All entries

Newton Matching for Generative Modeling: A Unified Framework for Fine-Tuning and Sampling

arXiv:2609.05727v1 Announce Type: new Abstract: We develop Newton Matching, a unified framework for fine-tuning and sampling in generative modeling. The target is piproptomu e^{au r}, where r is the r

Content type

All Blog X Post Paper YouTube Reddit GitHub

Clear filters

No-Regret Mixing of LRU and LFU with Optimal Switching Cost

DGX agent

arXiv:2609.07566v1 Announce Type: new Abstract: Caching systems often rely on simple eviction policies such as Least Recently Used (LRU) and Least Frequently Used (LFU), which perform well in compleme

model-releasesarxiv-cs-lg

9 Sept 2026

NOAH: Learning the Full Patient Journey. A Longitudinal Multimodal Time-Aware Model for Representation and Forecasting

arXiv:2609.09140v1 Announce Type: new Abstract: The digitization of healthcare has generated vast, longitudinal, and multimodal patient records over a lifetime, yet fully exploiting these data to repr

Noisy-Space Policy Gradient for Diffusion Policies in Offline Reinforcement Learning

arXiv:2609.06882v1 Announce Type: new Abstract: Diffusion policies offer a powerful and expressive parameterization for continuous control. Yet, their integration with reinforcement learning remains c

NoLoCo: No-all-reduce Low Communication Training Method for Large Models

arXiv:2506.10911v2 Announce Type: replace Abstract: Training large language models is generally done on clusters containing thousands of accelerators, communicating over a high-bandwidth interconnect.

Non-Adaptive 1-Bit Mean Estimation: Minimax Rates and the Sample-Interval Tradeoff

arXiv:2609.08564v1 Announce Type: cross Abstract: We study distributed one-dimensional mean estimation under a 1-bit communication constraint. Each agent observes one sample, drawn independently from

agentsarxiv-cs-lg

Non-Coherent Over-the-Air Federated Learning: Protocol, Convergence, and Device Scheduling

arXiv:2609.08312v1 Announce Type: cross Abstract: To mitigate the scalability bottleneck in the radio access network (RAN) in federated edge learning (FEEL), over-the-air federated learning (AirFL) ex

Nonlinear elliptic homogenization with the parametric Deep Ritz method

arXiv:2609.05778v1 Announce Type: new Abstract: Elliptic homogenization is used to determine coarse-grained properties of materials with features on small scales. When these small scale features have

Norms at a Price: Why RL-Based Alignment Can Promise Conditional Compliance at Best

arXiv:2609.07627v1 Announce Type: cross Abstract: AI agents sometimes act aligned when they infer they are being tested, and differently when not. We argue this is not an anomaly but what current trai

Not All Variables Agree: Reliability-Aware Variable-Wise Gradient Surgery for Multivariate Time-Series Forecasting

arXiv:2609.08554v1 Announce Type: new Abstract: In data-driven training, multivariate time-series forecasting is usually optimized with a scalar loss averaged over samples, variables, and horizons. Th

Applications

Not Just Oversmoothing: Detecting the Echo Chamber Effect in Graph Neural Networks

arXiv:2609.06521v1 Announce Type: new Abstract: Oversmoothing is a well-known failure mode of Graph Neural Networks (GNNs). However, most existing diagnostics rely on global aggregation measures that

applicationsarxiv-cs-lg

NOVA: Normal-Side Modeling for Training-Free Zero-Shot Video Anomaly Detection

arXiv:2609.06360v1 Announce Type: new Abstract: Training-free zero-shot video anomaly detection (ZS-VAD) leverages vision-language models (VLMs) to localize anomaly instances from a predefined anomaly

safetyarxiv-cs-cv

Novel hybrid protein scaffold gap filling using weighted machine learning ensemble, beam search, and mass-constrained reranking

arXiv:2609.05436v1 Announce Type: cross Abstract: Protein scaffold gap filling is an important computational task in protein sequence reconstruction, where missing amino acid regions must be inferred

Novel Methods for Catheter and Guidewire Segmentation in X-ray Fluoroscopy under a Federated Learning Setting

arXiv:2609.06876v1 Announce Type: new Abstract: Endovascular procedures rely on real-time manipulation of thin instruments, catheters and guidewires, under X-ray fluoroscopy guidance, where accurate v

NSFlow: End-to-End Differentiable Neuro-Symbolic Optical Flow for Visual Odometry

arXiv:2609.06074v1 Announce Type: new Abstract: Sparse optical flow provides stable inter-frame correspondence, playing a key role in Visual Odometry (VO) and Visual-Inertial Odometry (VIO). Classical

researcharxiv-cs-cv

NutriBench-Kitchen: Benchmarking Embodied AI for Nutrition Management

arXiv:2609.07135v1 Announce Type: new Abstract: An embodied kitchen assistant must do more than recognize food in isolated frames. It must track ingredient states over time and integrate visual observ

Nystrom Attention Matches Full Attention for Cross-Sectional Stock Prediction

arXiv:2609.08106v1 Announce Type: new Abstract: MASTER's inter-stock multi-head attention -- the module responsible for modeling cross-sectional stock relationships -- accounts for 42.5% of model para

Local Ai

Object-Aware Background-Controlled Editing via Weighted Velocity Guidance

arXiv:2609.06288v1 Announce Type: new Abstract: Training-free image editing steers diffusion or flow-matching generative models at inference time by modifying prompt-conditioned denoising velocities.

local-aiarxiv-cs-cv

Local Ai

Observability Analysis of Joint Steering and Extrinsic Calibration

arXiv:2609.05498v1 Announce Type: new Abstract: This technical report studies the local weak observability of a planar bicycle-model vehicle when vehicle pose, planar LiDAR extrinsic calibration, and

local-aiarxiv-cs-ro

Observability conditions for neural state-space models with eigenvalues and their roots of unity

arXiv:2504.15758v3 Announce Type: replace Abstract: We operate through the lens of ordinary differential equations and control theory to study the concept of observability in the context of neural sta

Observation Design for Certified Control Authority: Projection--Estimability Separation and Active-Face Equivalence

arXiv:2609.05962v1 Announce Type: new Abstract: A sound runtime admission gate executes only actions it can certify, and certifies only what its observations support. This paper asks how observations

researcharxiv-cs-ro

Observe Before You Alert: Adaptive Driver Alerting with Vision-Language Models

arXiv:2609.08130v1 Announce Type: new Abstract: Driver alerting from dashcam video requires sequential decision-making under partial observability: a system must decide not only whether a scene is ris

Observing Health Outcomes Using Remote Sensing Imagery and Geo-Context Guided Visual Transformer

arXiv:2602.00110v2 Announce Type: replace Abstract: Visual transformers have driven major progress in remote sensing image analysis, particularly in object detection and segmentation. Recent vision-la

safetyarxiv-cs-cv

OcclusionCBF: Backup Control Barrier Functions for Safe Navigation Among Hidden Dynamic Obstacles

arXiv:2609.06342v1 Announce Type: new Abstract: Robots navigating under occlusion may enter states from which no admissible input can avoid a dynamic obstacle once it becomes visible. We present Occlu

safetyarxiv-cs-ro

Hardware

OCTN: Neural OCT Representations for Robot-Guided Precision Intervention

arXiv:2609.06810v1 Announce Type: new Abstract: Optical coherence tomography (OCT) offers compact, contactless, micron-scale imaging suitable for intraoperative guidance, but native OCT volumes are di

hardwarearxiv-cs-ro

OdysSim: Building Foundation Models for Human Behavior Simulation

arXiv:2606.14199v2 Announce Type: replace-cross Abstract: Large language models are increasingly deployed as human simulators for interactive evaluation and social simulation. Yet helpfulness-driven p

Omni Interaction Agent Technical Report

arXiv:2609.08977v1 Announce Type: cross Abstract: In this work, we present Gander, an end-to-end model that unifies omni perception, realtime interaction, and agentic capabilities within a single fram

agentsarxiv-cs-lg

Applications

OmniNav: Robust Long-Horizon Target Navigation in Dynamic Environments

arXiv:2609.08159v1 Announce Type: new Abstract: Long-horizon target navigation requires a robot to sustain task execution across evolving observations, decisions, and physical interactions. This requi

applicationsarxiv-cs-ro

On BatchNorm Forward Modes in Value-Based Reinforcement Learning

arXiv:2609.06421v1 Announce Type: new Abstract: Batch normalization (BN) substantially improves sample efficiency in continuous-control actor-critic methods such as CrossQ, yet recent studies report p

On Generalisation Error Bounds for Transformers

arXiv:2410.11500v2 Announce Type: replace-cross Abstract: In this paper, we establish a collection of covering number bounds for linear function classes under various norm constraints on the inputs an

On the Effectiveness of the z-Transform Method in Quadratic Optimization

arXiv:2507.03404v3 Announce Type: replace Abstract: The z-transform of a sequence is a classical tool used within signal processing, control theory, computer science, and electrical engineering. It al

On-the-go Forgetting without Explicit Unlearning via ERASE

arXiv:2609.05966v1 Announce Type: new Abstract: Existing unlearning approaches typically rely on post hoc weight adaptation or distillation, leading to duplicated memory costs, degraded generalization

On the Potential of Graph Neural Networks as Metamodels for Supply Chain Optimization: Dataset, Architectures, and Directions

arXiv:2607.16769v2 Announce Type: replace Abstract: Graph Neural Networks (GNNs) have emerged as a powerful, differentiable class of learning models for graph-structured systems. Their ability to gene

On the Recall Scaling Laws in Mamba: A Theoretical and Mechanistic Study via Hashing

arXiv:2609.07681v1 Announce Type: new Abstract: Associative Recall (AR) is the cognitive ability to learn and retrieve links between items in memory. In NLP, AR is used as a benchmark for evaluating t

ONE CYLinder: A Benchmark for Graph-Based Surrogate Modeling of Unsteady Bluff-Body Flows

arXiv:2609.08947v1 Announce Type: cross Abstract: Graph-based surrogate models offer a promising route to accelerate computational fluid dynamics (CFD) simulations on unstructured meshes. However, the

One for All: Generalist Foundation Model for Cross-Sensor Skeleton Representation Learning

arXiv:2609.07078v1 Announce Type: new Abstract: For learning generalizable motion representations from large-scale unlabeled data, Self-supervised learning (SSL) has become a widely adopted methodolog

researcharxiv-cs-cv

One MLLM, One Call: Efficient Zero-Shot Vision-and-Language Navigation via Spatial-Aware Waypoints

arXiv:2609.06476v1 Announce Type: new Abstract: Vision-and-Language Navigation in Continuous Environments (VLN-CE) requires an embodied agent to navigate unseen environments by following natural langu

agentsarxiv-cs-cv

One Model, Two Worlds: Bidirectional Sonar-Optical Translation

arXiv:2609.06253v1 Announce Type: new Abstract: Translating between imaging sonar and optical cameras is valuable for underwater perception, but supporting both directions with separate models duplica

researcharxiv-cs-cv

One Perturbation Is Not Enough: Identifiability and Blind Baselines for Behavioral AI Evaluation

arXiv:2609.06190v1 Announce Type: new Abstract: Behavioral evaluations perturb an input and read the induced change in the output in order to certify that a system uses that input. We show that the nu

safetyarxiv-cs-cv

One Rate Is Not Enough: Adaptive Anisotropic Learning Rates for LoRA Fine-Tuning

arXiv:2609.05885v1 Announce Type: new Abstract: Low-rank adaptation (LoRA) has become the standard for parameter-efficient fine-tuning of large language models. Most LoRA variants follow a uniform-LR

One Shared LoRA Weight for MRI Reconstruction across Acceleration Factors

arXiv:2609.06338v1 Announce Type: new Abstract: Accelerated MRI reconstruction recovers images from undersampled k-space. However, different acceleration factors produce distinct artifact patterns. Ex

One Step, One Lead: Mitigating Higher-Order Interference in Multi-Domain Reinforcement Learning via Cross-Step Control

arXiv:2609.06469v1 Announce Type: new Abstract: Reinforcement learning (RL) across multiple domains can broaden the reasoning capabilities of large language models (LLMs), yet joint training often deg

Local Ai

Online Draft Co-Training for Speculative Decoding in Large-Scale, Long-Context RL Post-Training

arXiv:2609.07108v1 Announce Type: new Abstract: Speculative decoding accelerates rollout generation, which dominates the cost of reinforcement learning (RL) post-training. Online co-training can furth

local-aiarxiv-cs-lg

Tutorials

Online Learning with LLM Experts from Limited Feedback

arXiv:2609.05820v1 Announce Type: new Abstract: We study adaptive routing of prompts to large language model (LLM) experts to maximize response quality in an online setting with limited feedback. We f

tutorialsarxiv-cs-lg

Online, Reachability-Aware, Sampling-Based Motion Planning

arXiv:2609.09073v1 Announce Type: new Abstract: Sampling-Based Model-Predictive Control (MPC) algorithms are a flexible class of controllers used for navigation on a wide range of robotic systems. His

safetyarxiv-cs-ro

Online Signature Verification Using Augmented Path Signature and T-Mamba

arXiv:2609.08276v1 Announce Type: cross Abstract: Handwritten signature verification is vital for personal authentication across commercial and financial applications. Although deep learning methods a

Online Surrogate Repair: Decoupling High-Fidelity Feedback from Search Length in Closed-Loop Discovery

arXiv:2609.07655v1 Announce Type: new Abstract: Closed-loop AI scientists can generate candidate designs at low marginal computational cost, whereas reliable feedback may require wet-lab synthesis, ch

agentsarxiv-cs-lg

Open-Set Ego-Noise Separation for Legged-Robot Audition via Annotation-Free Adaptation and Pretrained-Model Transfer

arXiv:2609.07440v1 Announce Type: new Abstract: This paper proposes an open-set ego-noise separation framework for legged-robot audition via annotation-free adaptation and pretrained-model transfer. T

researcharxiv-cs-ro