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Position: Unlabeled IS NOT Equal to No Human Supervision in Visual Learning

arXiv:2609.03077v1 Announce Type: new Abstract: This position paper argues that the absence of labels does not imply the absence of human supervision in visual learning, and urges the research communi

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Safety

PPO-STGNN: A Proximal Policy Optimization Approach with Spatio-Temporal Graph Neural Networks for DAG Task Scheduling in Cloud-Edge-End Computing

DGX agent

arXiv:2609.03503v1 Announce Type: new Abstract: With the rapid development of the Internet of Things, computation intensive directed acyclic graph (DAG) tasks have become increasingly common in cloud-

safetyarxiv-cs-ai

4 Sept 2026

Predictive Zonotope Reduction: Precise Runtime Monitoring under Uncertainty

arXiv:2609.03699v1 Announce Type: new Abstract: Robots operating in physical environments make control decisions based on uncertain sensor measurements, which can lead to unsafe or suboptimal actions.

safetyarxiv-cs-ro

Preprocessing Failure and Adversarial Detection in Depthwise-Separable Edge Vision Systems

arXiv:2609.03453v1 Announce Type: new Abstract: Preprocessing-based defenses are the standard first-line response to adversarial attacks on edge vision systems, requiring no retraining, no architectur

Preserving Knowledge across Space and Time for Continual Video Deepfake Detection

arXiv:2609.03446v1 Announce Type: new Abstract: The continuous emergence of high-quality video deepfakes requires detectors that continually adapt to new forgery patterns, yet existing approaches, whi

Principia: Relational Physics Tests for Video Models

arXiv:2609.04200v1 Announce Type: new Abstract: Evaluating physical reasoning in video models is difficult because absolute motion measurements depend on frame rate, object scale, and camera calibrati

Privacy Leakage in Federated Learning: Gradient-Based Client Identity Inference and Defenses for Inertial Sensing in Vehicular Edge Networks

arXiv:2609.02971v1 Announce Type: cross Abstract: As vehicular networks move toward 5G/6G edge intelligence, federated learning (FL) is widely promoted as a privacy-preserving way for vehicles and inf

Privacy-Preserving Heterogeneous Multi-LLM Federated Inference for Cognitive Diagnosis

model-releasesarxiv-cs-ai

arXiv:2609.02947v1 Announce Type: cross Abstract: Significant challenges remain in AI-driven educational systems in balancing privacy preservation with accurate cognitive diagnosis. To overcome this,

Local Ai

Privacy-Preserving Topology-Guided Safety for LLM-Based Multi-Agent Systems via Federated Graph Learning

arXiv:2609.02967v1 Announce Type: cross Abstract: Topology-guided safeguards for LLM-based multi-agent systems (MAS) train a GNN over the inter-agent communication graph to localize risky agents and i

local-aiarxiv-cs-ai

Privacy, Robustness, and Fairness Trade-offs in Federated Intrusion Detection: Geometric Indistinguishability at the Aggregation Interface

arXiv:2609.03420v1 Announce Type: cross Abstract: Federated learning enables privacy-conscious collaboration for network intrusion detection without centralizing sensitive traffic data, yet its deploy

Applications

PrivateHub: Contrastive Diffusion Model for Private Sensor-Intensive Environment Data Generation

arXiv:2609.02958v1 Announce Type: cross Abstract: Sensor-intensive environments enable many intelligent services by inferring user applications from heterogeneous data streams. However, not all applic

applicationsarxiv-cs-ai

ProAct: Harnessing Streaming Motion Generation and Agentic Reasoning for Real-Time Embodied Social Interaction

arXiv:2602.14048v2 Announce Type: replace-cross Abstract: Real-time embodied social interaction places two equally demanding requirements on an agent: continuously generating fluent multimodal interac

Proactive Service Agents: A Unified Decision Framework, Methods, and Evaluation

arXiv:2609.03727v1 Announce Type: new Abstract: Large language model agents can plan, invoke tools, and modify external states, yet most systems still take an explicit user instruction as a fixed star

Probe Generalization as Subspace Selection for OOD Deception Detection

model-releasesarxiv-cs-cl

arXiv:2609.02893v1 Announce Type: new Abstract: Linear probes can be used to detect behaviors and concepts inside language model activations, but may fail to transfer to out-of-distribution examples.

Applications

Programming and execution of skill-based human-robot-crane collaborative tasks

arXiv:2609.03392v1 Announce Type: new Abstract: Highly varying production sets increasing challenges for robotic manufacturing and indoor logistics. New capabilities for agility, flexibility, and robu

applicationsarxiv-cs-ro

ProgResViT: Progressive Resolution and Width for Adaptive Vision Transformers

arXiv:2609.03216v1 Announce Type: new Abstract: Vision Transformers (ViTs) typically process every image using a fixed input resolution and model width, even though many images can be classified with

Projected Riemannian Gradient Descent for the Bures-Wasserstein Barycenter: Dimension-Independent Linear Convergence at Unit Step Size

arXiv:2609.03762v1 Announce Type: new Abstract: The computation of the Bures-Wasserstein (BW) barycenter of an ensemble of positive definite matrices arises throughout machine learning, optimal transp

Prospective Coding Improves Learning in Deep Continuous-Time Recurrent Networks

arXiv:2609.04134v1 Announce Type: new Abstract: Temporal integration gives continuous-time recurrent networks memory, but in deep stacks it also delays bottom-up signals and attenuates top-down errors

Puffin-World: Scaling a Unified Multimodal Model with Native 3D World States

arXiv:2609.04196v1 Announce Type: new Abstract: We propose Puffin-World, a unified multimodal architecture that integrates physical understanding, spatial simulation, and 3D world generation and recon

Local Ai

Pushing the (Decision) Boundaries: Dynamically Calibrating Differentially Private Noise to Explainability in Federated Learning

arXiv:2609.03851v1 Announce Type: new Abstract: Federated Learning (FL) with Differential Privacy (DP) is increasingly adopted to preserve data confidentiality in distributed machine learning. However

local-aiarxiv-cs-lg

Hardware

QLAUN: A Research-Oriented, Robust, Agile, Modular, and Affordable Torque-Controlled Quadruped Robot

arXiv:2609.03623v1 Announce Type: new Abstract: QLAUN Bot (Quad-Legged Adaptive Unmanned Navigator Robot) is a torque-controlled quadruped robot that is research-oriented, cost-effective, and aimed at

hardwarearxiv-cs-ro

Quantum Implicit Neural Representations for Novel View Synthesis

arXiv:2601.05250v2 Announce Type: replace Abstract: Quantum implicit neural representations have recently emerged as compact function approximators for continuous visual signals. In parallel, Neural R

safetyarxiv-cs-cv

R^{2}Adapter: A Routing and Rewriting Adapter for Efficient Hybrid RAG

arXiv:2609.02894v1 Announce Type: new Abstract: Retrieval-Augmented Generation (RAG) has become a prevailing paradigm for enhancing Large Language Models (LLMs) with non-parametric knowledge. Vanilla

researcharxiv-cs-cl

R2S-Eval: Robot Evaluation with Real-to-Sim Calibration via Vision-Language Models

arXiv:2609.03276v1 Announce Type: new Abstract: Evaluating robot manipulation policies is becoming increasingly important as generalist models, particularly vision-language-action (VLA) models, are de

safetyarxiv-cs-ro

RACE-AIMC: Selective Inference for Heterogeneous Analog In-Memory Accelerators at the Edge

arXiv:2609.03149v1 Announce Type: cross Abstract: Analog in-memory computing (AIMC) speeds up neural-network inference by doing the arithmetic directly inside a memory array, instead of shuttling weig

Random Attention: Rethinking KV Cache Eviction for Efficient Reasoning

arXiv:2609.03430v1 Announce Type: new Abstract: Large language models achieve superior performance on tasks that require extended reasoning, but long chains of thought make the KV cache a severe memor

researcharxiv-cs-cl

RARF: Region-Aware Rectified Flows for 3D Brain MRI Inpainting

arXiv:2609.03956v1 Announce Type: cross Abstract: Medical image inpainting has the potential to improve automated brain MRI analysis by reconstructing healthy tissue within pathological regions. We in

researcharxiv-cs-ai

Local Ai

RATL: Learning from Retrieved Residuals for Robust Multivariate Time-Series Forecasting

arXiv:2609.03937v1 Announce Type: cross Abstract: Retrieval-augmented generation (RAG) complements parametric models with retrieved external evidence. The same idea is attractive for continuous-output

local-aiarxiv-cs-ai

Real-Time Shape Control of Multi-Segment Soft Robotic Arms Using Koopman Operators with Global and Local Observables

arXiv:2609.03175v1 Announce Type: new Abstract: Multi-segment soft robotic arms can continuously reconfigure their body shapes for safe interaction, but tip control alone is insufficient for constrain

researcharxiv-cs-ro

RealCADBench: Benchmarking Parametric CAD Modeling from Industrial Design Intents

model-releasesarxiv-cs-cl

arXiv:2609.03773v1 Announce Type: cross Abstract: Parametric computer-aided design (CAD) modeling is difficult to evaluate with a single metric. Existing CAD benchmarks often emphasize synthetic or CA

Applications

RECAST: Expanding the Boundaries of LLMs' Complex Instruction Following with Multi-Constraint Data

arXiv:2505.19030v5 Announce Type: replace Abstract: Large language models (LLMs) are increasingly expected to tackle complex tasks, driven by their expanding applications and users' growing proficienc

applicationsarxiv-cs-ai

ReCoSplat: Online Feed-Forward Gaussian Splatting via Render-and-Compare

arXiv:2603.09968v2 Announce Type: replace Abstract: Online novel view synthesis requires a model to reconstruct a scene causally from a stream of observations while keeping it renderable at every mome

RecurTrace: Adaptive Latent Reasoning with Loop-Time Memory

arXiv:2609.03379v1 Announce Type: new Abstract: Repeating a small block of middle layers increases a language model's effective inference depth without adding parameters or generating extra tokens, an

Reducing Catastrophic Risk from AI with Systematic Monitoring and Evaluation of Rogue AI Progression

arXiv:2609.03189v1 Announce Type: cross Abstract: This article presents a structured framework of behavioral indicators that may signal progression toward potentially catastrophic threats from artific

researcharxiv-cs-ai

Reflect-SQL: A Self-Reflection Based Framework for Text-to-SQL

model-releasesarxiv-cs-ai

arXiv:2609.02944v1 Announce Type: cross Abstract: Democratizing data access through natural language is a crucial goal for modern enterprises, but the practical adoption of Text-to-SQL is critically h

Relative Prime Factorization and Finite-State Presentations under Fixed Finite-Monoid Observation

arXiv:2609.03643v1 Announce Type: cross Abstract: Let LsubseteqSigma^* and fix a morphism h:Sigma^*o M into a finite monoid. We study exact factorization and canonical presentation in the relative syn

Reliable Selection of Heterogeneous Treatment Effect Estimators

arXiv:2511.18464v2 Announce Type: replace-cross Abstract: We study the problem of selecting the best heterogeneous treatment effect (HTE) estimator from a collection of candidates in settings where th

Remember and Reweight: Enhancing Multi-Agent Debate with Experience Memory and Confidence Estimation

arXiv:2609.03619v1 Announce Type: cross Abstract: Multi-agent debate (MAD) improves the reasoning capabilities of large language models by having multiple agents iteratively refine their responses thr

Rent-a-RAG: Embedding-Space Watermarks for Auditing Third-Party RAG

model-releasesarxiv-cs-cl

arXiv:2609.03749v1 Announce Type: cross Abstract: Third-party retrieval-augmented generation (RAG) marketplaces create a new auditing problem: data providers may license corpora to a RAG operator, yet

Representational alignment yields generalizable safety in language models

model-releasesarxiv-cs-ai

arXiv:2609.04022v1 Announce Type: cross Abstract: Aligning large language models (LLMs) is essential for their safe deployment. Current alignment methods mainly optimize observable responses, yet mode

Residual neural networks overcome the curse of dimensionality for semilinear heat equations

arXiv:2609.03626v1 Announce Type: cross Abstract: Rigorous results show that feedforward neural networks can overcome the curse of dimensionality in the numerical approximation of high-dimensional par

Residual Optimal Transport-Based Experts Collaboration Towards Modality-Aware Infrared-Visible Object Detection

arXiv:2609.03516v1 Announce Type: new Abstract: Infrared-visible object detection (IVOD) integrates complementary evidence from visible and infrared sensors for reliable perception in challenging scen

Resolution-Aware Experimental Design under Partial Identifiability

arXiv:2609.03686v1 Announce Type: new Abstract: Experimental design is commonly framed as choosing the experiment expected to provide the most information. Under partial identifiability however, persi

Restricted Eigenvalues Beyond Gaussian Width: Threshold Occupancy under Heavy Tails

arXiv:2609.03504v1 Announce Type: new Abstract: Restricted eigenvalue (RE) bounds govern stable recovery by norm-regularized estimators. For isotropic sub-Gaussian measurements, the benchmark sample s

Rethinking 3D Noise: Learning 3D-Aware Video Priors via Optimization-Free Morphological Perturbations

arXiv:2609.03657v1 Announce Type: new Abstract: 3D scene representations like NeRF and 3D Gaussian Splatting (3DGS) suffer severe artifacts in sparse-view settings. Recent generative 3D artifact fixer

Rethinking On-Policy Distillation of Large Language Models II: One Training Example

arXiv:2609.04172v1 Announce Type: new Abstract: On-policy distillation (OPD) combines student-generated rollouts with dense token-level supervision from a teacher. Existing work has mainly studied its

Rethinking World Models for Safety-Critical Embodied Systems

arXiv:2609.03774v1 Announce Type: new Abstract: World models have progressed from compact latent dynamics to generative, controllable, and interactive simulators of embodied environments. However, hig

Revisiting Topological Graphs for Macro Action based Closed-loop Reinforcement Learning of Vision Language Navigation in Continuous Environment

arXiv:2609.03906v1 Announce Type: new Abstract: Vision-Language Navigation in Continuous Environments (VLN-CE) requires an agent to follow natural language instructions through unseen environments. Ex

safetyarxiv-cs-ro