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PulseQuant: Propagation-Guided Subspace Correction for 4-Bit Video Diffusion Transformers

arXiv:2609.33384v1 Announce Type: new Abstract: Quantization errors in video diffusion transformers can be amplified or attenuated by subsequent denoising updates, making local reconstruction error an

local-aiarxiv-cs-cv

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Pushing Toward the Simplex Vertices: A Simple Remedy for Code Collapse in Smoothed Vector Quantization

DGX agent

arXiv:2509.22161v4 Announce Type: replace Abstract: Vector quantization, which discretizes a continuous vector space into a finite set of representative vectors (a codebook), has been widely adopted i

researcharxiv-cs-lg

29 Sept 2026

Q-Drift: Quantization-Aware Drift Correction for Diffusion Model Sampling

arXiv:2603.18095v2 Announce Type: replace Abstract: Post-training quantization (PTQ) is a practical path to deploy large diffusion models, but quantization noise can accumulate over the denoising traj

applicationsarxiv-cs-cv

Q-learning Penalized Transformer for Safe Offline Reinforcement Learning

arXiv:2609.34426v1 Announce Type: new Abstract: This paper addresses the problem of safe offline reinforcement learning, which involves training a policy to satisfy safety constraints using an offline

Q-WAM: 4-Bit Quantization of World Action Models with Action-Subspace Protection

arXiv:2609.33269v1 Announce Type: cross Abstract: World Action Models (WAMs) jointly generate video and robot actions through iterative diffusion and perform strongly in robotic manipulation. However,

QAM: Quadratic-Accurate Checkpoint Merging via Sequential Consistency

arXiv:2609.35168v1 Announce Type: new Abstract: Saved checkpoints record states along a training trajectory, but generally do not determine the updates at states that would be visited under a differen

local-aiarxiv-cs-lg

QAMM: Adjoint MeanFlow Matching for Few-Step Offline Reinforcement Learning

arXiv:2609.34497v1 Announce Type: new Abstract: Flow policies can model rich action distributions, but their iterative sampling limits decision speed. Adjoint matching uses the critic's action gradien

QC-Stark: A Multi-Task Benchmark Revealing Capability Dissociations in LLMs Evaluated on Quantum Computing Tasks

arXiv:2609.35581v1 Announce Type: cross Abstract: We introduce QC-Stark, a benchmark for evaluating large language models (LLMs) on 11 quantum computing (QC) tasks, spanning circuit construction, debu

Tutorials

QiYao-M: Multimodal Time Series Foundation Model with Role-Aware Modeling of Endogenous and Exogenous Modalities

arXiv:2609.34842v1 Announce Type: new Abstract: Existing multimodal time series foundation models (TSFMs) typically model heterogeneous modalities through largely shared mechanisms, overlooking the di

tutorialsarxiv-cs-lg

QSCP: Beyond Class-Name Prompts for Query-Guided Semantic Change Parsing

arXiv:2609.33088v1 Announce Type: new Abstract: Traditional change detection (CD) identifies changes between bi-temporal remote sensing images, while semantic change detection (SCD) assigns predefined

researcharxiv-cs-cv

QuacamFM: Quaternion-Constrained Flow Matching for Camera Pose Estimation

arXiv:2609.32455v1 Announce Type: new Abstract: Camera pose estimation from multi-view images remains a challenge in computer vision. Traditional methods often address this problem using Structure-fro

researcharxiv-cs-cv

QuadHand: A Compact Quadrotor Aerial Manipulator with MRC-SDF-Based Whole-Body Motion Planning

arXiv:2609.35094v1 Announce Type: new Abstract: Uncrewed aerial manipulators (UAMs) integrate robotic arms with aerial platforms for three-dimensional physical interaction. However, enlarging the work

applicationsarxiv-cs-ro

Quality Determines Direction, Length Shapes Magnitude: Length Control for Open-Ended Reinforcement Learning

arXiv:2609.34718v1 Announce Type: new Abstract: Reinforcement learning (RL) changes not only what language models say, but also how much they say, often increasing response length at the cost of token

QuantForge: Discovering Residual Decompositions for MXFP4 Post-Training Quantization

arXiv:2609.34680v1 Announce Type: new Abstract: Four-bit post-training quantization can reduce the memory demands of large language models, but preserving accuracy under strict MXFP4 W4A4 requires coo

applicationsarxiv-cs-lg

Quantifying Behavioral Tails in Black-Box Language Models

arXiv:2609.33638v1 Announce Type: new Abstract: We introduce RareTrap, a framework for estimating the probability of severe behaviors in black box large language models (LLMs). A key challenge for pro

Quantifying How Training Gradient Sparsity Affect Spiking Neural Network Accuracy And Robustness

arXiv:2509.23762v4 Announce Type: replace-cross Abstract: Spiking Neural Networks (SNNs) have recently received increasing attention in both computational neuroscience and artificial intelligence owin

researcharxiv-cs-cv

Quantifying the Effect of Test Set Contamination on Generative Evaluations

arXiv:2601.04301v3 Announce Type: replace Abstract: As frontier AI systems are pretrained on web-scale data, test set contamination has become a critical concern for accurately assessing their capabil

Quantile Head for Vision-Language-Action Models

arXiv:2609.34061v1 Announce Type: cross Abstract: Vision-Language-Action (VLA) models integrate pretrained Vision-Language Models (VLMs) with action heads for robot control. Common action heads have d

safetyarxiv-cs-cv

Quantization-Aware Pre-Training with Constrained Empirical Weight Distribution

arXiv:2609.32659v1 Announce Type: new Abstract: Quantization-Aware Pre-Training (QAPT) can increase the inference efficiency of DNNs, but a problematic behaviour known as rounding boundary weight osci

Quantization Error Is Spectrally Flat: A Single Random Probe Is a Calibrated, Data-Free Sensitivity Estimator, with Application to Budget-Targeted Mixed-Precision Quantization

arXiv:2609.33923v1 Announce Type: cross Abstract: A single random Gaussian probe gives an unbiased estimate of the squared Frobenius norm of a layer's quantization error. The estimator is well-behaved

Quasi Linear Kernel Attention with Infinite Capacity

arXiv:2609.35349v1 Announce Type: new Abstract: The evaluation cost of transformers with softmax attention scales quadratically with sequence length. Kernel attention addresses this by replacing softm

Query, Align, and Distill: Navigation-Aware Cross-Modal Interaction for Efficient Vision-and-Language Navigation

arXiv:2609.33097v1 Announce Type: new Abstract: Recent large-scale Vision-and-Language Navigation (VLN) models deliver strong accuracy but remain costly to deploy due to their large parameter counts a

Query-aligned video frame selection for long video understanding

arXiv:2609.31668v1 Announce Type: new Abstract: Multimodal large language models (MLLMs) process multimodal inputs by converting text, images, and videos into token sequences that are subsequently pro

safetyarxiv-cs-cv

Query Expansion and Key Specialization in Transformer Attention Geometry

arXiv:2609.34273v1 Announce Type: cross Abstract: The projection of queries and keys are central to the attention mechanism in Transformer architectures. While they are mathematically symmetric, they

QuPID: Quantum Parameter-Efficient Input-Dependent Retrieval Adaptation for Medical RAG

arXiv:2609.33351v1 Announce Type: cross Abstract: Fidelity-based quantum retrieval ranks candidates by the fidelity between query and archive states. Applying a shared input-independent unitary after

QwenGyre: An Elastic Reinforcement Learning Framework for Training xLong-Horizon Agents

arXiv:2609.33848v1 Announce Type: new Abstract: Large language model (LLM) agents increasingly undertake extreme-long (xlong) horizon tasks, where a single execution can span hours, hundreds of model-

R2T: Rule-Encoded Loss Functions for Sequence Tagging in Low-Resource Languages

arXiv:2510.13854v2 Announce Type: replace-cross Abstract: We introduce Rule-to-Tag (R2T), a framework that turns linguistic rules into the training signal for neural sequence taggers in low-resource l

Radiomap Blind Prediction under Incomplete Observation: Error Characterization and Correctable Propagation-Prior Learning

arXiv:2609.32836v1 Announce Type: cross Abstract: Radiomap blind prediction aims to infer radiomaps from observable representations of the propagation environment and base station configuration withou

RAEGL: Risk-Aware Evidence-Gated Learning for Selective Contextual Routing under Temporal Shift

arXiv:2609.33340v1 Announce Type: new Abstract: Contextual specialization can improve forecasting accuracy, but a correction selected on one historical interval may become unreliable under temporal di

applicationsarxiv-cs-lg

RAGWarrant: Evidence-Preserving Governance for RAG Policy Promotion Under Quality, Cost, Latency, and Risk Constraints

arXiv:2609.34179v1 Announce Type: cross Abstract: Retrieval-augmented generation systems are extensively instrumented with metrics, benchmarks, traces, and automated judges, but these tools do not dec

RAISE: Reinforcing Access Control Policy Synthesis in LLMs via Symbolic Evaluation

arXiv:2609.33796v1 Announce Type: cross Abstract: Translating natural-language access-control requirements into policies requires careful reasoning about permissions, constraints, and exceptions, and

Rank Collapse Is Recoverable, Growing |Q| Is Not: Out-of-Sample Early Warning for Value Divergence in High-UTD Soft Actor-Critic

arXiv:2609.32819v1 Announce Type: cross Abstract: Raising the update-to-data (UTD) ratio breaks off-policy critics in two ways grouped as 'plasticity loss': collapsing representations and growing valu

Rate-Adaptive One-Step Diffusion Compression for AIGC Images

arXiv:2609.31795v1 Announce Type: new Abstract: We describe our entry to the LoViF 2026 AIGC Image Compression Challenge, a benchmark for ultra-low-bitrate coding of AI-generated images under a strict

Rate-Distortion Adaptive Primitive Selection for Omnidirectional Gaussian Splatting

arXiv:2609.34367v1 Announce Type: new Abstract: Learned image codecs (LICs) achieve high reconstruction quality, but their decoding speed is often insufficient for immersive virtual reality (VR). Gaus

RAVEL: Asynchronous Rolling Inference for Flow-Based Vision-Language-Action Models

arXiv:2609.34170v1 Announce Type: new Abstract: Flow-based vision-language-action (VLA) models are highly effective for generalist robot manipulation, yet their reliance on computationally expensive V

applicationsarxiv-cs-ro

RCVLA: 4D Radar-Grounded Semantic Reasoning and Trajectory Arbitration for Autonomous Driving

arXiv:2609.32681v1 Announce Type: new Abstract: 4D radar provides geometric and motion cues that complement visual semantics, but integrating it into vision-language-action (VLA) models requires both

RE-0: Verified Recursive Improvement of Embodied Code-as-Policy Agents through Local On-Policy Distillation

arXiv:2609.32416v1 Announce Type: cross Abstract: Code-as-Policy agents accomplish long-horizon embodied tasks by generating and executing code, yet continually improving them with teachers that are s

Re-derivability Decides What a Staged Agent Pipeline Recovers After an Upstream Fault

arXiv:2609.32802v1 Announce Type: cross Abstract: One variable sets what an upstream fault costs a staged pipeline of language-model agents: re-derivability, how much of what a stage needs it can rebu

Reachability is not enough: Diagnosing long-range behavior in GNNs

arXiv:2609.33674v1 Announce Type: new Abstract: Graph neural networks (GNNs) are often called long-range because their architecture can connect distant nodes, but this does not show whether they use d

Reading Too Much into Context: Passive Exposure Can Steer LLM Decisions

arXiv:2609.33065v1 Announce Type: cross Abstract: Large language model (LLM) assistants can now search the web and consult external sources while completing user requests. These sources can provide us

applicationsarxiv-cs-lg

Readout is not Recovery: Dissociating Coordinate Emission from Visual-Corruption Repair in Vision-Language Models

arXiv:2609.32757v1 Announce Type: cross Abstract: VLM bounding-box localization is both language generation and spatial commitment. Parseable fields such as bbox_2d make localization easy to score, bu

ReAL: Accelerating Flow Matching through Segment Advancement with Shared Lookahead

arXiv:2609.33202v1 Announce Type: new Abstract: Flow-matching models generate high-quality images and videos, but repeated neural network evaluations make sampling expensive. Skipping evaluations redu

local-aiarxiv-cs-cv

REALIS: A Curated Dataset for Studying the Challenges of AI Image Detection

arXiv:2609.32734v1 Announce Type: new Abstract: AI-generated image detectors are often evaluated on benchmarks where real and synthetic images differ in content, quality, or generation artifacts, allo

REALM: A Coarse-to-Fine Generative Framework for Embodied Reactive Listening

arXiv:2609.33095v1 Announce Type: new Abstract: Generating responsive listener facial motion is an important task for embodied conversational AI. Two modeling challenges are central: accounting for th

local-aiarxiv-cs-ro

REALM: Regime-Switching, Explainable, and Activation-Induced Linear Models

arXiv:2609.32141v1 Announce Type: new Abstract: Deep ReLU networks are piecewise-affine mappings that partition the input space into cells, each characterized by a distinct activation pattern. This st

local-aiarxiv-cs-lg

RECAST: Recasting Vision-Language Semantics into an Actionable Cost Map for Robot Navigation

arXiv:2609.32595v1 Announce Type: new Abstract: Safe and robust robot navigation across diverse environments requires a high-level understanding of complex scenes and the ability to carry it into stab

researcharxiv-cs-ro

RecastVLA: From Past Interaction to Future Control with Adaptive Policy States

arXiv:2609.32155v1 Announce Type: new Abstract: Sequential manipulation requires a robot to track what has already happened, even when the current scene no longer reveals it. Policies with explicit hi

safetyarxiv-cs-ro

ReCAT: Remember, Count, and Time: Structured Recurrent Memory for Robot Manipulation

arXiv:2609.35200v1 Announce Type: cross Abstract: Memory-dependent manipulation requires robots to make decisions using information that is no longer available to their current sensors, such as recall

safetyarxiv-cs-cv