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arXiv:2609.00956v1 Announce Type: new Abstract: Removing an object is not the same as filling its mask. Cast shadows and contact shading usually lie outside the user-provided instance mask M_obj, so a

tutorialsarxiv-cs-cv

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Predicting Program Exit Code with LLMs and Programming Language Semantics

DGX agent

arXiv:2609.00579v1 Announce Type: cross Abstract: Large language models (LLMs) have shown proficiency in various software engineering tasks, such as code generation and translation. However, a key lim

researcharxiv-cs-ai

2 Sept 2026

Predicting Subsurface Abnormalities Growth using Physics-Informed Neural Networks

arXiv:2609.01417v1 Announce Type: new Abstract: The research explores the pioneering integration of Physics-Informed Neural Networks (PINNs) into the domain of Ground-Penetrating Radar (GPR) data pred

researcharxiv-cs-lg

Prediction-Assisted Pricing and Admission for LLM APIs with Stochastic Token Consumption

arXiv:2609.00710v1 Announce Type: cross Abstract: An LLM application often sells or internally allocates several service products: a small or premium model, a short or long token cap, and possibly mul

Prior-Guided Implicit Neural Representations for Single-Subject Diffusion MRI Super-Resolution

arXiv:2609.00981v1 Announce Type: cross Abstract: Resolving complex fiber geometries in brain white matter requires high-resolution diffusion MRI at the cost of long acquisition times. This leads many

Probabilistic Model Checking of Autoregressive Neural Sequence Models

arXiv:2609.00838v1 Announce Type: cross Abstract: Test-set accuracy is silent on two issues that matter when deploying autoregressive neural sequence models: how much probability mass the system under

Agents

Probabilistic Multi-Agent Aircraft Landing Time Prediction

arXiv:2512.08281v2 Announce Type: replace-cross Abstract: Accurate and reliable aircraft landing time prediction is essential for effective resource allocation in air traffic management. However, the

agentsarxiv-cs-lg

Probing Factual Knowledge Transfer with Training Data Interventions

arXiv:2609.01341v1 Announce Type: cross Abstract: Do multilingual language models transfer factual knowledge across languages during continued pretraining, or do they mostly recall facts learned direc

Process-Aware AI for Rainfall-Runoff Modeling: A Mass-Conserving Neural Framework with Hydrological Process Constraints

arXiv:2603.25093v2 Announce Type: replace Abstract: Machine learning models can achieve high predictive accuracy in hydrological applications but often lack physical interpretability. The Mass-Conserv

Prompt-Robust Language Models: Which Training Strategies Work?

arXiv:2609.01217v1 Announce Type: new Abstract: Despite their strong performance, large language models remain highly sensitive to prompt formulation. Prior work addresses this through refined data co

Provably Efficient Federated Reinforcement Learning with Linear Function Approximation and Logarithmic Communication Cost

arXiv:2609.00193v1 Announce Type: cross Abstract: We study federated online reinforcement learning with linear function approximation. While recent multi-agent reinforcement learning algorithms achiev

Provably Safe Sim-to-Real Transfer

arXiv:2609.01418v1 Announce Type: cross Abstract: To mitigate the sample complexity of real-world reinforcement learning (RL), a common practice is to first train a policy in a simulator, where sample

ProxPI: Proximal Prior Injection for Sampling-Based MPC under Learned-Prior Mismatch

arXiv:2609.00941v1 Announce Type: new Abstract: Combining learned policies with model predictive control can leverage learned task priors while retaining online adaptation to new objectives and constr

safetyarxiv-cs-ro

Puppeteer: Object-Grounded Posture-Aware Co-Speech Gesture Generation

arXiv:2609.00369v1 Announce Type: new Abstract: Generating co-speech gestures that are temporally coherent, semantically aligned with speech, and grounded with surrounding objects remains challenging.

safetyarxiv-cs-cv

Hardware

PyDoseRT Proton: A GPU Pencil-Beam Engine with a Convolutional Residual-Correction Network for Fast Proton Dose Calculation

arXiv:2609.01018v1 Announce Type: cross Abstract: Architecture category. Hybrid method: a physics-based analytical pencil-beam (PB) dose engine followed by a 3-D convolutional residual-correction netw

hardwarearxiv-cs-cv

QILP-0: Constructing Observational Declarative Twins of Quantum Circuits

arXiv:2609.01049v1 Announce Type: new Abstract: This paper introduces QXymb, a general framework for constructing observational declarative twins of quantum circuits, and develops QILP-0, its first co

Hardware

QTEA: Ternary LLMs with Sparse Residual Salient Weight and By-Column Optimization

arXiv:2609.00224v1 Announce Type: cross Abstract: Weight-only post-training quantization (PTQ) can alleviate the computational burden of serving large language models (LLMs) at scale. However, existin

hardwarearxiv-cs-ai

Quantum Sparse Autoencoders for Q-Matrix Estimation in Cognitive Diagnosis

arXiv:2609.01537v1 Announce Type: new Abstract: Q-matrices play a central role in cognitive diagnosis within educational data mining (EDM), specifying which latent skills each assessment item requires

Quit While You're Ahead: Quit for Efficient Candidate Generation in Machine Translation Reranking

arXiv:2609.00588v1 Announce Type: new Abstract: Reranking methods, such as Minimum Bayes Risk (MBR) decoding and Quality Estimation (QE) reranking, are widely used in modern neural machine translation

researcharxiv-cs-cl

Qwen-Drive-1.0: An Initial Step towards a Vision-Language Foundation Model for Autonomous Driving

model-releasesarxiv-cs-cv

arXiv:2609.00111v1 Announce Type: new Abstract: We present Qwen-Drive-1.0, an initial step towards a vision-language foundation model for autonomous driving. Qwen-Drive-1.0 retains the architecture of

RadMatch: Auditable Radiology Report Evaluation via Finding-Level Matching

arXiv:2609.01470v1 Announce Type: new Abstract: As AI systems are increasingly used to draft radiology reports, reliably evaluating their clinical quality remains a critical challenge. Large language

safetyarxiv-cs-cv

Agents

RAPIDMap: Rapid Multi-Agent Pipeline for Interpretable Disaster Mapping from Satellite and Street-view Imagery

arXiv:2609.00046v1 Announce Type: cross Abstract: Rapid and reliable disaster mapping of impacted areas, damaged infrastructure, and affected populations is essential for emergency response and recove

agentsarxiv-cs-ai

ReA-OVCD: Training-Free Open-Vocabulary Change Detection via Semantic-Spatial Reliability Assessment

arXiv:2606.20032v2 Announce Type: replace Abstract: Unlike traditional remote sensing change detection that relies on predefined categories, Open-Vocabulary Change Detection (OVCD) identifies land cov

Ready to Speak: Aligning LLMs for TTS-Friendly Text Generation

arXiv:2609.01246v1 Announce Type: new Abstract: Current Large Language Models (LLMs) are primarily optimized for written text, often producing outputs that are grammatically correct and helpful yet po

safetyarxiv-cs-cl

REAL-Q: E2E LLM Quantization via Dynamic Gradient Descent

arXiv:2609.00049v1 Announce Type: cross Abstract: Post-training quantization (PTQ) is essential for deploying large language models (LLMs) under strict resource constraints. State-of-the-art PTQ metho

Real-Time Neuromorphic Spectrum Intelligence Simulator

arXiv:2609.00585v1 Announce Type: cross Abstract: We present the Real-Time Neuromorphic Spectrum Intelligence Simulator (RT-NuSIS), a modular framework to study spiking neural network (SNN) and memris

ReBridge-Flow: Re-Coupling Posterior Bridges in Flow Matching for Image Restoration

arXiv:2609.00811v1 Announce Type: new Abstract: Flow Matching provides an efficient generative prior for image restoration by learning continuous transport between source and data distributions. Howev

RecalibrateGPT: AI Fatigue Resilient Conversational Interfaces

arXiv:2609.00506v1 Announce Type: cross Abstract: Large language models are powerful, but their interfaces often devolve into a type rightarrow read rightarrow retype loop, creating conversational AI

Tutorials

Recent Developments in Transformer Inference Deployment on FPGA Platforms: A Survey

arXiv:2609.01212v1 Announce Type: new Abstract: With the rapid and continuous growth in the incorporation of machine learning models based on the Transformer architecture, capable deployment is in hig

tutorialsarxiv-cs-lg

Hardware

Reconstruct! Don't Encode: Self-Supervised Representation Reconstruction Loss for High-Intelligibility and Low-Latency Streaming Neural Audio Codec

arXiv:2603.05887v2 Announce Type: replace-cross Abstract: Neural audio codecs optimized for mel-spectrogram reconstruction often fail to preserve intelligibility. While semantic encoder distillation i

hardwarearxiv-cs-ai

Recurrent State Encoders for Efficient Neural Combinatorial Optimization

arXiv:2509.05084v2 Announce Type: replace Abstract: The primary paradigm in Neural Combinatorial Optimization (NCO) consists of construction methods, where a neural network is trained to sequentially

researcharxiv-cs-lg

Recursive Criticality of AI Self-Improvement

arXiv:2609.00137v1 Announce Type: new Abstract: AI is increasingly used in the R&D process that produces future AI systems. We study the conditions under which this feedback becomes self-amplifying. O

ReDeck: Step-Level Render-Grounded Refinement for Document-to-Slide Generation

arXiv:2609.00194v1 Announce Type: new Abstract: Document-to-slide generation is challenging because slides are dense editable artifacts that require both faithful content selection and precise spatial

REFACTOR-VLA: Unsupervised Library Learning of Typed Motor Programs

arXiv:2609.01215v1 Announce Type: cross Abstract: Most vision-language-action (VLA) models -- OpenVLA, pi_0, RT-2, RDT-1B -- are monolithic: they emit raw motor commands or short action chunks without

ReFlowSET: Representation-Aligned Latent Flow Matching for SAR-to-EO Image Translation

arXiv:2609.00968v1 Announce Type: new Abstract: SAR-to-EO image translation aims to generate electro-optical (EO) imagery from synthetic aperture radar (SAR) observations. Existing latent diffusion ap

safetyarxiv-cs-cv

Agents

Reinforcement Learning Enhanced LLM Agents for Complex Vehicle Routing Problems

arXiv:2609.00859v1 Announce Type: new Abstract: Vehicle Routing Problems (VRPs) are fundamental combinatorial optimization problems with widespread applications in various scenarios. The advanced opti

agentsarxiv-cs-ai

Relational-Core Graph Analytics Querying graphs at SQL scale, and why the node/edge model is a performance tax, not a truer picture of connected data

arXiv:2609.01525v1 Announce Type: cross Abstract: A durable assumption holds that graph analytics requires a purpose-built graph engine, and that relational systems are ill-suited to connected data. W

Relational Task Generation Language: A Declarative Specification Framework for Relational Deep Learning

arXiv:2609.01292v1 Announce Type: cross Abstract: Relational Deep Learning (RDL) has become a powerful paradigm for learning from multi-tabular data. However, manually defining RDL prediction tasks is

Reliability Challenges in Diffusion Vision-Language Models

arXiv:2609.01318v1 Announce Type: cross Abstract: Diffusion-based Large Vision-Language Models (dLVLMs) have recently emerged as a compelling alternative to autoregressive (AR) LVLMs, offering advanta

safetyarxiv-cs-cl

Applications

Removable and Irreducible: A Token-Cost Ledger for the Multilingual Tokenization Tax

arXiv:2609.00378v1 Announce Type: new Abstract: Large language models pay a well-documented tax on non-English text: the same content costs several times more tokens, and because attention is quadrati

applicationsarxiv-cs-cl

ReNFT: Repairing Mode Collapse in Reward Post-Training via Internal Probability-Mass Recalibration

arXiv:2609.00061v1 Announce Type: cross Abstract: Reward post-training of diffusion generators inevitably concentrates probability mass on a few reward-favored modes, a mode collapse that erases withi

Replacing Training with Memory: Listwise Selection for Text-to-SQL

arXiv:2609.00834v1 Announce Type: cross Abstract: Modern Text-to-SQL systems often follow generate-execute-select pipelines, generating multiple candidate queries then selecting the best one. Listwise

Replicating TRACE: A Practitioner's Guide to Its Threshold and Particle Budget

arXiv:2609.01108v1 Announce Type: new Abstract: TRACE (Math & Lienhart, arXiv:2602.01135) reads causal graphs over event types out of a pretrained autoregressive sequence model by thresholding a per-p

RePro: Proof-Verified Benchmark Rewriting for Reliable Evaluation of LLM Mathematical Problem Solving

arXiv:2609.00062v1 Announce Type: cross Abstract: Data contamination undermines the reliable evaluation of large language models (LLMs) on mathematical problem solving. While rewriting-based evaluatio

ReproRepo: Scaling Reproducibility Audits with GitHub Repository Issues

arXiv:2606.18237v2 Announce Type: replace-cross Abstract: Reproducing research results from papers and released code is central to scientific progress. Existing works have introduced benchmarks to eva

Residual Kalman Dynamics for Event-Based UAV Forecasting

arXiv:2609.00839v1 Announce Type: new Abstract: We study short- and mid-horizon UAV bounding-box forecasting on the FRED event-camera dataset. We use a constant-velocity Kalman filter over a full cent

Residual Sparsification via Output Importance for Compressing Mixture-of-Experts LLMs

arXiv:2609.00575v1 Announce Type: new Abstract: Mixture-of-experts (MoE) architectures scale large language models efficiently, but they demand massive GPU memory. To cope with such demand, models are

RestoreBench: Can AI Agents Restore Power Flow Convergence?

arXiv:2609.00384v1 Announce Type: new Abstract: Large Language Model (LLM) agents increasingly automate multi-step engineering workflows through tool use, interpretation of intermediate results, and i