research

HumanoidTTT: Test-Time Capability Reuse for Efficient Humanoid Control

arXiv:2610.00198v1 Announce Type: new Abstract: Recent advances in motion generation and whole-body tracking have enabled humanoid robots to execute increasingly diverse motions, yet the same motion c

researcharxiv-cs-ro

Content type

All Blog X Post Paper YouTube Reddit GitHub

Clear filters

In CEM, a World Model Is Also a Proposal Mechanism

DGX agent

arXiv:2610.00921v1 Announce Type: new Abstract: The cross-entropy method (CEM) uses world-model scores to select action sequences and fit the distribution sampled in its next iteration. A scoring erro

researcharxiv-cs-lg

2 Oct 2026

In-context Learning of Single-index Targets: Comparing Kernel and Feature Learners

arXiv:2610.01712v1 Announce Type: new Abstract: In-context learning (ICL) enables a pretrained model to infer a task from demonstrations without updating its parameters. While much of the existing the

In Dialogue with Intelligence: Toward Insightful Co-Augmentation

arXiv:2505.22767v4 Announce Type: replace-cross Abstract: Dialogue with a large language model can lead a person to insight: a sudden change in how they understand a problem. This perspective asks how

Inference for stochastic differential equations driven by weighted sub-fractional Brownian motion using neural networks and the Euler approximation

arXiv:2610.00793v1 Announce Type: cross Abstract: We consider the estimation of drift, diffusion, and noise covariance from discrete observations of stochastic differential equations driven by Gaussia

Inferring Multi-Timescale Neural Dynamics with Switching Linear Dynamical Systems

arXiv:2610.01786v1 Announce Type: new Abstract: Neural activity often exhibits multiple timescales that can vary with behavioral states and task conditions. Identifying these timescales from neural re

Initial condition recovery in nonlinear damped viscous photoacoustic tomography using a convolutional neural network-guided gradient-free optimization framework

arXiv:2610.01015v1 Announce Type: cross Abstract: Photoacoustic tomography (PAT) is a hybrid imaging modality that combines high optical contrast with high ultrasonic resolution for biomedical imaging

Initialization Improves LLM-Driven Discovery

arXiv:2610.00707v1 Announce Type: cross Abstract: Large Language Models (LLMs) have been used for novel discovery of algorithms, theorems, drugs, and other tasks through the use of harnesses that prom

Is it Possible to Generate Irreversible PolyProtected Templates from Face Embeddings using System-Specific Keys?

arXiv:2610.01385v1 Announce Type: cross Abstract: This work aims to answer the question of whether it is possible to generate irreversible protected templates when the PolyProtect biometric template p

JEPA-Bisim: Learning Robust Visual Representations for Planning with Joint-Embedding Predictive World Models

arXiv:2602.18639v2 Announce Type: replace Abstract: World models learned from high-dimensional visual observations allow agents to make decisions and plan directly in latent space, avoiding pixel-leve

JEPA-TTT: Persistent Test-Time Training of Latent World Models for Planning under Dynamics Shifts

arXiv:2610.00722v1 Announce Type: new Abstract: World models enable agents to plan by predicting future states of the environment, but their predictions can become unreliable when test-time dynamics d

Joint Branch-Space Transform Coding for Diffusion Activation Quantization with Classifier-Free Guidance

arXiv:2610.00930v1 Announce Type: new Abstract: Post-training quantization for diffusion models increasingly exploits timestep, feature, and layer structure. While recent work has begun incorporating

Kernelized Activation Steering

arXiv:2610.01062v1 Announce Type: new Abstract: Activation steering provides a simple, training-free mechanism for controlling attributes of generative models such as sentiment, style, and helpfulness

Kolmogorov-Arnold Networks for Free-Boundary Partial Differential Equations

arXiv:2610.02084v1 Announce Type: cross Abstract: We study free-boundary problems within a physics-informed framework using Kolmogorov-Arnold network (KAN) approximations. The proposed approach incorp

Langevin-Informed Transfer Learning: Replacing Target Samples by Black-Box Feedback

arXiv:2610.01522v1 Announce Type: new Abstract: Many scientific and machine learning systems, from molecular dynamics to diffusion models and beyond, are governed by stochastic dynamics with low-dimen

Language Discrimination Improves Linguistic Learning in Multilingual Speech Models

researchapple-ml-research

Multilingual self-supervised speech models can benefit from sharing information across languages, but under a matched total pretraining data budget they still fall short of monolingual models. We show

Large Language Bayes Is Not Reparameterisation-Invariant

arXiv:2610.00265v1 Announce Type: new Abstract: Large Language Bayes (LLB) answers an informal modelling question by sampling candidate probabilistic programs from a language model, running approximat

LAST: Looped Audio Spectrogram Transformer

arXiv:2610.01926v1 Announce Type: cross Abstract: Increasing depth of transformer models improves recognition, but it comes at a substantial cost. Each additional layer requires more parameters, which

Latent-Foresight: End-to-End Learning Predictable Representations for Latent World Models

arXiv:2610.01942v1 Announce Type: new Abstract: Predicting the future evolution of a scene is a fundamental capability for world modeling. Recent work has shown that operating in the feature space of

Latent Information Sharing for Accelerating Federated Learning

arXiv:2610.01126v1 Announce Type: new Abstract: Federated learning (FL) is a communication-efficient distributed learning paradigm. However, client drift remains one of the most critical challenges, h

Latent JEPA: Abstract Future Prediction for Latent Reasoning in Chemistry

arXiv:2610.01947v1 Announce Type: new Abstract: Large language models offer a promising foundation for chemical reasoning, bringing together chemical knowledge and multistep problem solving. Chemical

Learned End-to-End Guidance Schedules for Diffusion Models

arXiv:2610.01502v1 Announce Type: new Abstract: Diffusion models are a powerful generative paradigm used across multimedia and scientific applications. Guided diffusion methods impose requirements on

Learning ab initio phase-field models

arXiv:2610.01432v1 Announce Type: cross Abstract: Simulating microstructure evolution requires quantum-mechanical accuracy and mesoscopic reach in length and time scales, a combination that no current

Learning Commute-Time-Preserving World Models for Planning

arXiv:2610.01373v1 Announce Type: new Abstract: World models allow agents to plan in latent space by choosing a sequence of actions that most reduces the distance to a given goal state. Thus, planning

Learning Goal-Reaching Quasimetric Geometry From Finite-Time Reachability

arXiv:2610.00778v1 Announce Type: new Abstract: In goal-conditioned reinforcement learning (GCRL), quasimetric learning models goal-reaching costs as quasimetric distances, connecting local constraint

Learning Linear Systems under Heavy-Tailed Noise: A Non-Asymptotic Analysis from A Single Trajectory

arXiv:2610.00637v1 Announce Type: new Abstract: We establish non-asymptotic sample complexity bounds for the least-squares estimation of vector autoregressive models for exponentially stable systems w

Learning PDE Dynamics between Submanifolds Using Green's Observation Operators

arXiv:2610.01697v1 Announce Type: new Abstract: Many physical systems are driven and observed only on lower-dimensional submanifolds of a larger spatial domain, while their dynamics are governed by th

Learnt Attacks on Quantum Key Distribution under Channel Noise and Device Drift

arXiv:2610.01792v1 Announce Type: cross Abstract: Quantum key distribution (QKD) links are provisioned from security analyses of stationary channels, whereas the devices that determine the channel dri

Least-time Gradient Flow

arXiv:2610.01426v1 Announce Type: new Abstract: Prescribing the speed of gradient flow on the risk itself, by the dynamics ot w=-u(E(w))nabla E(w)/abs{nabla E(w)}^{2}, makes the risk e(t)=E(w(t)) obey

LENS-GRF: Permutation-Invariant Lesion Evidence Network with Gated Residual Fusion for Acne Severity Grading and Multi-Rater Clinical Oracle Analysis

arXiv:2610.00294v1 Announce Type: cross Abstract: Automated acne severity grading requires both whole-face context and fine-grained lesion evidence. We propose LENS-GRF (Lesion Evidence Network with S

LensBridge: Frequency-Guided Compound Degradation Adaptation for Lens Aberration Correction and Veiling Glare Removal

arXiv:2610.00318v1 Announce Type: cross Abstract: Simplified optical systems often exhibit residual lens aberrations and Veiling Glare (VG), resulting in spatially varying blur and contrast reduction.

Let the Heads Talk: Beyond Diagonal Graph Attention

arXiv:2610.01494v1 Announce Type: new Abstract: Sheaf Neural Networks generalize scalar-weighted message passing by replacing scalar edge weights with linear transport maps between local feature space

Leveraging Instruction Tuning and Merging for Reasoning Model Adaptation

arXiv:2607.14895v2 Announce Type: replace Abstract: Reasoning language models (RLMs) demonstrate impressive performance by leveraging test-time compute in the form of reasoning tokens. However, this b

LiDARFlow: Real-Time Panel-Based MAV Guidance in Unknown Environments

arXiv:2610.01573v1 Announce Type: new Abstract: This paper presents a guidance algorithm for micro aerial vehicles operating in unknown, cluttered environments using only onboard sensing. The method i

researcharxiv-cs-ro

LLM-Assisted Discovery of Typed Semantic Links for Ontology Network Construction

arXiv:2610.01393v1 Announce Type: cross Abstract: Constructing typed, justified semantic links between ontologies is essential for enabling interoperability across heterogeneous and interdisciplinary

Loading history and window geometry bound compact-state slip ranking during granular shear startup

arXiv:2610.00124v1 Announce Type: cross Abstract: Granular slip forecasting can conflate material state, loading progress, and the geometry of event-centered sampling. We separated these contributions

Localizing Transfer Between Memorization Tasks

arXiv:2610.00771v1 Announce Type: new Abstract: A central puzzle in transfer learning is why pre-training on one task can accelerate training or improve performance on another task, and what mechanism

Low-Budget Active Learning through Entropic Optimal Transport

arXiv:2610.01199v1 Announce Type: new Abstract: We consider low-budget active learning, which consists of selecting a limited number of points, the coreset, such that a model can be trained to high ac

MaPa: Text-driven Photorealistic Material Painting for 3D Shapes

arXiv:2404.17569v4 Announce Type: replace Abstract: This paper aims to generate materials for 3D meshes from text descriptions. Unlike existing methods that synthesize texture maps, we propose to gene

Mathematical Transfer in LLMs Follows Reasoning Approach More Than Topic

arXiv:2610.00331v1 Announce Type: new Abstract: When selecting mathematical training data for LLMs, a natural organizing principle is topic: probability examples for probability targets. An alternativ

MatrixReward: Reward from Rubric Matrix for Open-Ended Generation

arXiv:2610.00389v1 Announce Type: cross Abstract: Open-ended query generation lacks standard answers, thus necessitating an effective reward mechanism. Pointwise scoring rubrics provide limited inform

MEGA: Object-Level Mesh Extraction from 3D Gaussian Splatting via Spatial Visual Distillation

arXiv:2610.01707v1 Announce Type: new Abstract: Mesh extraction from 3D Gaussian Splatting (3DGS) aims to endow 3D Gaussians with accurate geometric structures, enabling explicit and precise 3D occupa

MIRTO: a registration-gated, multiverse-tested evaluation protocol for unsupervised anomaly segmentation in brain MRI

arXiv:2610.02136v1 Announce Type: cross Abstract: Unsupervised anomaly detection (UAD) methods for brain MRI are ranked by a single score, yet that score rests on choices that are rarely reported: how

Misalignment of Low-Loss Regions Causes Grokking

arXiv:2610.00620v1 Announce Type: cross Abstract: Grokking refers to the delayed emergence of validation-set generalization after a model has already overfit the training set. Although first observed

Model Merging via Data-Free Covariance Estimation

arXiv:2604.01329v2 Announce Type: replace Abstract: Model merging provides a way of cheaply combining individual models to produce a model that inherits each individual's capabilities. While some merg

MoLE: Mixture of Latent Experts for Complementary Visual Reasoning

arXiv:2610.01917v1 Announce Type: cross Abstract: Latent visual reasoning equips vision--language models with continuous intermediate states that can process visual evidence without explicit textual r

Moore, Escher, Penrose: A Conformal Golden Braid

arXiv:2610.02210v1 Announce Type: new Abstract: I don't think I have ever done anything as peculiar in my life. Among other things, it shows a young man looking with interest at a print on the wall of

MOVE: Multimodal Open-world Verification and Expansion for Graph Learning

arXiv:2610.00268v1 Announce Type: new Abstract: Multimodal graph learning faces a fundamental challenge: new classes may emerge after deployment, while models are trained with a fixed label space. Exi