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PPLLaVA: Varied Video Sequence Understanding With Prompt Guidance

arXiv:2411.02327v4 Announce Type: replace Abstract: In the past year, video-based large language models (Video LLMs) have achieved impressive progress, particularly in their ability to process long vi

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Prediction of Alzheimer's Disease Risk Factors from Retinal Images via Deep Learning: Development and Validation of Biologically Relevant Morphological Associations in the UK Biobank

DGX agent

arXiv:2605.00665v1 Announce Type: new Abstract: The systemic, metabolic, lifestyle factors have established associations with Alzheimer's Disease (AD) through epidemiologic and AD-specific biomarker s

applicationsarxiv-cs-cv

4 May 2026

Tutorials

Predictive Spatio-Temporal Scene Graphs for Semi-Static Scenes

arXiv:2605.00121v1 Announce Type: new Abstract: We have seen tremendous recent progress in our ability to build 'spatio-semantic' representations that enable robots to perform complex reasoning across

tutorialsarxiv-cs-ro

Local Ai

Prefer-DAS: Learning from Local Preferences and Sparse Prompts for Domain Adaptive Segmentation of Electron Microscopy

arXiv:2602.19423v3 Announce Type: replace Abstract: Domain adaptive segmentation (DAS) is a promising paradigm for delineating intracellular structures from various large-scale electron microscopy (EM

local-aiarxiv-cs-cv

Preference Goal Tuning: Post-Training as Latent Control for Frozen Policies

arXiv:2412.02125v2 Announce Type: replace-cross Abstract: Goal-conditioned policies enable decision-making models to execute diverse behaviors based on specified goals, yet their downstream performanc

PrefMoE: Robust Preference Modeling with Mixture-of-Experts Reward Learning

arXiv:2605.00384v1 Announce Type: new Abstract: Preference-based reinforcement learning offers a scalable alternative to manual reward engineering by learning reward structures from comparative feedba

safetyarxiv-cs-ro

Privacy Amplification in Differentially Private Zeroth-Order Optimization with Hidden States

arXiv:2506.00158v2 Announce Type: replace Abstract: Zeroth-order optimization has emerged as a promising approach for fine-tuning large language models under differential privacy (DP) and memory const

Probing Multimodal Large Language Models on Cognitive Biases in Chinese Short-Video Misinformation

arXiv:2601.06600v2 Announce Type: replace Abstract: Short-video platforms have become major channels for misinformation, where deceptive claims frequently leverage visual experiments and social cues.

Prompt-Induced Score Variance in Zero-Shot Binary Vision-Language Safety Classification

arXiv:2605.00326v1 Announce Type: new Abstract: Single-prompt first-token probabilities from zero-shot vision-language model (VLM) safety classifiers are treated as decision scores, but we show they a

Provable and scalable quantum Gaussian processes for quantum learning

arXiv:2605.00099v1 Announce Type: cross Abstract: Despite rapid recent advances in quantum machine learning, the field is in many ways stuck. Existing approaches can exhibit serious limitations, and w

Putting HUMANS first: Efficient LAM Evaluation with Human Preference Alignment

arXiv:2605.00022v1 Announce Type: new Abstract: The rapid proliferation of large audio models (LAMs) demands efficient approaches for model comparison, yet comprehensive benchmarks are costly. To fill

Quantum Gradient-Based Approach for Edge and Corner Detection Using Sobel Kernels

arXiv:2605.00744v1 Announce Type: new Abstract: Edge detection refers to identifying points in a digital image where intensity changes sharply, indicating object boundaries or structural features. Cor

researcharxiv-cs-cv

Quantum Interval Bound Propagation for Certified Training of Quantum Neural Networks

arXiv:2605.00747v1 Announce Type: cross Abstract: Quantum machine learning is a promising field for efficiently learning features of a dataset to perform a specified task, such as classification. Inte

Local Ai

RadLite: Multi-Task LoRA Fine-Tuning of Small Language Models for CPU-Deployable Radiology AI

arXiv:2605.00421v1 Announce Type: new Abstract: Large language models (LLMs) show promise in radiology but their deployment is limited by computational requirements that preclude use in resource-const

local-aiarxiv-cs-cl

Randomized Subspace Nesterov Accelerated Gradient

arXiv:2605.00740v1 Announce Type: cross Abstract: Randomized-subspace methods reduce the cost of first-order optimization by using only low-dimensional projected-gradient information, a feature that i

RAT+: Train Dense, Infer Sparse -- Recurrence Augmented Attention for Dilated Inference

arXiv:2602.18196v3 Announce Type: replace Abstract: Structured dilated attention has an appealing inference-time efficiency knob: it reduces the FLOPs of attention and the KV cache size by a factor of

Real-Time Frame- and Event-based Object Detection with Spiking Neural Networks on Edge Neuromorphic Hardware: Design, Deployment and Benchmark

model-releasesarxiv-cs-cv

arXiv:2605.00146v1 Announce Type: new Abstract: Real-time object detection on energy-constrained platforms is critical for applications such as UAV-based inspection, autonomous navigation, and mobile

REALM: An RGB and Event Aligned Latent Manifold for Cross-Modal Perception

arXiv:2605.00271v1 Announce Type: new Abstract: Event cameras provide several unique advantages over standard frame-based sensors, including high temporal resolution, low latency, and robustness to ex

researcharxiv-cs-cv

Reasoning-Intensive Regression

arXiv:2508.21762v3 Announce Type: replace Abstract: AI researchers and practitioners increasingly apply large language models (LLMs) to what we call reasoning-intensive regression (RiR), i.e., deducin

Recovering Hidden Reward in Diffusion-Based Policies

arXiv:2605.00623v1 Announce Type: new Abstract: This paper introduces EnergyFlow, a framework that unifies generative action modeling with inverse reinforcement learning by parameterizing a scalar ene

safetyarxiv-cs-ro

Reinforcement Learning for LLM Post-Training: A Survey

arXiv:2407.16216v3 Announce Type: replace Abstract: Large language models (LLMs) trained via pretraining and supervised fine-tuning (SFT) can still produce harmful and misaligned outputs, or struggle

Reinforcement Learning with LLM-Guided Action Spaces for Synthesizable Lead Optimization

arXiv:2604.07669v2 Announce Type: replace Abstract: Lead optimization in drug discovery requires improving therapeutic properties while ensuring that molecular modifications correspond to feasible syn

Reinforcement Learning with Markov Risk Measures and Multipattern Risk Approximation

arXiv:2605.00654v1 Announce Type: new Abstract: For a risk-averse finite-horizon Markov Decision Problem, we introduce a special class of Markov coherent risk measures, called mini-batch measures. We

ReLay: Personalized LLM-Generated Plain-Language Summaries for Better Understanding, but at What Cost?

arXiv:2605.00468v1 Announce Type: new Abstract: Plain Language Summaries (PLS) aim to make research accessible to lay readers, but they are typically written in a one-size-fits-all style that ignores

Remote SAMsing: From Segment Anything to Segment Everything

model-releasesarxiv-cs-cv

arXiv:2605.00256v1 Announce Type: new Abstract: SAM2 produces high-quality zero-shot segmentation on natural images, but applying it to large remote sensing scenes exposes two problems: (1) its mask g

Representation in large language models

arXiv:2501.00885v2 Announce Type: replace Abstract: The extraordinary success of recent Large Language Models (LLMs) on a diverse array of tasks has led to an explosion of scientific and philosophical

ResRL: Boosting LLM Reasoning via Negative Sample Projection Residual Reinforcement Learning

arXiv:2605.00380v1 Announce Type: cross Abstract: Reinforcement Learning with Verifiable Rewards (RLVR) enhances reasoning of Large Language Models (LLMs) but usually exhibits limited generation diver

Resting Neurons, Active Insights: Robustify Activation Sparsity for Large Language Models

arXiv:2512.12744v3 Announce Type: replace Abstract: Activation sparsity offers a compelling route to accelerate large language model (LLM) inference by selectively suppressing hidden activations, yet

Rethinking LLM Ensembling from the Perspective of Mixture Models

arXiv:2605.00419v1 Announce Type: cross Abstract: Model ensembling is a well-established technique for improving the performance of machine learning models. Conventionally, this involves averaging the

RETO: A Rotary-Enhanced Transformer Operator for High-Fidelity Prediction of Automotive Aerodynamics

arXiv:2605.00062v1 Announce Type: cross Abstract: Rapid aerodynamic evaluation is crucial for modern vehicle design, yet existing neural operators struggle to capture intricate spatial correlations. W

Retrieval-Augmented Reasoning for Chartered Accountancy

arXiv:2605.00257v1 Announce Type: new Abstract: The inception of Large Language Models (LLMs) has catalyzed AI adoption in the finance sector, yet their reliability in complex, jurisdiction-specific t

Revealing graph bandits for maximizing local influence

arXiv:2605.00489v1 Announce Type: new Abstract: We study a graph bandit setting where the objective of the learner is to detect the most influential node of a graph by requesting as little information

Reward Modeling from Natural Language Human Feedback

arXiv:2601.07349v3 Announce Type: replace Abstract: Reinforcement Learning with Verifiable reward (RLVR) on preference data has become the mainstream approach for training Generative Reward Models (GR

Riemannian MeanFlow

arXiv:2602.07744v3 Announce Type: replace Abstract: Diffusion and flow models have become the dominant paradigm for generative modeling on Riemannian manifolds, with successful applications in protein

Applications

Robust Fusion of Object-Level V2X for Learned 3D Object Detection

arXiv:2605.00595v1 Announce Type: new Abstract: Perception for automated driving is largely based on onboard environmental sensors, such as cameras and radar, which are cost-effective but limited by l

applicationsarxiv-cs-cv

ROSA: Robust and Energy-Efficient Microring-Based Optical Neural Networks via Optical Shift-and-Add and Layer-Wise Hybrid Mapping

arXiv:2605.00032v1 Announce Type: cross Abstract: This work presents ROSA, a microring-based optical neural network architecture that improves robustness and energy efficiency using an optical shift-a

RouteProfile: Elucidating the Design Space of LLM Profiles for Routing

arXiv:2605.00180v1 Announce Type: cross Abstract: As the large language model (LLM) ecosystem expands, individual models exhibit varying capabilities across queries, benchmarks, and domains, motivatin

RSAT: Structured Attribution Makes Small Language Models Faithful Table Reasoners

arXiv:2605.00199v1 Announce Type: new Abstract: When a language model answers a table question, users have no way to verify which cells informed which reasoning steps. We introduce RSAT, a method that

RTPrune: Reading-Twice Inspired Token Pruning for Efficient DeepSeek-OCR Inference

model-releasesarxiv-cs-cv

arXiv:2605.00392v1 Announce Type: new Abstract: DeepSeek-OCR leverages visual-text compression to reduce long-text processing costs and accelerate inference, yet visual tokens remain prone to redundan

Agents

RunAgent: Interpreting Natural-Language Plans with Constraint-Guided Execution

arXiv:2605.00798v1 Announce Type: cross Abstract: Humans solve problems by executing targeted plans, yet large language models (LLMs) remain unreliable for structured workflow execution. We propose Ru

agentsarxiv-cs-cl

SAGA: Workflow-Atomic Scheduling for AI Agent Inference on GPU Clusters

arXiv:2605.00528v1 Announce Type: cross Abstract: AI agents execute tens to hundreds of chained LLM calls per task, yet GPU schedulers treat each call as independent, discarding gigabytes of intermedi

SAVGO: Learning State-Action Value Geometry with Cosine Similarity for Continuous Control

arXiv:2605.00787v1 Announce Type: new Abstract: While representation and similarity learning have improved the sample efficiency of Reinforcement Learning (RL), they are rarely used to shape policy up

SC-Taxo: Hierarchical Taxonomy Generation under Semantic Consistency Constraints using Large Language Models

arXiv:2605.00620v1 Announce Type: new Abstract: Scientific literature is expanding at an unprecedented pace, making it increasingly challenging to efficiently organize and access domain knowledge. A h

Scalable Context-Aware Graph Attention for Unsupervised Anomaly Detection in Large-Scale Mobile Networks

arXiv:2605.00482v1 Announce Type: new Abstract: Mobile network operators must monitor thousands of heterogeneous network elements across the radio access network and the packet core, each exposing hig

Scalable Learning in Structured Recurrent Spiking Neural Networks without Backpropagation

arXiv:2605.00402v1 Announce Type: cross Abstract: Spiking Neural Networks (SNNs) provide a promising framework for energy-efficient and biologically grounded computation; however, scalable learning in

Scale-Aware Adversarial Analysis: A Diagnostic for Generative AI in Multiscale Complex Systems

arXiv:2605.00510v1 Announce Type: cross Abstract: Complex physical systems, from supersonic turbulence to the macroscopic structure of the universe, are governed by continuous multiscale dynamics. Whi

researcharxiv-cs-cv

Applications

Scaling Federated Linear Contextual Bandits via Sketching

arXiv:2605.00500v1 Announce Type: new Abstract: In federated contextual linear bandits, high data dimensionality incurs prohibitive computation and communication costs: local agents perform O(d^3)-tim

applicationsarxiv-cs-lg

Scaling Reasoning Hop Exposes Weaknesses: Demystifying and Improving Hop Generalization in Large Language Models

arXiv:2601.21214v2 Announce Type: replace Abstract: Chain-of-thought (CoT) reasoning has become the standard paradigm for enabling Large Language Models (LLMs) to solve complex problems. However, rece