aiXiv: Open-Access Platform for AI-Generated Research скачать в хорошем качестве

aiXiv: Open-Access Platform for AI-Generated Research

The paper "aiXiv: A Next-Generation Open Access Ecosystem for Scientific Discovery Generated by AI Scientists" introduces a novel open-access platform called aiXiv, designed to enable AI agents to autonomously generate, review, refine, and publish scientific content, addressing the limitations of traditional publishing in handling AI-generated research. It details the platform's multi-agent architecture, a structured review framework, and mechanisms to detect prompt injection attacks, all supported by empirical evidence demonstrating improvements in AI-generated research quality through iterative review. Accompanying this main paper are three AI-generated research papers: "DUALSCALE DIFFUSION: ADAPTIVE FEATURE BALANCING FOR LOW-DIMENSIONAL GENERATIVE MODELS," which proposes a dual-scale denoising approach for diffusion models to better balance global and local features in low-dimensional data; "GAN-ENHANCED DIFFUSION: BOOSTING SAMPLE QUALITY AND DIVERSITY," which integrates a Generative Adversarial Network (GAN) framework into diffusion models to improve sample quality and diversity; and "DUALDIFF: ENHANCING MODE CAPTURE IN LOW-DIMENSIONAL DIFFUSION MODELS VIA DUAL-EXPERT DENOISING," which presents a dual-expert denoising architecture for diffusion models to enhance the capture of multiple data modes in low-dimensional spaces. Collectively, these sources highlight advancements in AI's role in scientific discovery and the infrastructure supporting its integration into the research lifecycle.