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arXiv:2610.18389v1 Announce Type: new Abstract: Large language models are increasingly used as judges to reduce reliance on costly human evaluation, yet their scores can systematically disagree with h

local-aiarxiv-cs-lg

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PRISM: Precision Forcing for Generalist Vision-Language-Action Policies

DGX agent

arXiv:2610.14704v1 Announce Type: new Abstract: Coupling a pretrained vision-language model (VLM) with a diffusion or flow-matching action expert has become the mainstream vision-language-action (VLA)

safetyarxiv-cs-ro

14 Oct 2026

Pristine Models

arXiv:2610.14091v1 Announce Type: new Abstract: Training on broad, unfiltered data enables high-quality image generation, but can also teach models concepts that should not be generated; restricting t

safetyarxiv-cs-cv

Privacy-Preserving GNN Inference on Financial Transaction Graphs via Homomorphic Encryption

arXiv:2610.18054v1 Announce Type: cross Abstract: Anti-money laundering (AML) in financial networks requires modeling transaction patterns that span multiple institutions. In practice, however, banks

Tutorials

Pro-t-VAE: Closed-Form Student's t Mixtures for Robust Prototype Discovery

arXiv:2610.14894v1 Announce Type: new Abstract: We present a robust deep generative framework for unsupervised image prototype discovery, built on a heavy-tailed Student's t mixture prior, that bridge

tutorialsarxiv-cs-lg

Proactive Physical AI through Continuous Observation and Timely Intervention

arXiv:2610.16967v1 Announce Type: new Abstract: Physical AI is commonly formulated around an explicitly specified task: given a goal or instruction, a system perceives, reasons, and acts. In many real

applicationsarxiv-cs-cv

Agents

ProAS: A Progress-Aware Advantage Shaping Framework for Multi-Turn Agentic Reinforcement Learning

arXiv:2610.18683v1 Announce Type: new Abstract: Reinforcement learning (RL) has emerged as the primary paradigm for optimizing Large Language Model (LLM) agents, yet efficient critic-free algorithms (

agentsarxiv-cs-lg

Probabilistic Forecasting of Wildfire Size Using Conditional Flow Matching

arXiv:2610.15983v1 Announce Type: new Abstract: In this work we present a probabilistic model for predicting the temporal evolution of wildfire size using conditional generative algorithms, namely flo

Probabilistic Multi-Source Data Selection via Learnable Categorical Priors: Variance Reduction and Guaranteed Derandomization

arXiv:2610.14942v1 Announce Type: new Abstract: Bilevel data selection is a fundamental task with numerous applications in machine learning and statistics. A common strategy for tackling this problem

Probabilistic Simulation of Gas-Liquid Interfaces with Coupled Flow Matching on Spherical Harmonics

arXiv:2610.16327v1 Announce Type: new Abstract: Bubbly flows involve strongly coupled bubble motion, deformation, and hydrodynamic interactions, making high-fidelity simulation computationally expensi

ProbabilityBench: Model Evaluation and Calibration with Exact Ground-Truth Uncertainty

arXiv:2610.17016v1 Announce Type: new Abstract: Evaluating model uncertainty on real-world data is challenging because each instance provides only a single observed label, leaving true conditional cla

Probe and Skip: Self-Predictive Token Skipping for Efficient Long-Context LLM Inference

arXiv:2601.13155v3 Announce Type: replace-cross Abstract: Long-context inference enhances the reasoning capability of Large Language Models (LLMs), but incurs significant computational overhead. Token

Probing and Inducing Clinical Modality Grounding in Brain Tumor Segmentation

arXiv:2603.19844v3 Announce Type: replace Abstract: Multimodal MRI provides complementary information for brain tumor segmentation, but high accuracy alone does not reveal whether a model relies on cl

Probing the Geometry of Color Representations in Multilingual Language Models

arXiv:2610.18331v1 Announce Type: cross Abstract: Do multilingual language models learn a universal internal geometry for color, and is it perceptually grounded or primarily linguistic? We decompose c

Process Rewards for Outcome-Guided Reasoning Steps in LLM Reinforcement Learning

arXiv:2604.02341v2 Announce Type: replace Abstract: Mathematical reasoning in large language models has improved substantially with reinforcement learning using verifiable rewards, where final answers

ProGAN-Based Synthetic Image Generation for Industrial Visual Inspection of Metallic Parts

arXiv:2610.16287v1 Announce Type: new Abstract: Automated visual inspection is essential for efficient repair and recycling of metallic components. AI-based defect detection is a strong fit. However,

ProML: Progressive Multi-label Learning

arXiv:2610.15614v1 Announce Type: new Abstract: Multi-label learning typically pursues model construction through the joint optimization of label fitting, correlation modeling, and structural regulari

ProMoDD: Dataset Distillation for 3D Human Motion via Prototype-Conditioned Diffusion Models

arXiv:2610.16134v1 Announce Type: new Abstract: Dataset distillation (DD) aims to retain the training utility of a large dataset in a much smaller set of examples. For skeleton-based action recognitio

Prompt Interference in LLM Post-Training: Why Pass@k Optimization Can Degrade Pass@1

arXiv:2602.21189v3 Announce Type: replace Abstract: Pass@k is a widely used performance metric for verifiable large language model tasks, including mathematical reasoning, code generation, and short-a

Agents

Prorsa: Unleashing the Potential of Prefill-Decode Disaggregation in Agentic Reinforcement Learning

arXiv:2610.16592v1 Announce Type: cross Abstract: Agentic reinforcement learning (RL) is commonly used to enhance the capabilities of large language models (LLMs) in solving complex multi-turn tasks.

agentsarxiv-cs-lg

Prospective Forcing: Learning to Look Ahead for Autoregressive Long Video Diffusion

arXiv:2610.14654v1 Announce Type: new Abstract: Autoregressive video generators synthesize long videos through chunk-wise rollouts, yet they are typically trained to predict only the immediate future.

Protein Crystallization Segmentation and Classification Using Subordinate Color Channel in Fluorescence Microscopy Images

arXiv:2610.15286v1 Announce Type: new Abstract: The accuracy of detecting protein crystals for fluorescence microscopy images is very critical for high throughput and automated systems. Although the t

Protocol Choices Matter More Than the Aggregation Algorithm: Federated Learning for In-Hospital Mortality Prediction in Care-Unit and Multi-Hospital Federations

arXiv:2610.17265v1 Announce Type: new Abstract: Federated learning allows hospitals to train a shared mortality model without pooling their records, but published comparisons of aggregation strategies

ProTok: Unified Protein Tokenization via Modular Multi-Codebook Quantization

arXiv:2610.18481v1 Announce Type: new Abstract: Protein sequence, structure, and functional annotations each capture complementary aspects of protein biology, yet existing tokenizers encode each modal

Local Ai

ProtoPur: Prototype-Purity Pseudo-Labeling for Weakly Supervised Instance Learning in Pathology

arXiv:2610.15669v1 Announce Type: new Abstract: Weak bag-level labels are widely available in computational pathology, whereas reliable instance-level annotations for whole-slide images remain expensi

local-aiarxiv-cs-cv

Tutorials

Provably scalable learning from quantum many-body data via locality

arXiv:2509.13705v2 Announce Type: replace-cross Abstract: Machine learning (ML) holds great promise for extracting insights from complex quantum many-body data obtained in quantum experiments, potenti

tutorialsarxiv-cs-lg

Local Ai

ProWorld: Multi-View World Model with Proprioceptive Feedback for Closed-Loop Policy Evaluation

arXiv:2610.15294v1 Announce Type: new Abstract: Evaluating robot manipulation policies through repeated physical trials is costly. We study whether an action-conditioned world model can serve as a clo

local-aiarxiv-cs-ro

PrT-Flow: Probability-Tractable Flow Matching for Reinforcement Learning

arXiv:2610.17667v1 Announce Type: new Abstract: Diffusion models and Flow Matching can represent flexible distributions over robot actions, but many reinforcement learning algorithms also require the

safetyarxiv-cs-ro

PsyBer-Agent: A Psychology-Driven User Agent for Recommender Systems

arXiv:2610.13989v1 Announce Type: cross Abstract: Evaluating recommender systems before deployment requires simulators that remain realistic over long, closed-loop interaction cycles. Current LLM-base

PTGPO: Prefix Tree Group Policy Optimization

arXiv:2610.14737v1 Announce Type: new Abstract: Training language agents on long-horizon interactive tasks is challenging because sparse terminal rewards provide weak credit signals for intermediate a

PUGS: Physics-Guided Uncertainty Modeling for Underwater Dynamic 3D Gaussian Splatting

arXiv:2610.17936v1 Announce Type: new Abstract: Dynamic 3D Gaussian Splatting (3DGS) provides an efficient framework for dynamic scene reconstruction. Most existing approaches model scene dynamics wit

PURE: Refining Feed-forward 3D Reconstruction with Unlabeled Post-training

arXiv:2610.14698v1 Announce Type: new Abstract: Feed-forward 3D reconstruction models have achieved remarkable progress through large-scale training and strong learned priors. However, they remain fra

applicationsarxiv-cs-cv

Push the Limit: Learning High-Speed Sprinting and Aggressive Cornering on a Full-Scale Humanoid

arXiv:2610.14840v1 Announce Type: new Abstract: We present a learning-based framework that enables a full-scale bipedal humanoid to sprint at over 7 m/s with motion-prior-guided gait and perform aggre

researcharxiv-cs-ro

PVD-ONet: A Multi-scale Neural Operator Method for Singularly Perturbed Boundary Layer Problems

arXiv:2507.21437v3 Announce Type: replace Abstract: Physics-informed neural networks and physics-informed DeepONet excel at solving partial differential equations but often struggle with singularly pe

PVFH-Bench: Benchmarking Premise-Induced Visual Fixation Hallucinations in Vision-Language Models

arXiv:2610.14635v1 Announce Type: cross Abstract: Large Vision-Language Models (LVLMs) have achieved remarkable progress across a wide range of multimodal tasks. However, object hallucination remains

Q-Save: Towards Scoring and Attribution for Generated Video Evaluation

arXiv:2511.18825v3 Announce Type: replace Abstract: Evaluating AI-generated video (AIGV) quality hinges on three crucial dimensions: visual quality, dynamic quality, and text-video alignment. While nu

QASA: Quality-Guided K-Adaptive Slot Attention for Unsupervised Object-Centric Learning

arXiv:2601.12936v2 Announce Type: replace Abstract: Slot Attention, an approach that binds different objects in a scene to a set of 'slots', has become a leading method in unsupervised object-centric

applicationsarxiv-cs-cv

QBFF: Deep Quantum Bottleneck for Automatic Speaker Verification Anti-Spoofing

arXiv:2610.16390v1 Announce Type: cross Abstract: A central goal of quantum machine learning is to identify applications in which small quantum circuits can improve task performance while remaining ex

QK-Edit: Revisiting Attention-based Injection in MM-DiT for Image and Video Editing

arXiv:2610.17829v1 Announce Type: new Abstract: Multimodal Diffusion Transformers (MM-DiTs) have recently emerged as a powerful framework for unified text-vision synthesis, surpassing traditional U-Ne

Tutorials

QSAR-Guided Generative Framework for the Discovery of Synthetically Viable Odorants

arXiv:2512.23080v2 Announce Type: replace-cross Abstract: The discovery of novel odorant molecules is key for the fragrance and flavor industries, yet efficiently navigating the vast chemical space to

tutorialsarxiv-cs-lg

QTrack: Query-Driven Reasoning for Multi-modal MOT

arXiv:2603.13759v2 Announce Type: replace Abstract: Category-level multi-object trackers estimate trajectories for all detected instances, whereas some applications require propagating only targets sp

QuAnTS: Question Answering on Time Series

arXiv:2511.05124v2 Announce Type: replace Abstract: Text offers intuitive access to information. This can, in particular, complement the density of numerical time series, thereby enabling improved int

Quantum-Classical Physics-Informed Neural Networks for Solving Reservoir Seepage Equations

arXiv:2512.03923v3 Announce Type: replace Abstract: Solving reservoir seepage partial differential equations (PDEs) is critical for hydrocarbon development optimization and production forecasting. Con

applicationsarxiv-cs-lg

Quasicrystal Architected Nanomechanical Resonators via Data-Driven Design

arXiv:2604.07379v2 Announce Type: replace-cross Abstract: From butterfly wings to remnants of nuclear detonation, aperiodic order repeatedly emerges in nature, often exhibiting reduced sensitivity to

Query-Adaptive Neuron Steering for LLMs

arXiv:2610.14519v1 Announce Type: new Abstract: Effectively eliciting the capabilities of large language models (LLMs) at inference time remains an important challenge. Existing studies address this c

Qwen 3.8 Flash Next, DeepSeekV4 Flash and Time

model-releasesr-localllama

MacStudio 128GB M1 ultra I'm curious how people who can run both models are deciding which one to work with. DeepSeekv4 benchmarks right below GPT 5.5 high. Flash Next benchmarks right above 5.4/5.5 x

qwen3.8-flash-next-iq3_s vs -27B-GGUF:UD-Q4_K_XL

model-releasesr-localllama

Sampling: temp 1.0, top_p 0.95, top_k 20 for both. Run with evalscope over the OpenAI-compatible API: Qwen3.8-Next on Strata (MI50 32GB), Qwen3.8-27B on llama.cpp (RX 7900 XTX 24GB). Caveat: These are

RadarGSOcc: Doppler-Aware 3D Semantic Occupancy Prediction Using 4D Radar Tensor with Geometric-Semantic Feature Extraction

arXiv:2610.18686v1 Announce Type: new Abstract: 3D semantic occupancy prediction is crucial for autonomous driving, but camera-based methods degrade in low-light conditions and LiDAR-based methods are