Search: “research”

GestAdapt: Workspace-Conditioned Co-Speech Gesture Generation for Humanoid Robots

arXiv:2609.38400v1 Announce Type: cross Abstract: Co-speech gestures for robots must adapt not only to speech and embodiment, but also to the workspace available for performing the motion. Since the s

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Graph Anomaly Detection as Finite-Horizon Control: Training-Free Scoring via Empirical Bayes

DGX agent

arXiv:2609.38424v1 Announce Type: new Abstract: Node-level graph anomaly detection (GAD) identifies nodes whose attributes and interactions deviate from dominant graph regularities. Existing GAD model

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1 Oct 2026

Graph Residual Conjugate Diffusion: SNR-Equalized Heat Flow for Graph Signals

arXiv:2609.39658v1 Announce Type: new Abstract: Diffusion models generate data by reversing a forward corruption process that typically approaches a simple Gaussian prior. Recent work has extended thi

GraphToxin: Reconstructing Full Unlearned Graphs from Graph Unlearning

arXiv:2511.10936v3 Announce Type: replace-cross Abstract: Graph unlearning (GU) has emerged as a promising solution to comply with 'the right to be forgotten' regulations by enabling the removal of se

H-WM: Robotic Task and Motion Planning Guided by Hierarchical World Model

arXiv:2602.11291v3 Announce Type: replace Abstract: World models are becoming central to robotic planning and control by predicting future state transitions. Existing approaches mainly rely on visual,

Halluscoring 2026: The first shared task on llms hallucination detection and answer verification

arXiv:2609.38355v1 Announce Type: new Abstract: We present HalluScoring 2026, a shared task for evaluating hallucination detection and factual verification in Arabic question answering under challengi

HAPMoE: Heterogeneity-Aware Automatic Parallelism Planning for Mixture-of-Experts Models Training

arXiv:2609.39350v1 Announce Type: cross Abstract: As model sizes continue to scale, distributed training has become inevitable. Automatic parallelization techniques can derive efficient training paral

Hard-Region Supervision: #1 on the Waymo Open Dataset 2D Video Panoptic Segmentation Leaderboard

arXiv:2609.38714v1 Announce Type: new Abstract: We describe our winning entry to the Waymo Open Dataset 2D Video Panoptic Segmentation Challenge. The task asks for a semantic class at every pixel of e

How Accurate Is Accurate Enough?

arXiv:2609.38785v1 Announce Type: new Abstract: How accurate must a numerical approximation be within a learning system? Primitive error alone cannot answer this question: errors of the same magnitude

How Many Samples Are Enough for Learning Across Domains?

arXiv:2609.39336v1 Announce Type: new Abstract: Understanding the fundamental mechanisms of learning is essential for designing systems with strong generalization. Recent studies have shown that incre

How Much Is an AI Token Worth? Scaling Laws for Wild AI-Generated Web Text

arXiv:2609.40295v1 Announce Type: new Abstract: Web text makes up the majority of pretraining data and is increasingly AI-generated. After applying FineWeb quality filtering, we find that 27.5% of tok

How Should Diffusion Language Models Edit Code?

arXiv:2609.38257v1 Announce Type: cross Abstract: Code editing requires a model to decide where to make changes, generate the new content, and preserve everything else. We study how masked diffusion l

Hyperspectral Image Dataset for Benchmarking on Salient Object Detection

arXiv:1806.11314v3 Announce Type: replace Abstract: Many works have been done on salient object detection using supervised or unsupervised approaches on colour images. Recently, a few studies demonstr

I Have a Stream: Making Self-Supervised Learning Work on Continuous Video

arXiv:2609.40333v1 Announce Type: new Abstract: Self-supervised learning draws inspiration from infant visual development, yet standard training pipelines bear little resemblance to it: images are ind

Image Classifiers are Efficient Self-Supervised Video Representation Learners

arXiv:2609.40347v1 Announce Type: new Abstract: We introduce VideoMSN, a Masked Siamese Network framework for efficient self-supervised spatio-temporal representation learning in videos. Instead of re

Inferring Causal Relations between Two Sequences of Events with Language Models

arXiv:2609.39406v1 Announce Type: new Abstract: Causal AI is a branch of Artificial Intelligence which helps understand and reason about cause and effect relationships, not just patterns or correlatio

Interestingly, just overheard in @swyx podcast interviewing an OpenAI engineer, their Decision API is literally built on GPT-6 Luna adding a…

researchsebastian-raschka--x

During a Swyx podcast interview in October 2026, an OpenAI engineer revealed that the company’s Decision API is built on the GPT‑6 Luna model, augmented with a dedicated decision head. This configurat

Interpretable but Fragile? Robustness of Concept Bottlenecks under Geometric-Semantic Perturbations

arXiv:2609.38625v1 Announce Type: cross Abstract: Concept Bottleneck Models (CBMs) are designed to provide interpretable intermediate representations, yet how such bottlenecks affect robustness remain

Invariant Shape Analysis of Surfaces with Spherical Topology

arXiv:2609.39567v1 Announce Type: new Abstract: Spherical harmonic descriptors of closed 3D shapes depend on the parameterization, the pose and the scale of the surface, and the standard rotation-inva

IronMind: Scaling Humanoid Dexterous Manipulation via Camera-Space Ego-Centric Pretraining

arXiv:2609.39403v1 Announce Type: new Abstract: Egocentric human video offers a scalable data source for dexterous manipulation, yet using it to train humanoid robots presents two challenges: (1) an e

Is This Evidence Decision-Critical? Learning to Verify Rule-Governed Decisions

arXiv:2609.39608v1 Announce Type: new Abstract: Rule-based reasoning, as in eligibility checks and contract reviews, requires language models to assess evidence against individual conditions and combi

Joint Supervised and Self-Supervised Training with Acquisition-Robust Techniques for Accelerated 4D Flow MRI Reconstruction

arXiv:2609.38644v1 Announce Type: cross Abstract: 4D flow MRI measures time-resolved, three-directional blood velocity but requires long acquisition times, and its diagnostic signal is carried by the

K2P: Label-Free Knowledge to Prompt Distillation

arXiv:2609.38898v1 Announce Type: cross Abstract: Knowledge distillation can transfer reasoning from stronger teachers to frozen students through reusable prompts, but avoiding weight updates does not

Kinematic signatures of impairment: Detecting alcohol intoxication in e-scooter riders using sensor data and machine learning

arXiv:2609.38276v1 Announce Type: new Abstract: Alcohol intoxication is a leading contributor to fatal and severe-injured e-scooterist crashes. Current countermeasures, such as temporal restrictions o

Lasting Effects of Abstract Pretraining Beyond Perplexity

arXiv:2609.38764v1 Announce Type: new Abstract: Language models are typically pretrained from random initialization. Recent work challenges this convention, showing that a brief warm-up on abstract, a

Learning-Based Progressive Barrier Control for Robot Manipulators with Initial Errors Outside Prescribed Tracking Bounds

arXiv:2503.14669v2 Announce Type: replace Abstract: Robot manipulators may start a new task with a tracking error larger than the prescribed tolerance. Conventional barrier controllers generally requi

Learning Beyond Full Imitation: Task-Preserving Knowledge Distillation

arXiv:2609.39338v1 Announce Type: new Abstract: Knowledge distillation transfers knowledge by encouraging a student to match a teacher's predicted class probabilities. These probabilities express not

Learning Continuous Neural Representation of Stochastic Hybrid Systems

arXiv:2609.38893v1 Announce Type: cross Abstract: A stochastic hybrid system (SHS) is governed by a stochastic differential equation (SDE) describing the continuous dynamics and a Markov reset kernel

Learning Infinite-Horizon Average-Reward CMDPs via State Augmentation

arXiv:2609.39093v1 Announce Type: new Abstract: We study infinite-horizon average-reward constrained Markov decision processes (CMDPs) under the weakly communicating assumption. Existing high-probabil

Learning Normal Diffusion Dynamics for Backdoor Defense in Text-to-Image Models

arXiv:2609.39548v1 Announce Type: cross Abstract: Backdoor attacks pose a serious threat to the secure deployment of text-to-image (T2I) diffusion models. Existing defenses typically detect backdoors

Learning Semantic Inpainting for Animatable Gaussian Head Avatars

arXiv:2609.38343v1 Announce Type: new Abstract: We present SInGA, a novel method for learning Semantic Inpainting for animatable Gaussian head Avatars from a single image. Existing avatar approaches o

Less is more: error-distance scaling relation for data-efficient kilometer-scale downscaling of extreme heat

arXiv:2609.40140v1 Announce Type: cross Abstract: Extreme heat is where urban adaptation needs kilometer-scale data the most, but the simulations training a downscaler can cost more than they save, an

Let the Carrier Carry the Attack: Preserving the Subject in Adversarial Image Generation

arXiv:2609.39723v1 Announce Type: cross Abstract: Strong unrestricted adversarial attacks can distort the primary object of an image, hereafter referred to as the subject. To preserve subject integrit

LongTake: Learning to Sustain Dynamics in Long-Horizon Video Generation

arXiv:2609.38562v1 Announce Type: new Abstract: World models, game simulators, and long-take video creation require coherent scene evolution and sustained dynamics over extended durations. Autoregress

LoopVL: Recurrent Visual Intelligence

arXiv:2609.38426v1 Announce Type: cross Abstract: We introduce LoopVL to study whether Loop Transformers can be effectively extended to vision- language models. LoopVL combines Module-Loop and Model-L

Lower Bounds for Linear-Oracle Online Learning

arXiv:2609.38375v1 Announce Type: cross Abstract: Can a constant number of linear minimizations per round improve on the T^{3/4} regret rate of online Frank-Wolfe on general convex sets? Weibel et al.

MADGRAV: a multilevel anomaly-detection pipeline for gravitational-wave searches applied to LIGO data

arXiv:2609.39583v1 Announce Type: cross Abstract: We present the results of extbf{MADGRAV}, a deep-learning-based search for high-mass compact binary coalescences, applied to the data collected by the

Magic-W0: A Structured World-Action Foundation Model for Physical Intelligence

arXiv:2609.39870v1 Announce Type: new Abstract: World-action models (WAMs) augment robot policies with action-conditioned environment dynamics, yet existing approaches largely rely on future observati

MAGiDiff: Sampling the Photospheric Vector Field from UV/EUV Filtergrams

arXiv:2609.40043v1 Announce Type: cross Abstract: Photospheric vector magnetic fields are foundational to modeling, understanding, and forecasting solar activity. These data are usually produced by in

Magnetic based In-situ Self 3D Pose Estimation for a Modular Soft Tendon-Driven Continuum Robot via IMU-Fusion

arXiv:2609.39950v1 Announce Type: new Abstract: Continuum robots are well suited for gentle manipulation because of their inherent compliance and ability to adapt to complex environments. However, the

Manifold-Aware Perturbations for Constrained Generative Modeling

arXiv:2601.23151v2 Announce Type: replace Abstract: Generative models have enjoyed widespread success in a variety of applications. However, they encounter inherent mathematical limitations in modelin

Marking Contour Tones in Yoruba

arXiv:2609.38627v1 Announce Type: new Abstract: Yoruba is a tonal language in which contour tones pose persistent orthographic challenges. These are especially notable for personal names and lexical i

Markovian Dynamics Enforcer: Feasibility Preserving Correction on Learned Dynamics Manifolds

arXiv:2609.39888v1 Announce Type: new Abstract: Neural trajectory predictors can reach low prediction error while violating dynamics, actuator limits, or state constraints, especially when controls ar

Masked Swingers: Harnessing Data Augmentation to Advance Autoencoders for Self-Supervised Learning

arXiv:2609.38278v1 Announce Type: new Abstract: Self-supervised learning (SSL) removes the need for annotations and makes models that are capable across more domains than supervised learning. The auto

MatGPTQ: Efficient and Accurate Inference over Nested Quantized Models

arXiv:2602.03537v2 Announce Type: replace Abstract: Matryoshka Quantization (MatQuant), Any-Precision-LLM (AP) and AnyBCQ (AB) are recent quantization approaches showing that a single integer-quantize

MeanVoiceFlow2: Joint Optimization of Mean Flow and Content Encoder for Fast One-Step Zero-Shot Voice Conversion

arXiv:2609.40087v1 Announce Type: cross Abstract: Flow-matching approaches to voice conversion (VC) have gained attention owing to their high speech quality and strong speaker similarity. Among them,

MegaAvatar: Controllable Talking Avatar Generation

arXiv:2609.39273v1 Announce Type: new Abstract: This report presents extbf{MegaAvatar}, a controllable talking avatar generation framework built on top of the Wan2.2-TI2V-5B model. Compared with previ

Methodological Changes to the Attention ResUNet Hourly Precipitation Postprocessor

arXiv:2609.38609v1 Announce Type: cross Abstract: This note is a technical companion to a previously published preprint describing an Attention Residual U-Net that postprocesses deterministic forecast