All entries

OneShot: Label-Efficient Detector Weighting for Time Series Anomaly Detection

arXiv:2610.16219v1 Announce Type: cross Abstract: Time series (TS) anomaly detection (AD) lacks a universal detector, with the best choice determined by the series rather than by the algorithm. Existi

Source

Human DGX agent

Content type

All Blog X Post Paper YouTube Reddit GitHub

Clear filters

Research

Online Individualized Treatment Selection under Partial Feedback: False Discovery Control and Minimax Optimality

DGX agent

arXiv:2610.16464v1 Announce Type: cross Abstract: We study online individualized treatment selection when only the outcome under the assigned treatment is observed. Our objective is to preferentially

researcharxiv-cs-lg

14 Oct 2026

Online learning of neural state-space models

arXiv:2607.17614v2 Announce Type: replace-cross Abstract: Recent advances in deep-learning-based nonlinear system identification have led to encoder-based estimation of neural state-space (ANN-SS) mod

Online LLM Personalization from Pairwise Preferences Across Users

arXiv:2610.17203v1 Announce Type: new Abstract: Large language models can answer the same question in different styles, but users' preferred styles are hidden and vary across users and questions. We s

Only What You've Signed up For: Secure Workflows with Constrained Diffusion Agents

arXiv:2610.15177v1 Announce Type: new Abstract: As agentic AI systems take on increasingly complex and consequential tasks, securing them against prompt injection becomes critical. Existing defenses o

Open Foundation Models in Healthcare: Challenges, Paradoxes, and Opportunities with GenAI Driven Personalized Prescription

arXiv:2502.04356v2 Announce Type: replace-cross Abstract: In response to the success of proprietary Large Language Models (LLMs), there is a growing interest in developing open, non-proprietary LLMs a

Open-o3-Video-v2: Seek, Ground, and Reason with Verifiable Spatio-Temporal Evidence

model-releasesarxiv-cs-cv

arXiv:2610.17656v1 Announce Type: new Abstract: Reliable video reasoning requires models to produce correct answers and expose supporting visual evidence. Open-o3-Video enables verifiable reasoning by

OpenDGO: Benchmarking Multi-Agent Organizations Across Diverse Application Domains

arXiv:2610.17143v1 Announce Type: new Abstract: Large language model (LLM)-based multi-agent systems are increasingly applied across diverse domains, yet different applications require different forms

OpenMobile-2: Building Versatile Mobile Agents with Scalable Environments and App-Native Tools

arXiv:2610.14157v1 Announce Type: cross Abstract: We introduce OpenMobile-2, a near-frontier mobile agent with fully open training environments and recipes. We make three key advances: (1) Diverse env

agentsarxiv-cs-cv

Operator Surrogates

arXiv:2610.18210v1 Announce Type: new Abstract: This chapter provides a mathematical introduction to operator surrogates, which are finite-parametric maps approximating complex (typically nonlinear) o

OPMD: On-Policy Mechanism Distillation Beyond Output Supervision

arXiv:2610.14022v1 Announce Type: new Abstract: On-policy distillation (OPD) improves language-model by supervising a student on prefixes sampled from its own policy, but its supervision remains confi

Tutorials

OPTIC-ER: A Reinforcement Learning Framework for Real-Time Emergency Response and Equitable Resource Allocation in Underserved African Communities

arXiv:2508.12943v3 Announce Type: replace-cross Abstract: Public service systems in many African regions suffer from delayed emergency response and spatial inequity, causing avoidable suffering. This

tutorialsarxiv-cs-lg

Optimal Online Regret for Context-Lumpable Bandits

arXiv:2610.18258v1 Announce Type: new Abstract: We study stochastic contextual bandits with an unknown context-lumpable structure. The S contexts are partitioned into r blocks, and every arm has the s

Local Ai

Optimal Transport Aggregation for Distributed Mixture-of-Experts

arXiv:2312.09877v3 Announce Type: replace Abstract: This paper considers the setting in which local Mixture-of-experts (MoE) models are fitted independently on distributed datasets and must be combine

local-aiarxiv-cs-lg

Optimal Transport PAC-Bayes Bounds

arXiv:2610.16560v1 Announce Type: new Abstract: PAC-Bayesian generalization bounds are among the strongest distribution-dependent guarantees in statistical learning and are typically expressed in term

Optimizing Team Behavior via Extremum-seeking Control in Multi-agent Reinforcement Learning

arXiv:2610.18808v1 Announce Type: cross Abstract: In cooperative Multi-Agent Reinforcement Learning (MARL), agents must learn to coordinate their actions to achieve shared objectives. A key indicator

agentsarxiv-cs-ro

Orientation Estimation of Cryo-EM Images for Molecules with Arbitrary Symmetry by Semidefinite Programming

arXiv:2610.18691v1 Announce Type: new Abstract: This paper extends the non-unique games framework introduced in Bandeira et al. (2020) to address symmetric structures. Precisely, we consider the probl

Over-parameterization Is Neither Robust nor Fragile in Matrix Sensing: A Geometric Theory of Solution Robustness

arXiv:2610.18318v1 Announce Type: new Abstract: Over-parameterization is ubiquitous in modern machine learning, yet its effect on robustness remains poorly understood. We study this question in matrix

Overcoming Catastrophic Forgetting: Continual Learning for Radar Waveform Classification

arXiv:2610.18404v1 Announce Type: cross Abstract: Automated radar waveform recognition classifies the waveform and modulation type of a received signal. As new waveform variants appear over time, a de

Local Ai

P-BFM: Perceptive Behavioral Foundation Models for Humanoid Control via Unsupervised Reinforcement Learning

arXiv:2610.15833v1 Announce Type: new Abstract: Behavioral foundation models (BFMs) provide humanoids with reusable repertoires of whole-body behaviors; in real-world humanoid control, the same behavi

local-aiarxiv-cs-ro

PA-LOCO: Learning Perturbation-Adaptive Locomotion for Quadruped Robots

arXiv:2407.04224v2 Announce Type: replace Abstract: Numerous locomotion controllers have been designed based on Reinforcement Learning (RL) to facilitate blind quadrupedal locomotion traversing challe

Hardware

PACC: Protocol-Aware Cross-Layer Compression for Compact Network Traffic Representation

arXiv:2602.08331v2 Announce Type: replace-cross Abstract: Network traffic classification underpins network security and management but faces growing challenges from pervasive encryption and evolving p

hardwarearxiv-cs-lg

PACE: Temporal-Role Tactile Conditioning for Chunk-Based Insertion Policies

model-releasesarxiv-cs-ro

arXiv:2610.16308v1 Announce Type: new Abstract: Contact-rich insertion requires robots to combine visual alignment with tactile adjustment. However, existing visuotactile chunk-based policies collapse

PaceReasoner: Label-Free Learning to Answer Briefly from Long Reasoning

arXiv:2610.17900v1 Announce Type: cross Abstract: Extended chain-of-thought reasoning improves problem-solving performance but increases inference cost and latency, motivating more accurate answers wi

PACT: Posture-Aligned Co-Design Technique for Hand Configuration and Retargeting in Multi-Finger Imitation Learning

arXiv:2610.18443v1 Announce Type: new Abstract: The fidelity of teleoperated demonstrations used for imitation learning is determined by how faithfully a robot hand can replicate the operator's postur

PanoLAM: Large Avatar Model for Gaussian Full-Head Synthesis from One-shot Unposed Image

arXiv:2509.07552v3 Announce Type: replace Abstract: We present a feed-forward framework for static Gaussian full-head reconstruction and novel-view rendering from a single unposed image. Unlike previo

Panoramic Out-of-Distribution Segmentation

arXiv:2505.03539v4 Announce Type: replace Abstract: Panoramic imaging enables capturing 360{eg} images with an ultra-wide Field-of-View (FoV) for dense omnidirectional perception, which is critical to

agentsarxiv-cs-cv

Parameter-Efficient Multimodal Brain Tumor Segmentation With Stage-Wise Shared Adapters

model-releasesarxiv-cs-cv

arXiv:2610.18571v1 Announce Type: new Abstract: Different MRI modalities reveal different parts of a brain tumor. Segmentation models combine information from these modalities to outline tumor regions

PathLift: Depth-Conditioned Metric Lifting for Visual-Navigation Priors

arXiv:2610.16103v1 Announce Type: new Abstract: Generalist visual-navigation policies tell a robot which direction to go but not how far. A separate lifting step is needed at deployment to convert the

PathPilot: An Adaptive Question-Conditioned Multi-Magnification Navigation Agent for Whole-Slide Image Analysis

arXiv:2610.16734v1 Announce Type: new Abstract: The diagnostic capability of WSI agents relies on effective slide navigation to identify diagnostically relevant regions and efficiently integrate evide

agentsarxiv-cs-cv

PATTERN Can Be Solved from Memory: Generator-Aware Baselines for Synthetic Node Classification

arXiv:2610.15020v1 Announce Type: new Abstract: PATTERN, a synthetic node-classification benchmark still used to rank graph networks, plants a random subgraph in a random host graph and asks which nod

PC7 Model: Dynamics of Expanding and Contracting Soft Robots using Piece-wise Constant 7D Deformation Based on Similarity Transformation Group

model-releasesarxiv-cs-ro

arXiv:2610.14586v1 Announce Type: new Abstract: This paper proposes a novel soft robot model named piece-wise constant 7D deformation (PC7) model that can represent expanding and contracting deformati

Perception Beyond the Visible: Incompleteness-Aware Scene Understanding for Robot Autonomy

arXiv:2610.18670v1 Announce Type: cross Abstract: Reliable robot autonomy requires reasoning about the world from partial and noisy observations. However, many perception systems model only visible sc

Perfect Reconstruction Cannot Show Whether Two Codebooks on a VAE's Latent Space Agree

arXiv:2610.14012v1 Announce Type: new Abstract: Many systems read the latent space of a variational autoencoder (VAE) as discrete codes, and rely on each latent being read back as the code it was writ

Persistence Positional Encoding: A Minimal Transformer Recipe for Persistence Diagrams

arXiv:2610.17848v1 Announce Type: new Abstract: Persistence diagrams (PDs) represent multiscale topological information as multisets of birth-death pairs (b,d). How best to use them with Transformers,

Persistent LLM Orchestration of Heterogeneous Robot Teams via Partial Delegation to Multi-Agent Protocols

arXiv:2610.15224v1 Announce Type: cross Abstract: Natural language specifications let non-expert users task robot teams, and large language models (LLMs) have recently proven effective at grounding su

agentsarxiv-cs-ro

Personalized LLM Recommendation with Semantic IDs: Autoregressive Beam Search vs. Multi-Token Prediction

arXiv:2610.15864v1 Announce Type: cross Abstract: Large-scale personalization is shifting from embedding-table retrieval to LLM-based recommenders that generate the Semantic ID (SID) of the item to re

Personalized Physiological-Model-Based Neural Network for Glucose Forecasting in Type 1 Diabetes

arXiv:2610.15456v1 Announce Type: cross Abstract: Accurate, physiologically consistent glucose forecasts could help people with type 1 diabetes respond to impending excursions. We applied our physiolo

PerturbBridge: Direct Phenotypic Supervision for Cellular Perturbation Generation

arXiv:2610.17066v1 Announce Type: new Abstract: Virtual cell models should capture phenotypes induced by specific perturbations. However, image-based models are typically trained and evaluated to matc

Pessimistic Latent Task-aware Optimization for Robust Offline Meta-Reinforcement Learning

arXiv:2610.15428v1 Announce Type: new Abstract: Context-based offline meta-reinforcement learning (COMRL) has advanced almost entirely through better task representations, which separate task identity

PGSB: Pretrained-Guided Shared Basis for LoRA Model Merging

arXiv:2610.14558v1 Announce Type: new Abstract: Large pretrained models have achieved remarkable success across diverse domains, yet adapting them to multiple downstream tasks remains challenging: ful

Phase-Conditioned Affordance Fields for SE(3)-Equivariant Visuomotor Policies

arXiv:2610.17281v1 Announce Type: new Abstract: SE(3)-equivariance provides a useful geometric inductive bias for visuomotor policies, encoding that actions covary with rigid transformations of the ob

Phase-Gated Constrained Reinforcement Learning for End-to-End Capture and Detumbling of Non-Cooperative Targets

arXiv:2610.15491v1 Announce Type: new Abstract: Active debris removal requires a servicer spacecraft to approach a non-cooperative tumbling object, capture it, and arrest its rotation. This sequence i

PhaseQuant: Mitigating Activation Quantization Collapse via Residual-Stream Dynamics

arXiv:2606.12487v2 Announce Type: replace Abstract: Post-training quantization (PTQ) is essential for efficient large language model (LLM) inference, but reliably quantizing activations remains challe

Phone Segmentation and Recognition through Phonological Activation Mapping

arXiv:2607.09020v2 Announce Type: replace-cross Abstract: Phone segmentation and recognition are inherently related tasks, yet modern approaches typically model them separately. We argue that phonetic

Physical-Field-Bridged Material Parameter Inference from Multi-View Deformation Videos

model-releasesarxiv-cs-cv

arXiv:2610.17591v1 Announce Type: new Abstract: Estimating simulation-ready material parameters from visual observations is crucial for deformable-object manipulation but challenging because material

Physical SLAM: Physics-aware Simultaneous Localisation and Mapping

model-releasesarxiv-cs-ro

arXiv:2610.17260v1 Announce Type: new Abstract: To reconstruct a predictive 4D representation of the physical world from monocular video captured by a moving camera, we jointly reconstruct dynamic Gau

Physics-Aware Deepfake Detection via Distance-Speech Consistency

arXiv:2610.18485v1 Announce Type: new Abstract: Recent progress in generative models has made talking-head deepfakes highly realistic and easy to create, jeopardizing public trust. However, most audio