented reasoning agents interleave internal reasoning with calls to an external retriever, and their performance relies on the quality of each

SegRAG: Training-Free Retrieval-Augmented Semantic Segmentation

arXiv:2605.17630v1 Announce Type: new Abstract: Here's a trimmed version under 1920 characters: Open-vocabulary segmentation models such as SAM3 achieve strong performance through concept-level text p

Semantics Disentanglement and Composition for Universal Image Coding with Efficiently LLM Reasoning and Generative Diffusion

arXiv:2412.18158v2 Announce Type: replace Abstract: Learned image compression methods have shown impressive performance but are often highly specialized for either human perception or specific machine

Sequential Structure in Intraday Futures Data: LSTM vs Gradient Boosting on MNQ

arXiv:2605.17724v1 Announce Type: cross Abstract: This paper compares gradient boosting and long short-term memory (LSTM) architectures for intraday directional prediction in Micro E-Mini Nasdaq 100 f

applicationsarxiv-cs-lg

Spatial Blindness in Whole-Slide Multiple Instance Learning

arXiv:2605.17449v1 Announce Type: cross Abstract: Whole-slide MIL models are often called context-aware once graphs, Transform ers, or state-space modules are placed above patch embeddings. We show th

Spiker-LL: An Energy-Efficient FPGA Accelerator Enabling Adaptive Local Learning in Spiking Neural Networks

arXiv:2605.18003v1 Announce Type: cross Abstract: Deploying adaptive intelligence at the edge remains challenging due to the high computational and energy cost of training neural models. Spiking Neura

local-aiarxiv-cs-ai

Stable and Near-Reversible Diffusion ODE Solvers for Image Editing

arXiv:2605.16399v1 Announce Type: new Abstract: The inversion of diffusion models plays a central role in image editing. Algebraically reversible ODE solvers provide an appealing approach to diffusion

safetyarxiv-cs-cv

Statistical Unlearning of Distributions: A Hypothesis Testing Approach

arXiv:2605.16645v1 Announce Type: cross Abstract: Machine learning systems increasingly face requirements to forget not only individual data points, but entire domains of information, such as toxic la

Stress-Testing Neural Network Verifiers with Provably Robust Instances

arXiv:2605.17153v1 Announce Type: new Abstract: Neural network verifiers aim to provide formal guarantees on model behavior, but existing verification benchmarks are fundamentally limited by their lac

Subject-Specific Analysis of Self-Initiated Attention Shifts from EEG with Controlled Internal and External Attention Conditions

arXiv:2605.18251v1 Announce Type: cross Abstract: Self-initiated attention shifts play a critical role in voluntary behavior but are difficult to study due to the absence of explicit temporal markers.

Sustainable Intelligence for the Wild: Democratizing Ecological Monitoring via Knowledge-Adaptive Edge Expert Agents

arXiv:2605.16671v1 Announce Type: new Abstract: Rapid biodiversity loss underscore the urgency of effective monitoring, yet manual surveys remain resource-intensive. While on-device AI offers a scalab

local-aiarxiv-cs-ai

Synthetic Aperture Radar Image Change Detection Based on Global Dynamic Context-Aware Network

arXiv:2605.16764v1 Announce Type: new Abstract: Convolutional neural networks (CNNs) have been extensively and successfully applied to the task of synthetic aperture radar (SAR) image change detection

local-aiarxiv-cs-cv

Systematic Evaluation of the Quality of Synthetic Clinical Notes Rephrased by LLMs at Million-Note Scale

arXiv:2605.17775v1 Announce Type: cross Abstract: Large language models (LLMs) can generate or synthesize clinical text for a wide range of applications, from improving clinical documentation to augme

TabKDE: Simple and Scalable Tabular Data Generation with Kernel Density Estimates

arXiv:2605.17642v1 Announce Type: new Abstract: Tabular data generation considers a large table with multiple columns -- each column comprised of numerical, categorical, or sometimes ordinal values. T

Hardware

TeleRAG: Efficient Retrieval-Augmented Generation Inference with Lookahead Retrieval

arXiv:2502.20969v4 Announce Type: replace-cross Abstract: Retrieval-augmented generation (RAG) extends large language models (LLMs) with external data sources to enhance factual correctness and domain

hardwarearxiv-cs-lg

Temporal Aware Pruning for Efficient Diffusion-based Video Generation

arXiv:2605.17837v1 Announce Type: cross Abstract: Video diffusion models have recently enabled high-quality video generation with ViT-based architectures, but remain computationally intensive because

The Bayesian Geometry of Transformer Attention

arXiv:2512.22471v5 Announce Type: replace-cross Abstract: Transformers often appear to perform Bayesian reasoning in context, but verifying this rigorously has been impossible: natural data lack analy

The Loupe: A Plug-and-Play Attention Module for Amplifying Discriminative Features in Vision Transformers

arXiv:2508.16663v2 Announce Type: replace-cross Abstract: Fine-Grained Visual Classification (FGVC) requires models to focus on subtle, task-relevant regions rather than broad object context. We prese

Agents

This release is absolutely stacked. I want to call out a sleeper feature: Interpreters Imagine your cloud agent gets a 10k-row CSV of suppor…

This release is absolutely stacked. I want to call out a sleeper feature: Interpreters Imagine your cloud agent gets a 10k-row CSV of support tickets. Without a code runtime, the model is stuck reason

agentsharrison-chase--x

To Trust or Not to Trust: Authors' Response to AI-based Reviews

arXiv:2605.16623v1 Announce Type: cross Abstract: Large language models are increasingly discussed and used as tools that may assist with scholarly peer review, but empirical evidence regarding how au

Hardware

Token-Space Mask Prediction for Efficient Vision Transformer Segmentation

arXiv:2605.18177v1 Announce Type: new Abstract: Query-based Vision Transformer segmentation models typically reconstruct dense spatial feature maps to predict masks, inheriting design patterns from co

hardwarearxiv-cs-cv

Toward Robust Multilingual Adaptation of LLMs for Low-Resource Languages

arXiv:2510.14466v3 Announce Type: replace-cross Abstract: Large language models (LLMs) continue to struggle with low-resource languages, primarily due to limited training data, translation noise, and

Transformers for Learning on Noisy and Task-Level Manifolds: Approximation and Generalization Insights

arXiv:2505.03205v3 Announce Type: replace Abstract: Transformers serve as the foundational architecture for large language and video generation models, such as GPT, BERT, SORA and their successors. Em

applicationsarxiv-cs-lg

UniversalRAG: Retrieval-Augmented Generation over Corpora of Diverse Modalities and Granularities

arXiv:2504.20734v4 Announce Type: replace-cross Abstract: Retrieval-Augmented Generation (RAG) has shown substantial promise in improving factual accuracy by grounding model responses with external kn

UST-Hand: An Uncertainty-aware Spatiotemporal Point Cloud Interaction Network for 3D Self-supervised Hand Pose Estimation

arXiv:2605.17742v1 Announce Type: new Abstract: Manually annotating accurate 3D hand poses is extremely time-consuming and labor-intensive. Existing self-supervised hand pose estimation methods levera

Visual Sculpting: Visually-Aligned Planning Representations for Long-Horizon Robot Clay Sculpting

arXiv:2605.17556v1 Announce Type: cross Abstract: Clay sculpting is a nuanced, artistic task involving dexterous manipulation with long-horizon planning to achieve high-level goals. As a robotics prob

VolTA-3D: Self-Supervised Learning for Brain MRI using 3D Volumetric Token Alignment

arXiv:2605.16775v1 Announce Type: cross Abstract: Self-supervised learning (SSL) has advanced medical image analysis be enabling learning form large unlabelled data. However, in brain magnetic resonan

WASIL: In-the-Wild Arabic Spoken Interactions with LLMs

arXiv:2605.16364v1 Announce Type: cross Abstract: Large Language Models (LLMs) voice assistants are commonly built as cascaded Automatic Speech recognition (ASR) to LLM systems, where recognition erro

Industry

`WE` understand why that gen-ai will always be derivative, because it learned from the most common things people do. Sadly, the slightly cliched writing tropes that most people do, are now being condemned because LLM's like GPT learned them.

This Reddit post discusses how large language models like GPT inevitably produce derivative content because they train on the most common patterns in human-generated text, including prevalent writing

industryr-chatgpt

Weighted Reverse Convolution for Feature Upsampling

arXiv:2605.17472v1 Announce Type: new Abstract: Pre-trained vision foundation models (VFMs) provide strong semantic representations, yet their patch-level features are inherently coarse, limiting thei