Search: “framework”

Causal DAG Identification for Count Data via Poisson Thinning Structural Equation Models

arXiv:2609.06098v1 Announce Type: cross Abstract: Count-valued variables arise in many scientific and applied settings, yet explicit structural models that allow full identification of causal DAGs fro

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Research

CausalChapter: Improving Long-Video Chaptering with Interventional Dependency Modeling

DGX agent

arXiv:2609.08686v1 Announce Type: new Abstract: Long-form instructional videos require automatic chaptering to support browsing, navigation, and knowledge access. Recent long-context language models c

researcharxiv-cs-cv

9 Sept 2026

Certifying cooperation: a novel approach to cooperative multi-agent task generation

arXiv:2609.06586v1 Announce Type: cross Abstract: A shared reward gives agents a common objective, but leaves open when, how and even whether they must cooperate to succeed. We address these questions

agentsarxiv-cs-lg

ChatBEV: Empowering Traffic Scene Understanding and Simulation via Vision-Language Model

arXiv:2503.13938v3 Announce Type: replace Abstract: Comprehensive traffic scene understanding is a foundational capability for Intelligent Transportation Systems (ITS) underpinning applications such a

CHILD: Human-in-the-Loop OOD Detection for Safe Clinical Deployment

arXiv:2609.07188v1 Announce Type: new Abstract: Out-of-distribution (OOD) detection is critical for safe deployment of medical AI systems. Recently, test-time adaptation (TTA) has emerged as a new par

CogniDir: Combating Cognitive Malicious Comments via Adaptive Distributional Learning for Robust Fake News Detection

arXiv:2510.09712v4 Announce Type: replace Abstract: The proliferation of Large Language Models (LLMs) has enabled a new class of psychologically grounded malicious comments, shifting fake news attacks

safetyarxiv-cs-lg

Collision Snapshot Guided Time-Reversed Safety-Critical Scenario Generation

arXiv:2609.06433v1 Announce Type: new Abstract: The generation of safety-critical traffic scenarios is essential for training and evaluating autonomous vehicles. Prior approaches typically perturb the

Tutorials

Comparative Study of Anatomical and Learned Features in AI Models for Structural Brain MRI

arXiv:2609.06807v1 Announce Type: new Abstract: In this work, we comprehensively evaluate three popular feature-extraction paradigms in AI-based neuroimaging modeling: (1) computation of anatomical su

tutorialsarxiv-cs-cv

ComVLA: Communication-Aware Split Inference for VLA Models in 6G-Connected Robotics

model-releasesarxiv-cs-ro

arXiv:2609.07838v1 Announce Type: new Abstract: Connected robotics is an emerging 6G application where mobile robots follow natural-language instructions to manipulate physical objects. The Vision-Lan

Connectome-to-Function: Conditional Generative Latent Representations for Reservoir Computing

arXiv:2609.06093v1 Announce Type: new Abstract: Connectomes, graph-level maps of neurons and their synaptic connections, provide a structural basis for understanding how brain circuits support functio

local-aiarxiv-cs-lg

ContactWorld: What Representations Matter in Vision-Tactile World Models for Contact-Rich Manipulation

arXiv:2606.13877v2 Announce Type: replace Abstract: Contact-rich manipulation requires world models to capture complex interaction dynamics from heterogeneous visual and tactile observations, yet the

applicationsarxiv-cs-ro

ContextFlow: In-Context Flow Matching for Robot Manipulation

arXiv:2609.06852v1 Announce Type: new Abstract: Although highly effective in vision and language domains, applying in-context learning to robotics remains challenging. Existing autoregressive in-conte

researcharxiv-cs-ro

CONTHER: Context-Aware Reinforcement Learning for Robotic Manipulation with Sparse Rewards

arXiv:2503.15895v2 Announce Type: replace Abstract: This paper investigates whether sequential context improves goal-conditioned Reinforcement Learning in sparse-reward manipulation tasks. While Hinds

Continual Policy Consolidation for Lifelong Robot Learning

arXiv:2601.22475v2 Announce Type: replace Abstract: Building a generalist robot policy requires continuously integrating new skills while preserving previously acquired behaviors. Directly optimizing

safetyarxiv-cs-lg

ControlTac: Scaling Tactile Data with Physically Controlled Tactile Image Generation

arXiv:2505.20498v3 Announce Type: replace Abstract: Vision-based tactile sensing is widely used in perception, reconstruction, and robotic manipulation, yet collecting large-scale tactile data remains

applicationsarxiv-cs-cv

CoordFormer: Give Me Any Coordinates and I Will Give You Labels

arXiv:2609.08660v1 Announce Type: new Abstract: Semantic segmentation on very-high-resolution images remains challenging due to the high computational cost and the difficulty of capturing fine-grained

CoRe-SAM3: Conditional Semantic--Visual Reconciliation for SAM3 Crack Segmentation

arXiv:2609.05816v1 Announce Type: new Abstract: Crack segmentation requires a model to recognize target semantics while accurately recovering thin, low-contrast, and topologically continuous local str

CoRL: Co-Evolutionary Reinforcement Learning for Adaptive Indirect Prompt-Injection Attacks and Defenses

model-releasesarxiv-cs-lg

arXiv:2609.07529v1 Announce Type: new Abstract: Tool-augmented language agents are vulnerable to indirect prompt injection (IPI). Unlike direct prompt injection, IPI hides adversarial instructions in

CR-VLA-Force: Learning Control-aware Compliance VLA Model for Robust Contact-rich Robotic Manipulation

arXiv:2609.05832v1 Announce Type: new Abstract: Integrating visuomotor policies or Vision-Language-Action (VLA) models with force/torque (F/T) perception has demonstrated significant progress in imita

CST-WM: A Causally Structured World Model for Embodied Visual Tracking

arXiv:2609.06302v1 Announce Type: new Abstract: Embodied visual tracking requires a robot not only to react to the current view, but to choose actions that preserve or recover future evidence of a mov

safetyarxiv-cs-cv

CUNO: Curriculum and Preference Optimization for Stable Graph Unlearning under Mass Deletion

arXiv:2609.08244v1 Announce Type: new Abstract: Graph unlearning removes the influence of designated training data from a trained graph model without retraining from scratch. However, existing methods

CUSP: Decomposable Collective Uncertainty for Multi-Agent Multimodal Reasoning

arXiv:2609.05708v1 Announce Type: cross Abstract: Aggregating heterogeneous vision-language models (VLMs) can improve multimodal reasoning, but neither an individual model's confidence nor that of the

agentsarxiv-cs-lg

CVT-GS: Learning to Simplify 3D Gaussian Splatting with Centroidal Voronoi Tessellation

arXiv:2609.08730v1 Announce Type: new Abstract: While 3D Gaussian Splatting (3DGS) has emerged as a powerful representation for real-time novel view synthesis, rendering high-fidelity scenes often rel

researcharxiv-cs-cv

DAGLFNet: Deep Feature Attention Guided Global and Local Feature Fusion for Pseudo-Image Point Cloud Segmentation

arXiv:2510.10471v3 Announce Type: replace Abstract: Environmental perception systems are crucial for high-precision mapping and autonomous navigation, with LiDAR serving as a core sensor providing acc

agentsarxiv-cs-cv

DART: Distributional Adversarial Recurrent Training for Algorithm Learning

arXiv:2609.05988v1 Announce Type: new Abstract: Recurrent reasoning models (RRMs) can solve structured problems, achieving easy-to-hard generalization through iterative computation in hidden space. Th

local-aiarxiv-cs-lg

Data Efficient Sample Selection for In-Context Learning

arXiv:2609.06670v1 Announce Type: new Abstract: The In-context learning (ICL) paradigm aids large language models (LLMs) to adapt to new tasks without need for fine-tuning. However, selecting an optim

Data Quality Rule Generation with LLMs

arXiv:2609.06053v1 Announce Type: new Abstract: The validation of data, such as customer and employee data, is an important task in many organizations. Errors in data can have severe consequences. For

applicationsarxiv-cs-lg

DC-Gen: Post-Training Diffusion Acceleration with Deeply Compressed Latent Space

arXiv:2509.25180v3 Announce Type: replace Abstract: Existing text-to-image diffusion models excel at generating high-quality images, but face significant efficiency challenges when scaled to high reso

safetyarxiv-cs-cv

Decentralized Safe Multi-Agent Reinforcement Learning via Predictive Shielding

arXiv:2609.07618v1 Announce Type: cross Abstract: Environments are increasingly populated by multiple robots performing independent tasks with limited prior knowledge of each other. Deploying such mul

Deception in Reach-Avoid Game with Unknown Heterogeneous Attackers Speed Information

arXiv:2609.06953v1 Announce Type: cross Abstract: This letter investigates a reach-avoid game involving two Attackers and one Defender, where the Attackers aim to maximize the number reaching the targ

researcharxiv-cs-ro

Dex-X: Learning Visual-Tactile Dexterous Manipulation From Human Videos with Simulated Interaction

arXiv:2609.07747v1 Announce Type: new Abstract: Human videos are an abundant source of dexterous manipulation behaviors, but they lack tactile information that is crucial for contact-rich interaction.

Differentiable Causal Discovery for Singular Linear Models under Confounding

arXiv:2601.01368v2 Announce Type: replace Abstract: Score-based causal discovery in the presence of unobserved confounders requires both a consistent scoring criterion and an efficient search over gra

applicationsarxiv-cs-lg

Diffusion Model in Latent Space for Medical Image Segmentation Task

arXiv:2512.01292v4 Announce Type: replace Abstract: Medical image segmentation is crucial for clinical diagnosis and treatment planning. Traditional methods typically produce a single segmentation mas

researcharxiv-cs-cv

Directed mixed membership stochastic blockmodel

arXiv:2101.02307v4 Announce Type: replace-cross Abstract: Mixed membership modeling for undirected networks has been extensively explored in network science over the past few years. Despite the substa

Disentangling Steering Vectors

arXiv:2609.07037v1 Announce Type: new Abstract: Activation steering has emerged as a lightweight, inference-time approach to control the behavior of Large Language Models (LLMs). However, traditional

Distributed Dexterous Manipulation with Spatially Conditioned Multi-Agent Transformers

arXiv:2609.06930v1 Announce Type: cross Abstract: Distributed Dexterous Manipulation (DDM) is a novel paradigm that presents significant control challenges due to high action-space redundancy, inter-r

agentsarxiv-cs-lg

Do Input-Level Defenses Transfer to Observation-Level Attacks on VideoLLMs?

arXiv:2609.08331v1 Announce Type: new Abstract: Video Large Language Models (VideoLLMs) are increasingly deployed in safety-critical applications such as content moderation and video analytics. To pro

local-aiarxiv-cs-cv

Do Reasoning Representations Help Humans Evaluate LLM Outputs?

arXiv:2609.09038v1 Announce Type: new Abstract: Reasoning representations are increasingly used as explanations for large language model outputs. Yet they are typically evaluated with model-centric cr

Dreaming in Flow: Generative Grounding Feedback for Self-Evolving Unified Multimodal Models

arXiv:2609.08282v1 Announce Type: new Abstract: Unified multimodal models integrate visual understanding and generation within a single network, yet the two capabilities are commonly optimized as sepa

researcharxiv-cs-cv

DriftParking: Trajectory Modeling via Drifting Field for End-to-End Automated Parking

arXiv:2609.06923v1 Announce Type: new Abstract: Automated parking requires generating complete and executable trajectories in highly constrained spaces with low tolerance for goal pose error. Existing

DriveZero: End-to-End Driving Beyond Human Demonstrations

arXiv:2609.06055v1 Announce Type: new Abstract: Most end-to-end autonomous-driving systems learn by imitating human driving logs, leaving their learned behavior constrained by the quality and behavior

Driving is a Game: Combining Planning and Prediction with Bayesian Iterative Best Response

arXiv:2512.03936v2 Announce Type: replace Abstract: Autonomous driving planning systems perform nearly perfectly in routine scenarios using lightweight, rule-based methods but still struggle in dense

agentsarxiv-cs-ro

DroneGround: Open-Vocabulary Drone Payload Characterization Using Synthetic Data and Grounded Vision-Language Models

arXiv:2609.07780v1 Announce Type: new Abstract: Automated drone surveillance has become increasingly important for public safety, critical infrastructure protection,and restricted airspace monitoring.

DrugReason: Dynamic Multi-View Reasoning over Knowledge Graph and Language Evidence for Drug Repurposing

arXiv:2609.06779v1 Announce Type: new Abstract: Drug repurposing aims to identify new therapeutic uses for existing compounds and, compared with de novo drug discovery, offers a faster and more cost-e

DSE-VTG: Dual-Side Enhancement for Training-Free Video Temporal Grounding

arXiv:2609.08850v1 Announce Type: new Abstract: Text-guided Video Temporal Grounding (VTG) aims to localize the relevant segments in an untrimmed video based on text queries, yet collecting dense temp

Dual-Layer Semantic-Spatial Belief Mapping for Aerial Object Goal Navigation

model-releasesarxiv-cs-ro

arXiv:2609.08164v1 Announce Type: new Abstract: Aerial Object Goal Navigation (ObjectNav) requires an unmanned aerial vehicle (UAV) to locate a described target in an unknown outdoor environment using

DualPathOcc: Dual-Resolution BEV Encoder for 3D Occupancy Prediction

arXiv:2609.06370v1 Announce Type: new Abstract: Predicting 3D occupancy from multi-view images requires preserving geometric detail during 2D-to-3D lifting while reasoning over sparse, volumetric scen

local-aiarxiv-cs-cv

EcoFair: Energy-Efficient Inference Routing for Edge AI under Data Degradation

arXiv:2603.26483v2 Announce Type: replace Abstract: Medical edge-AI systems must operate under a difficult tension: delivering reliable diagnostic inference while running on devices with limited batte

local-aiarxiv-cs-lg