Search: “framework”

Empirical Variational Autoencoder

arXiv:2610.06545v1 Announce Type: new Abstract: We present Empirical Variational Autoencoder, a general generative framework for continuous-valued (i.e., non-vector-quantized) sequences. EVA is based

EvoState: Closed-Loop Visual State Management for Long-Form Video Generation

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arXiv:2606.16184v2 Announce Type: replace Abstract: Multi-shot long-form video generation remains challenging due to identity drift and compounding inconsistencies across shots. Existing methods encod

model-releasesarxiv-cs-cv

6 Oct 2026

Exact Fast Batch Simulation for Tabular Reinforcement Learning

arXiv:2610.04746v1 Announce Type: cross Abstract: Simulation is a fundamental computational primitive in reinforcement learning (RL), yet conventional simulation explicitly generates individual trajec

FoleyGenEx: Unified Video-to-Audio Generation with Multi-Modal Control, Temporal Alignment, and Semantic Precision

arXiv:2606.14049v2 Announce Type: replace-cross Abstract: We present FoleyGenEx, a unified video-to-audio (VTA) framework integrating multi-modal control, frame-level temporal alignment, and fine-grai

safetyarxiv-cs-cv

Generalizable Neural Downscaling of Earth System Model Wind Fields via Continuous Dynamics Modeling

arXiv:2610.03757v1 Announce Type: cross Abstract: Accurate high-resolution wind field simulations are critical for resolving fine-scale atmospheric dynamics, yet the simulation of wind fields in Earth

GJK-CBF: Control Barrier Functions for Convex Rigid Body Collision Avoidance on SE(3)

arXiv:2610.04910v1 Announce Type: new Abstract: Collision avoidance among convex bodies is a fundamental problem in robotics. Control Barrier Functions (CBFs) provide a practical framework for real-ti

safetyarxiv-cs-ro

HeuFouFT: Task-Guided Metaheuristic Coordinate Search for Fourier Fine-Tuning

arXiv:2610.06437v1 Announce Type: new Abstract: We introduce Heuristic-Guided Fourier Fine-Tuning (HeuFouFT), a task-guided framework for selecting trainable frequency coordinates in Fourier fine-tuni

agentsarxiv-cs-lg

Homogeneous Semantic Alignment and Hierarchical Expert Routing for Radiology Report Generation

arXiv:2610.04499v1 Announce Type: new Abstract: Radiology report generation (RRG) aims to convert medical images into diagnostic texts to assist in clinical decision-making and alleviate the workload

Latent-Lagrangian Neural Networks for Reduced Order Modeling of Non-autonomous Nonlinear Dynamical Systems

arXiv:2610.04723v1 Announce Type: cross Abstract: This work proposes a latent Lagrangian-based framework for reduced-order modelling of forced nonlinear dynamical systems. In contrast with conventiona

agentsarxiv-cs-lg

Latent Score-Based Bayesian Cramer-Rao Bound Estimation for High-Dimensional Imaging Systems

arXiv:2610.03956v1 Announce Type: cross Abstract: We propose a data-driven framework for estimating the Bayesian Cramer-Rao bound (CRB) in high-dimensional imaging systems with complex, analytically i

Learning Conditional Source Distribution via Flow Reversal for Temporal Flow Matching

arXiv:2610.05349v1 Announce Type: new Abstract: We introduce CNP-Flow, a flow matching framework for temporal generation that learns conditional source distributions through flow reversal. Whereas sta

researcharxiv-cs-cv

LightVLN: Efficient Aerial Vision-and-Language Navigation with Compact Memory and History-Guided Local Aggregation

arXiv:2610.05024v1 Announce Type: new Abstract: Aerial vision-and-language navigation (VLN) enables unmanned aerial vehicles to execute long-horizon natural-language instructions from visual observati

local-aiarxiv-cs-cv

LLM-enhanced spatio-temporal learning for grid-level docked bike sharing demand prediction

arXiv:2610.03834v1 Announce Type: new Abstract: Short-term bike-sharing demand forecasting is complicated by spatial-temporal non-stationarity and the practical difficulty of incorporating unstructure

local-aiarxiv-cs-lg

MAGIC: Topology-Aware Analytic Graph Few-Shot Class-Incremental Learning

arXiv:2610.04963v1 Announce Type: new Abstract: Graph few-shot class-incremental learning (GFSCIL) requires a model to continually recognize emerging classes from only a few labeled nodes while preser

MTV: Revisiting Multi-Task Visual Representation Learning

arXiv:2601.13886v2 Announce Type: replace Abstract: Current visual representation learning remains bifurcated: vision-language models (e.g., CLIP) excel at global semantic alignment but lack spatial p

local-aiarxiv-cs-cv

One-Step Generation via Riemannian Wasserstein Gradient Flows

arXiv:2610.04454v1 Announce Type: new Abstract: Recently, Drifting Models and Wasserstein Gradient Flows have attracted substantial attention because they move iterative distributional refinement to t

P3: Persistent Particle Planning for Constrained Diffusion Control

arXiv:2610.06002v1 Announce Type: cross Abstract: Diffusion models provide expressive priors over trajectories, but adapting these priors to test-time constraints requires maintaining feasibility and

Applications

Patch-based Querying Identifies Structures of Interest in Electron Microscopy

arXiv:2610.06020v1 Announce Type: new Abstract: Volume electron microscopy (vEM) has emerged as an essential sensing technique in biomedical research, allowing the three-dimensional imaging of biologi

applicationsarxiv-cs-cv

Tutorials

Pay to Learn, Share to Earn: Incentivized Federated Multi-Player Bandits

arXiv:2610.06062v1 Announce Type: new Abstract: Federated multi-player multi-armed bandit problems model collaborative sequential decision-making where multiple players interact with a common bandit e

tutorialsarxiv-cs-lg

Really nice paper on harness-aware distillation for small language model agents. (bookmark it) It's a super interesting distillation framewo…

Really nice paper on harness-aware distillation for small language model agents. (bookmark it) It's a super interesting distillation framework for small language model agents that are deployed with a

safetydair-ai--x

REBASE: Reference-Background Subspace Elimination for Training-Free In-Context Segmentation

arXiv:2607.09082v2 Announce Type: replace Abstract: Training-free in-context segmentation enables new object categories to be introduced at inference time from a single annotated reference image, elim

Recursive Self-Improvement of Visuomotor Policies through Local Recovery Supervision

arXiv:2610.05151v1 Announce Type: new Abstract: Visuomotor policies can execute familiar tasks yet lack the corrective behavior needed after their own mistakes. We present a framework for recursive se

safetyarxiv-cs-ro

Tutorials

Researching from scratch! [R]

tutorialsr-machinelearning

Hey everyone! I am trying to go really deep into Machine Learning and Artificial Intelligence. Not just learning how to use the latest models and frameworks, but understanding how we reached here from

Riemannian Shape Analysis of the Corpus Callosum in Kendall Space: Aging and Alzheimer's Disease

arXiv:2610.05326v1 Announce Type: new Abstract: The corpus callosum (CC) is a major white-matter structure and a well-established marker of brain aging, but most studies quantify it using scalar summa

SPDAlign: Interpretable Riemannian Alignment for EEG Forward Modeling Shifts

arXiv:2610.06315v1 Announce Type: new Abstract: Electroencephalography (EEG) based brain-computer interfaces enable direct brain-to-device communication for applications such as rehabilitation and com

safetyarxiv-cs-lg

Textual Echo Cancellation

arXiv:2008.06006v5 Announce Type: replace-cross Abstract: In this paper, we propose Textual Echo Cancellation (TEC) - a framework for cancelling the text-to-speech (TTS) playback echo from overlapping

VolS-GS: Relightable Gaussian Splatting with Volumetric Subsurface Scattering

arXiv:2610.04007v1 Announce Type: new Abstract: We present VolS-GS, a relightable Gaussian splatting framework that reconstructs objects from one-light-at-a-time (OLAT) captures and renders them under

researcharxiv-cs-cv

What Gradients Add to Text Leakage in Split Language Models, Counted per Token and per Document

arXiv:2610.04128v1 Announce Type: cross Abstract: Split learning lets a client train a language model on a server without sending its text. The client runs the first layers itself and sends the server

When Does Longer Reasoning Help? Predicting Mathematical Reasoning Through Discovery and Execution

arXiv:2610.05322v1 Announce Type: new Abstract: Test-time compute can improve mathematical reasoning, but can short-budget runs predict how mathematical reasoning scales with additional compute? We in

When Does Retrieval Help? A Study of In-Context Adaptation in Vision-Language-Action Models

arXiv:2610.05492v1 Announce Type: new Abstract: Vision-language-action (VLA) models have shown strong potential as generalist robot policies, but adapting them to unseen tasks often requires costly pa

A Locally Penalized Cross-Estimate Federated Method with Guarantees for Constrained Personalized Learning

arXiv:2603.19617v2 Announce Type: replace Abstract: Federated learning (FL) is a communication-efficient framework for solving distributed optimization problems. Standard FL methods aggregate local up

local-aiarxiv-cs-lg

AI-driven Thermal-aware Data Center Capacity Planning

arXiv:2610.02442v1 Announce Type: new Abstract: The emerging of large language models (LLMs) has posed significant challenges to the thermal management of data center. Intense GPU computation for LLMs

local-aiarxiv-cs-lg

Applications

BioMol-MQA: A Multi-Modal Question Answering Dataset For LLM Reasoning Over Bio-Molecular Interactions

arXiv:2506.05766v2 Announce Type: replace Abstract: Retrieval augmented generation (RAG) has shown great power in improving Large Language Models (LLMs). However, most existing RAG-based LLMs are dedi

applicationsarxiv-cs-cl

Differentiable Koopman Operator for Contrastive Learning on Dynamic Graphs

arXiv:2610.02990v1 Announce Type: new Abstract: Real-world interaction networks are inherently dynamic: edges form and dissolve as node behavior shifts over time. Most snapshot-based contrastive metho

safetyarxiv-cs-lg

Diffusion Flow Matching: Dimension-Improved KL Bounds and Wasserstein Guarantees

arXiv:2606.16610v2 Announce Type: replace-cross Abstract: Diffusion Flow Matching (DFM) has recently emerged as a versatile framework for generative modeling, yet its theoretical convergence propertie

EpiWorld: Grounding LLM Policy Agents in Epidemiological World Models

arXiv:2610.02744v1 Announce Type: new Abstract: Epidemic intervention policies are textual artefacts that human decision-makers interpret, justify, and revise through natural language, making large la

safetyarxiv-cs-cl

EvoRiskBench: An Evolving Benchmark for Runtime Security Risks in Workspace Agents

model-releasesarxiv-cs-ai

arXiv:2610.03153v1 Announce Type: cross Abstract: Workspace agents combine large language models with execution harnesses to perform stateful, multi-step tasks that access or modify external resources

EyeRobot 2.0: Active Gaze for Precise Manipulation without Wrist Cameras

arXiv:2610.03710v1 Announce Type: cross Abstract: Inspired by human vision, we introduce a framework using active gaze to enable fine-grained bimanual manipulation with only a single stereo camera. Ey

safetyarxiv-cs-ai

FlowHMR: Physically Plausible Motion Capture from Video

arXiv:2610.03691v1 Announce Type: new Abstract: We present FlowHMR, a framework for recovering physically plausible global 3D human motion from monocular video. Previous learning-based methods typical

safetyarxiv-cs-cv

From Expression to Reaction: Role-aware Visual Transfer and Stimulus-guided Reasoning for Interlocutor Emotion Recognition

arXiv:2610.03016v1 Announce Type: cross Abstract: In this paper, we propose a Role-aware Stimulus-guided (RASG) framework for interlocutor emotion recognition, which predicts listener emotions from li

researcharxiv-cs-cv

From Migration to Calibration: Preserving Agent Capabilities across Models, Jurisdictions, and Scale

arXiv:2609.35149v2 Announce Type: replace Abstract: Deploying, migrating, or scaling an agent can change its model, harness, infrastructure, application, and intended users. We formulate agent calibra

safetyarxiv-cs-ai

From Mixing to Tearing: Graph Decomposition in Decentralized Optimization via Message Passing

arXiv:2610.03709v1 Announce Type: cross Abstract: We study the minimization of sums of smooth strongly convex functions over undirected graphs, with each function held by one agent and communication r

agentsarxiv-cs-lg

Hardware

Hardware-Native Joint Sparse-Quantization for Trillion-Scale Mixture-of-Experts

arXiv:2610.02241v1 Announce Type: cross Abstract: Mixture-of-Experts (MoE) architectures allow frontier language models to scale to trillions of parameters, but their deployment is constrained by mass

hardwarearxiv-cs-ai

HXAI: Hierarchical Privacy-Preserving Explainable AI in Distributed Energy Systems

arXiv:2610.02504v1 Announce Type: new Abstract: Balancing electricity demand and supply is increasingly difficult due to the inherent intermittency of renewable power generation and the stochastic pow

local-aiarxiv-cs-ai

Hyperparameter selection for equation learning with biologically-informed neural networks

arXiv:2610.02954v1 Announce Type: new Abstract: Biologically-informed neural networks (BINNs) have emerged as a flexible subclass of physics-informed neural networks (PINNs) for learning terms in part

In-Distribution Forcing for Long Video Generation at Test Time

arXiv:2610.03120v1 Announce Type: new Abstract: Modern autoregressive (AR) video diffusion models excel at short-horizon video generation, yet generating long videos remains challenging due to driftin

Applications

Light Entropic Optimal Transport on Riemannian Manifolds

arXiv:2610.03085v1 Announce Type: new Abstract: Entropic Optimal Transport (EOT) has become a practical framework for learning stochastic couplings between complex distributions, with applications in

applicationsarxiv-cs-lg

LLA-MPC on Embedded Hardware: Rapid Adaptive Control with Thousands of Parallel Models

arXiv:2610.03616v1 Announce Type: new Abstract: We present a generalized implementation of Look-Back and Look-Ahead Adaptive Model Predictive Control (LLA-MPC), a learning-free framework for real-time

agentsarxiv-cs-ro