Search: “framework”

arXiv:2608.19831v1 Announce Type: new Abstract: Causal Bayesian networks (CBNs) and structural causal models (SCMs) are the dominant frameworks for graphical causal reasoning, but they cannot adequate

arXiv:2608.07642v1 Announce Type: new Abstract: Aligning large language models (LLMs) with human values remains a major challenge, especially for trustworthy AI. While existing approaches such as RLHF

arXiv:2608.08037v1 Announce Type: new Abstract: LLM-based agent frameworks now act as personal assistants for multi-step tasks. Existing agent frameworks such as OpenClaw commonly follow the Cloud Age

arXiv:2112.07752v4 Announce Type: replace-cross Abstract: Researchers have formalized reinforcement learning (RL) in different ways. If an agent in one RL framework is to run within another RL framewo

arXiv:2607.26068v1 Announce Type: cross Abstract: Existing AI governance frameworks, including the EU AI Act and NIST AI RMF, address safety, transparency, and accountability but do not operationalize

arXiv:2509.03059v2 Announce Type: replace-cross Abstract: Recent advances in Large Language Models (LLMs) have shown that their reasoning capabilities can be significantly improved through Reinforceme

arXiv:2607.12625v1 Announce Type: new Abstract: OpenClaw has emerged as a leading agent framework for complex task automation, yet it faces insufficient cross-platform GUI interaction support and a we

arXiv:2603.22169v2 Announce Type: replace Abstract: We propose a new Verbal Reinforcement Learning (VRL) framework for interpretable task-level planning in mobile robotic systems operating under execu

arXiv:2605.30388v1 Announce Type: new Abstract: This paper introduces a new systematic framework for detecting anomalies in maritime Automatic Identification System (AIS) datasets. These anomalies inc

arXiv:2506.12060v2 Announce Type: replace-cross Abstract: Cybersecurity organizations are adapting to GenAI integration through modified frameworks and hybrid operational processes, with success influ

arXiv:2602.05052v3 Announce Type: replace Abstract: We present a unified algorithmic framework for the numerical solution, constrained optimization, and physics-informed learning of PDEs with a variat

arXiv:2605.08130v1 Announce Type: new Abstract: We present a framework for the simultaneous symbolic recovery of a function and its antiderivative from data. The framework rests on three ideas. First,

arXiv:2506.01404v2 Announce Type: replace Abstract: This paper introduces an innovative error feedback framework designed to mitigate quantization noise in distributed graph filtering, where communica

arXiv:2605.04325v1 Announce Type: new Abstract: We introduce a unified theoretical framework for the rigorous analysis and systematic construction of deep neural networks (DNNs). This framework addres

arXiv:2604.24793v1 Announce Type: cross Abstract: We present CRC-SAM, a unified framework for colorectal cancer segmentation across colonoscopy, CT, and histopathology images. Unlike prior single-moda

arXiv:2506.19089v5 Announce Type: replace-cross Abstract: We introduce StorySim, a programmable framework for synthetically generating stories to evaluate the theory of mind (ToM) and world modeling (

arXiv:2604.22958v1 Announce Type: new Abstract: Preference-based argumentation frameworks (PAFs) extend Dung's approach to abstract argumentation (AAFs) by encoding preferences over arguments. Such pr

arXiv:2603.09002v2 Announce Type: replace-cross Abstract: Multi-agent artificial intelligence systems or MAS are systems of autonomous agents that exercise delegated tool authority, share persistent m

arXiv:2507.03806v3 Announce Type: replace-cross Abstract: This paper presents a learning-based framework for approximating an exact magnetic-field interaction model, supported by both numerical and ex

arXiv:2604.21172v1 Announce Type: cross Abstract: This paper develops a refined version of TAPO-description logic for the analysis of information behavior. The framework is treated not as a single hom

arXiv:2604.20331v1 Announce Type: cross Abstract: Large language models (LLMs), trained on vast datasets, encode extensive real-world knowledge within their parameters, yet their black-box nature obsc

arXiv:2604.19528v1 Announce Type: cross Abstract: This technical note revisits the relationship between RaBitQ and TurboQuant under a unified comparison framework. We compare the two methods in terms

arXiv:2604.16935v1 Announce Type: cross Abstract: Scarce longitudinal evidence examines LLMs' persuasiveness and humanness along time-evolving psychological frameworks. We introduce Talk2AI, a longitu

arXiv:2604.16895v1 Announce Type: new Abstract: We propose Physics-Informed Tracking (PIT), a video-based framework for tracking a single particle from video, where a neural network autoencoder locali

arXiv:2604.13484v1 Announce Type: cross Abstract: Clustering and dimensionality reduction have been crucial topics in machine learning and computer vision. Clustering high-dimensional data has been ch

arXiv:2604.13776v1 Announce Type: cross Abstract: Watermarking is becoming the default mechanism for AI content authentication, with governance policies and frameworks referencing it as infrastructure

arXiv:2603.25326v4 Announce Type: replace Abstract: Interest in the concept of AI-driven harmful manipulation is growing, yet current approaches to evaluating it are limited. This paper introduces a f

arXiv:2604.12519v1 Announce Type: new Abstract: Recent work established a generalized-Fano framework for lower bounding prior-predictive (Bayesian) CVaR in interactive statistical decision making. In

arXiv:2604.11578v1 Announce Type: cross Abstract: Measurement-based quantum computation (MBQC) is a framework for quantum information processing in which a computational task is carried out through on

arXiv:2610.16015v1 Announce Type: new Abstract: The rapid deployment of agentic AI has introduced complex challenges for the ethics and governance of AI systems. Agents built on large language models

arXiv:2610.20218v1 Announce Type: cross Abstract: Diffusion models are a powerful framework for learning complex probability distributions. While widely used diffusion models for continuous data rely

arXiv:2610.16979v1 Announce Type: cross Abstract: Generative retrieval (GR) offers a promising paradigm for industrial search by unifying candidate generation within an autoregressive framework. Howev

arXiv:2610.15921v1 Announce Type: new Abstract: With ever increasing personalized large language model (LLM) services, the next question to answer is how to improve the quality of services/users' expe

arXiv:2610.17829v1 Announce Type: new Abstract: Multimodal Diffusion Transformers (MM-DiTs) have recently emerged as a powerful framework for unified text-vision synthesis, surpassing traditional U-Ne

arXiv:2610.12140v1 Announce Type: new Abstract: Growing automation makes collaborative robots work in more variable environments, increasing the need for adaptation. We propose a human-in-the-loop onl

arXiv:2610.09525v1 Announce Type: new Abstract: We introduce a calibration-first framework that produces supervision scores without access to ground-truth labels or a shared annotation space. Our fram

arXiv:2610.07458v1 Announce Type: new Abstract: Hypergraphs offer a natural framework for modeling networked data, where dependencies among entities are governed by higher-order interactions. While hy

arXiv:2610.05843v1 Announce Type: cross Abstract: Predicting metal-organic framework (MOF) structures from given building blocks requires recovering their positions and orientations in a periodic crys

arXiv:2610.06230v1 Announce Type: new Abstract: A multi-domain eXtended Physics-Informed Neural Network (XPINN) framework is developed for nonsmooth Delay Differential Equations (DDEs). This is the fi

arXiv:2610.06041v1 Announce Type: new Abstract: Deep learning has advanced chest X-ray (CXR) diagnosis, yet demographic biases in learned representations may contribute to performance disparities acro

arXiv:2610.04413v1 Announce Type: new Abstract: We develop a principled framework for constructing neural networks whose specific parameter realizations admit an explicit algorithmic interpretation. E

arXiv:2610.02564v1 Announce Type: cross Abstract: We develop a closure modeling framework for identifying missing components of dynamical systems using Kernel Ridge Regression (KRR). The framework add

arXiv:2610.00705v1 Announce Type: new Abstract: This paper develops a meta-multi-agent reinforcement learning (meta-MARL) framework to enable fast adaptation of interactive policies in a multi-agent s

arXiv:2610.01910v1 Announce Type: new Abstract: All the objects composing our world are enclosed within surfaces. Yet, most robot learning and motion generation frameworks treat surfaces as constraint

arXiv:2609.34211v2 Announce Type: replace Abstract: In financial fraud detection, rich semantic context can provide important evidence for transaction behavior modeling and fraud reasoning. However, p

arXiv:2609.38559v1 Announce Type: new Abstract: This paper examines human-AI interactions (HAIIs) in clinical trials and presents a multidimensional categorisation framework that classifies interactio