Search: “framework”

Progressive Deep Learning for Automated Spheno-Occipital Synchondrosis Maturation Assessment

arXiv:2604.10945v1 Announce Type: new Abstract: Accurate assessment of spheno-occipital synchondrosis (SOS) maturation is a key indicator of craniofacial growth and a critical determinant for orthodon

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ReadMOF: Structure-Free Semantic Embeddings from Systematic MOF Nomenclature for Machine Learning

DGX agent

arXiv:2604.10568v1 Announce Type: new Abstract: Systematic chemical names, such as IUPAC-style nomenclature for metal-organic frameworks (MOFs), contain rich structural and compositional information i

researcharxiv-cs-lg

14 Apr 2026

Resource Consumption Threats in Large Language Models

arXiv:2603.16068v3 Announce Type: replace-cross Abstract: Given limited and costly computational infrastructure, resource efficiency is a key requirement for large language models (LLMs). Efficient LL

S^3: Structured Sparsity Specification

arXiv:2604.11315v1 Announce Type: cross Abstract: We introduce the Structured Sparsity Specification (S^3), an algebraic framework for defining, composing, and implementing structured sparse patterns.

S4M: 4-points to Segment Anything

arXiv:2503.05534v3 Announce Type: replace Abstract: Purpose: The Segment Anything Model (SAM) promises to ease the annotation bottleneck in medical segmentation, but overlapping anatomy and blurred bo

Safe Human-to-Humanoid Motion Imitation Using Control Barrier Functions

arXiv:2604.11447v1 Announce Type: new Abstract: Ensuring operational safety is critical for human-to-humanoid motion imitation. This paper presents a vision-based framework that enables a humanoid rob

safetyarxiv-cs-ro

Self-Organizing Dual-Buffer Adaptive Clustering Experience Replay (SODACER) for Safe Reinforcement Learning in Optimal Control

arXiv:2601.06540v2 Announce Type: replace-cross Abstract: This paper proposes a novel reinforcement learning framework, named Self-Organizing Dual-buffer Adaptive Clustering Experience Replay (SODACER

Teaching Robots to Interpret Social Interactions through Lexically-guided Dynamic Graph Learning

arXiv:2604.10895v1 Announce Type: cross Abstract: For a robot to be called socially intelligent, it must be able to infer users internal states from their current behaviour, predict the users future b

researcharxiv-cs-ro

Template-assisted Contrastive Learning of Task-oriented Dialogue Sentence Embeddings

arXiv:2305.14299v3 Announce Type: replace-cross Abstract: Learning high quality sentence embeddings from dialogues has drawn increasing attentions as it is essential to solve a variety of dialogue-ori

The Missing Knowledge Layer in Cognitive Architectures for AI Agents

arXiv:2604.11364v1 Announce Type: new Abstract: The two most influential cognitive architecture frameworks for AI agents, CoALA [21] and JEPA [12], both lack an explicit Knowledge layer with its own p

The Wasserstein transform

arXiv:1810.07793v2 Announce Type: replace Abstract: We introduce the Wasserstein Transform (WT), a general unsupervised framework for updating distance structures on given data sets with the purpose o

Topology-Aware PAC-Bayesian Generalization Analysis for Graph Neural Networks

arXiv:2604.10553v1 Announce Type: new Abstract: Graph neural networks have demonstrated excellent applicability to a wide range of domains, including social networks, biological systems, recommendatio

Unfolding 3D Gaussian Splatting via Iterative Gaussian Synopsis

arXiv:2604.11685v1 Announce Type: new Abstract: 3D Gaussian Splatting (3DGS) has become a state-of-the-art framework for real-time, high-fidelity novel view synthesis. However, its substantial storage

Conformal Prediction in Hierarchical Classification with Constrained Representation Complexity

model-releasesarxiv-cs-lg

arXiv:2501.19038v3 Announce Type: replace-cross Abstract: Conformal prediction has emerged as a widely used framework for constructing valid prediction sets in classification and regression tasks. In

Applications

Enhanced Self-Supervised Multi-Image Super-Resolution for Camera Array Images

arXiv:2604.06816v2 Announce Type: replace-cross Abstract: Conventional multi-image super-resolution (MISR) methods, such as burst and video SR, rely on sequential frames from a single camera. Conseque

applicationsarxiv-cs-cv

Enhancing LLM Problem Solving via Tutor-Student Multi-Agent Interaction

arXiv:2604.08931v1 Announce Type: new Abstract: Human cognitive development is shaped not only by individual effort but by structured social interaction, where role-based exchanges such as those betwe

LLM4Delay: Flight Delay Prediction via Cross-Modality Adaptation of Large Language Models and Aircraft Trajectory Representation

arXiv:2510.23636v3 Announce Type: replace-cross Abstract: Flight delay prediction has become a key focus in air traffic management (ATM), as delays reflect inefficiencies in the system. This paper pro

Neural Distribution Prior for LiDAR Out-of-Distribution Detection

arXiv:2604.09232v1 Announce Type: cross Abstract: LiDAR-based perception is critical for autonomous driving due to its robustness to poor lighting and visibility conditions. Yet, current models operat

OASIS: Online Activation Subspace Learning for Memory-Efficient Training

arXiv:2604.09406v1 Announce Type: new Abstract: Training large language models (LLMs) is constrained by memory requirements, with activations accounting for a substantial fraction of the total footpri

Local Ai

Physics-Informed Reinforcement Learning of Spatial Density Velocity Potentials for Map-Free Racing

arXiv:2604.09499v1 Announce Type: new Abstract: Autonomous racing without prebuilt maps is a grand challenge for embedded robotics that requires kinodynamic planning from instantaneous sensor data at

local-aiarxiv-cs-ro

Reflection of Episodes: Learning to Play Game from Expert and Self Experiences

arXiv:2502.13388v4 Announce Type: replace Abstract: StarCraft II is a complex and dynamic real-time strategy (RTS) game environment, which is very suitable for artificial intelligence and reinforcemen

Applications

Task-Distributionally Robust Data-Free Meta-Learning

arXiv:2311.14756v2 Announce Type: replace-cross Abstract: Data-Free Meta-Learning (DFML) aims to enable efficient learning of unseen few-shot tasks, by meta-learning from multiple pre-trained models w

applicationsarxiv-cs-ai

Truncated Rectified Flow Policy for Reinforcement Learning with One-Step Sampling

arXiv:2604.09159v1 Announce Type: new Abstract: Maximum entropy reinforcement learning (MaxEnt RL) has become a standard framework for sequential decision making, yet its standard Gaussian policy para

safetyarxiv-cs-lg

Accelerating 4D Hyperspectral Imaging through Physics-Informed Neural Representation and Adaptive Sampling

arXiv:2604.06561v1 Announce Type: cross Abstract: High-dimensional hyperspectral imaging (HSI) enables the visualization of ultrafast molecular dynamics and complex, heterogeneous spectra. However, ap

Agents

Beyond Functional Correctness: Design Issues in AI IDE-Generated Large-Scale Projects

arXiv:2604.06373v1 Announce Type: cross Abstract: New generation of AI coding tools, including AI-powered IDEs equipped with agentic capabilities, can generate code within the context of the project.

agentsarxiv-cs-ai

Faithful-First Reasoning, Planning, and Acting for Multimodal LLMs

arXiv:2511.08409v4 Announce Type: replace Abstract: Multimodal Large Language Models (MLLMs) frequently suffer from unfaithfulness, generating reasoning chains that drift from visual evidence or contr

From Load Tests to Live Streams: Graph Embedding-Based Anomaly Detection in Microservice Architectures

arXiv:2604.06448v1 Announce Type: cross Abstract: Prime Video regularly conducts load tests to simulate the viewer traffic spikes seen during live events such as Thursday Night Football as well as vid

Logics-Parsing-Omni Technical Report

arXiv:2603.09677v3 Announce Type: replace Abstract: Addressing the challenges of fragmented task definitions and the heterogeneity of unstructured data in multimodal parsing, this paper proposes the O

MENO: MeanFlow-Enhanced Neural Operators for Dynamical Systems

arXiv:2604.06881v1 Announce Type: new Abstract: Neural operators have emerged as powerful surrogates for dynamical systems due to their grid-invariant properties and computational efficiency. However,

Local Ai

MLX creator @awnihannun sharing the story of MLX. Apple management called him right after the launch: “why didn’t you tell us this was going…

MLX is an open-source machine learning framework developed by Apple ML researcher Awni Hannun and colleagues, announced on December 5, 2023, and designed specifically for Apple Silicon hardware. Th...

local-aiollama--x

MO-RiskVAE: A Multi-Omics Variational Autoencoder for Survival Risk Modeling in Multiple MyelomaMO-RiskVAE

arXiv:2604.06267v1 Announce Type: cross Abstract: Multimodal variational autoencoders (VAEs) have emerged as a powerful framework for survival risk modeling in multiple myeloma by integrating heteroge

On-Policy Distillation of Language Models for Autonomous Vehicle Motion Planning

model-releasesarxiv-cs-ro

arXiv:2604.07944v1 Announce Type: new Abstract: Large language models (LLMs) have recently demonstrated strong potential for autonomous vehicle motion planning by reformulating trajectory prediction a

Open-Ended Instruction Realization with LLM-Enabled Multi-Planner Scheduling in Autonomous Vehicles

model-releasesarxiv-cs-cv

arXiv:2604.08031v1 Announce Type: cross Abstract: Most Human-Machine Interaction (HMI) research overlooks the maneuvering needs of passengers in autonomous driving (AD). Natural language offers an int

Physics-Informed Neural Networks for Joint Source and Parameter Estimation in Advection-Diffusion Equations

model-releasesarxiv-cs-lg

arXiv:2512.07755v3 Announce Type: replace-cross Abstract: Recent studies have demonstrated the success of deep learning in solving forward and inverse problems in engineering and scientific computing

Applications

QA-MoE: Towards a Continuous Reliability Spectrum with Quality-Aware Mixture of Experts for Robust Multimodal Sentiment Analysis

arXiv:2604.05704v2 Announce Type: replace Abstract: Multimodal Sentiment Analysis (MSA) aims to infer human sentiment from textual, acoustic, and visual signals. In real-world scenarios, however, mult

applicationsarxiv-cs-ai

Reflection-Based Task Adaptation for Self-Improving VLA

arXiv:2510.12710v3 Announce Type: replace Abstract: Pre-trained Vision-Language-Action (VLA) models represent a major leap towards general-purpose robots, yet efficiently adapting them to novel, speci

safetyarxiv-cs-ro

Safe Large-Scale Robust Nonlinear MPC in Milliseconds via Reachability-Constrained System Level Synthesis on the GPU

arXiv:2604.07644v1 Announce Type: new Abstract: We present GPU-SLS, a GPU-parallelized framework for safe, robust nonlinear model predictive control (MPC) that scales to high-dimensional uncertain rob

safetyarxiv-cs-ro

Agents

Scaling-Aware Data Selection for End-to-End Autonomous Driving Systems

arXiv:2604.08366v1 Announce Type: cross Abstract: Large-scale deep learning models for physical AI applications depend on diverse training data collection efforts. These models and correspondingly, th

agentsarxiv-cs-cv

SkillTrojan: Backdoor Attacks on Skill-Based Agent Systems

arXiv:2604.06811v1 Announce Type: cross Abstract: Skill-based agent systems tackle complex tasks by composing reusable skills, improving modularity and scalability while introducing a largely unexamin

Strategic Persuasion with Trait-Conditioned Multi-Agent Systems for Iterative Legal Argumentation

arXiv:2604.07028v1 Announce Type: cross Abstract: Strategic interaction in adversarial domains such as law, diplomacy, and negotiation is mediated by language, yet most game-theoretic models abstract

Tabular GANs for uneven distribution

model-releasesarxiv-cs-cv

arXiv:2010.00638v2 Announce Type: replace-cross Abstract: Generative models for tabular data have evolved rapidly beyond Generative Adversarial Networks (GANs). While GANs pioneered synthetic tabular

Uni-ViGU: Towards Unified Video Generation and Understanding via A Diffusion-Based Video Generator

arXiv:2604.08121v1 Announce Type: new Abstract: Unified multimodal models integrating visual understanding and generation face a fundamental challenge: visual generation incurs substantially higher co

Variational Feature Compression for Model-Specific Representations

model-releasesarxiv-cs-lg

arXiv:2604.06644v1 Announce Type: cross Abstract: As deep learning inference is increasingly deployed in shared and cloud-based settings, a growing concern is input repurposing, in which data submitte

Agents

Highlights: 👉 Configurable thinking mode for step-by-step reasoning 👉 Multimodal understanding with text and image input, including docume…

Highlights: 👉 Configurable thinking mode for step-by-step reasoning 👉 Multimodal understanding with text and image input, including document parsing and OCR 👉 Native function calling with structured t

agentstogether-ai--x

9 Apr 2026

3DCodeVerse: Exploring 3D Worlds as Code

arXiv:2610.22341v1 Announce Type: cross Abstract: We introduce 3DCodeVerse, a research ecosystem that enables coding agents to create editable and verifiable 3D content at scale. 3DCodeVerse comprises

AgentPerturb: Evaluating LLM Agent Robustness under Controlled Interaction Perturbations

arXiv:2610.21624v1 Announce Type: new Abstract: Real-world tasks are often complex and noisy, posing substantial challenges for LLM agents. Yet existing evaluations lack a systematic framework for joi

AI Alignment From Social Choice Perspectives

arXiv:2606.21550v2 Announce Type: replace Abstract: Alignment from human feedback uses human judgments about model outputs to steer the behavior of language models after pretraining. When those judgme

Audio-Visual Dog Identification and Tracking in Real-World Videos

model-releasesarxiv-cs-cv

arXiv:2610.21011v1 Announce Type: new Abstract: Identifying individual dogs from barks in real-world videos remains challenging and largely unexplored. Solving this problem would enable long-term beha