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arXiv:2609.03770v1 Announce Type: cross Abstract: Institutions practising outcome-based education compute learning outcome attainment routinely, while reviews of curriculum analytics report an absence

tutorialsarxiv-cs-cl

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Observation-Conditioned Latent Energy Priors for Sparse Implicit Neural Shape Completion

DGX agent

arXiv:2609.03694v1 Announce Type: new Abstract: Implicit neural representations (INRs) can model continuous 3D shapes with a shared coordinate decoder and per-instance latent codes. At test time, auto

researcharxiv-cs-cv

4 Sept 2026

ObserverBench: Testing Mechanistic Estimates for Intervention and Control

arXiv:2609.03026v1 Announce Type: cross Abstract: Mechanistic interpretability is increasingly used to guide interventions such as activation steering, circuit removal, and safety monitoring. Yet an i

Occlusion-Robust Multimodal Emotion Recognition in VR via Fusion of Facial Images and EMG

arXiv:2609.03569v1 Announce Type: new Abstract: Head-mounted displays (HMDs) fundamentally limit emotion recognition in virtual reality (VR): by occluding the upper face, they render conventional imag

Occupancy-based Quantile Risk Control

arXiv:2609.03104v1 Announce Type: cross Abstract: Conformal risk control is an emerging framework for the safe deployment of machine learning models with finite-sample guarantees. To accommodate a bro

Local Ai

OCR-EDR: Rendering-Aware Diagnosis and Repair for Closed-Loop OCR Improvement

arXiv:2609.03445v1 Announce Type: new Abstract: Although document OCR systems perform increasingly well on routine documents, complex formulas, structured text, and long-tail formats remain error-pron

local-aiarxiv-cs-cv

OctWorld: Long-Range World-Consistent Video Generation with Octree-Based 3D Mapping

arXiv:2609.03919v1 Announce Type: new Abstract: We present OctWorld, a video diffusion framework with persistent 3D memory for generating explorable, world-consistent, and high-fidelity visual scenes.

Off the Planckian Locus: Using 2D Chromaticity to Improve In-Camera Color

arXiv:2511.17133v4 Announce Type: replace Abstract: Traditional in-camera colorimetric mapping relies on correlated color temperature (CCT)-based interpolation between pre-calibrated transforms optimi

On the Interaction Between Model Compression and Test-Time Adaptation

arXiv:2609.03604v1 Announce Type: cross Abstract: Deep neural networks deployed in the wild must be both efficient and adaptable, requiring model compression and test-time adaptation (TTA). While both

researcharxiv-cs-ai

One Editor, Many Edits: A Unified Training-Free Framework for Diverse Video Editing

arXiv:2609.04190v1 Announce Type: cross Abstract: Video editing spans diverse editing paradigms, yet achieving high-quality instruction-guided and subject-guided editing within a single unified framew

researcharxiv-cs-ai

One Model to Translate Them All? A Journey to Mount Doom for Multilingual Model Merging

arXiv:2604.02881v2 Announce Type: replace-cross Abstract: Weight-space model merging combines independently fine-tuned checkpoints without access to the original training data. While merging has shown

researcharxiv-cs-ai

Applications

Opening mind by opening architecture: analysis strategies

arXiv:2609.03719v1 Announce Type: new Abstract: In numerical signal processing for electroacoustic composition, the progressive loss of specific development and research environments caused by the inc

applicationsarxiv-cs-cl

Agents

ORMOT: A Dataset and Framework for Omnidirectional Referring Multi-Object Tracking

arXiv:2603.05384v2 Announce Type: replace Abstract: Omnidirectional cameras provide 360{eg} spatial coverage, making them increasingly valuable in applications such as autonomous driving and video sur

agentsarxiv-cs-cv

OSR: Output Space Redistribution for Adaptive Label Removal in Classification Models

arXiv:2609.03972v1 Announce Type: new Abstract: Label removal occurs frequently in classification systems with evolving taxonomies, where categories must be dynamically updated or eliminated. To accom

researcharxiv-cs-lg

Agents

Out-of-Distribution Generalisation with Sequence Models in Offline Multi-Agent Reinforcement Learning

arXiv:2609.03667v1 Announce Type: cross Abstract: Generalising to unseen tasks remains a fundamental challenge in offline multi-agent reinforcement learning (MARL). In this work, we present a principl

agentsarxiv-cs-ai

Tutorials

P-CORE: Self-Supervised Surface Consistency for Point-Based Neural Editing

arXiv:2609.03349v1 Announce Type: new Abstract: Advances in neural rendering have enabled high-fidelity multi-view reconstruction of 3D scenes. However, free-form non-rigid shape editing remains a sig

tutorialsarxiv-cs-cv

PACE: Towards Surfacing Hidden Conflicts in User Requests

arXiv:2609.03293v1 Announce Type: new Abstract: Personalized assistants should not only comply with user requests but also assess whether those requests are appropriate given the user's current circum

safetyarxiv-cs-cl

PaperScout: An Autonomous Agent for Academic Paper Search with Process-Aware Sequence-Level Policy Optimization

arXiv:2601.10029v3 Announce Type: replace Abstract: Academic paper search is a fundamental task in scientific research, yet most existing approaches organize retrieval around predefined workflows or s

Hardware

Para-Pipe: Exploiting Hierarchical Operator Parallelism of ML Computational Graphs on SoCs

arXiv:2609.04168v1 Announce Type: cross Abstract: As edge-based deep learning applications become more complex, optimizing performance on heterogeneous System-on-Chips (SoCs) presents unique challenge

hardwarearxiv-cs-lg

Parameterised graph theory for tensor networks: entanglement rerouting, structural simplification, and agnostic tomography

arXiv:2609.04165v1 Announce Type: cross Abstract: Parameterised graph theory studies how the complexity of graph-theoretic problems depends on structural parameters of the input graph. This perspectiv

PatchBench: Evaluating AI Agents for Vulnerability Patching

arXiv:2609.04075v1 Announce Type: cross Abstract: AI agents have recently demonstrated strong performance in automated vulnerability patching. However, existing evaluations often validate a patch only

Pattern Over-Generalization of Knowledge Graph Embedding

arXiv:2609.03487v1 Announce Type: cross Abstract: Knowledge graph embedding (KGE) demonstrates its effectiveness for predicting missing links in knowledge graphs (KGs) by projecting entities and relat

PeroMAS: A Multi-agent System of Perovskite Material Discovery

arXiv:2602.13312v2 Announce Type: replace-cross Abstract: As a pioneer of the third-generation photovoltaic revolution, Perovskite Solar Cells (PSCs) are renowned for their superior optoelectronic per

Persistent Identity Preservation in Generative Image Models: A Benchmark and Evaluation System

model-releasesarxiv-cs-cv

arXiv:2609.04151v1 Announce Type: new Abstract: Generative image models can now produce high-quality images, follow complex instructions, and support precise edits, but they still struggle to preserve

Physics-Informed Neural Network Surrogate for Oxygen Vacancy Dynamics in epitaxial SrTiO_3 on Si memristors via Dynamic Spectral Optimization

arXiv:2609.02966v1 Announce Type: cross Abstract: Physics-informed neural networks (PINNs) offer a promising framework for modeling semiconductor devices, yet standard architectures struggle with seve

PiPMRE: A Pipeline Based on Language Model for Medical Relation Extraction

arXiv:2609.02896v1 Announce Type: new Abstract: Medical relation extraction (MRE) is commonly known for extracting entities and their relations jointly from a medical text, which has attracted conside

researcharxiv-cs-cl

PL-SCEA: Reconfiguring Pretrained Attention for Few-Shot Industrial Anomaly Detection

arXiv:2609.03655v1 Announce Type: new Abstract: Vision Foundation Models (VFMs) provide transferable patch representations for few-shot industrial anomaly detection, but their attention computation is

Plan Pointers and Record-Directive Form in Budgeted Verification of Inherited Agent Memory

arXiv:2609.03450v1 Announce Type: cross Abstract: An agent that inherits six one-line memories may pull at most one archived source record before acting; a directive written into the store can steer t

PointGT: Simultaneous Geometry and Texture Editing for Point-Based Representations

arXiv:2609.03341v1 Announce Type: new Abstract: We present PointGT, a point-based 3D representation that enables simultaneous editing of object geometry and appearance. Existing reconstruction and vie

Population-Calibrated Graph Screening at 835-Million-Address Scale, with Label-Free Transfer to New Chains

arXiv:2609.03036v1 Announce Type: cross Abstract: Compliance screening of blockchain addresses is, in practice, a lookup against sanctions registries plus clustering heuristics; it fails on unlabelled

researcharxiv-cs-lg

Portable Causal Fairness Across Synthetic Data Generator Families

arXiv:2609.03180v1 Announce Type: new Abstract: When a statistical agency or regulator releases synthetic data in place of sensitive records, it chooses the generator that produces the table, and can

safetyarxiv-cs-lg

PoseDreamer: Scalable and Photorealistic Human Data Generation Pipeline with Diffusion Models

arXiv:2603.28763v2 Announce Type: replace Abstract: Acquiring labeled datasets for 3D human mesh estimation is challenging due to depth ambiguities and the inherent difficulty of annotating 3D geometr

safetyarxiv-cs-cv

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

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

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-

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

model-releasesarxiv-cs-cv

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

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

model-releasesarxiv-cs-cv

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