AiraXiv: AI-Driven Open-Access Publishing Platform for Human and AI Scientists
AiraXiv is a proposed open-access academic publishing platform designed to accommodate both human and AI-generated research outputs, addressing scalability challenges in traditional peer review. The platform supports AI scientists via Model Context Protocol (MCP)-based interactions and human scientists through an interactive UI, with papers evolving through continuous feedback-driven iteration. It was validated through real-world deployment as the submission platform for ICAIS 2025. The work positions itself as infrastructure for a future where AI agents are first-class participants in the scientific publishing ecosystem.
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MCP for Research: How to Connect AI to Research Tools
Hugging Face published a blog post explaining how the Model Context Protocol (MCP) can be used to connect AI agents to research tools and data sources. The post covers practical patterns for integrating AI with academic and scientific workflows using MCP as a standardized interface layer. This is a commentary/tutorial piece aimed at researchers looking to extend AI agent capabilities into domain-specific tooling.
Anthropic Open-Sources the Model Context Protocol (MCP)
Anthropic has released the Model Context Protocol (MCP), an open standard enabling secure, two-way connections between AI assistants and external data sources such as business tools, content repositories, and development environments. The protocol introduces a client-server architecture with SDKs, local MCP server support in Claude Desktop, and a repository of pre-built connectors for systems like GitHub, Slack, Google Drive, and Postgres. Early adopters include Block and Apollo, with development tool companies Zed, Replit, Codeium, and Sourcegraph integrating MCP into their platforms. The goal is to replace fragmented, per-source integrations with a single universal protocol, improving context availability for AI agents.
Anthropic launches AI for Science program offering free API credits to researchers
Anthropic is launching an AI for Science program that provides free API credits to qualified researchers at academic institutions, with a focus on biology, life sciences, drug discovery, and agricultural productivity. Researchers are selected based on scientific contribution, potential impact, and AI's ability to accelerate their work. The initiative aligns with Dario Amodei's 'Machines of Loving Grace' vision and represents a structured philanthropic/access program rather than a technical release.
ARIS: Lightweight autonomous ML research agent using Markdown-only skills
ARIS (Auto-Research-In-Sleep) is an open-source Python project providing lightweight, framework-free Markdown-based skills for autonomous ML research workflows, including cross-model review loops, idea discovery, and experiment automation. It is designed to work with any LLM agent backend including Claude Code, Codex, or others. The project has accumulated 11,791 GitHub stars with notable daily traction (+106), suggesting meaningful community adoption.
AutoResearchClaw: Fully Autonomous Self-Evolving Research Agent (Idea to Paper)
AutoResearchClaw is an open-source Python project from aiming-lab that claims to automate the full research pipeline from idea to paper, positioning itself as a fully autonomous and self-evolving research agent. The repository has accumulated 12,426 stars with 55 added today, indicating notable community traction. It represents a concrete implementation in the growing space of AI agents designed to conduct and write scientific research autonomously.
OpAI-Bench: Benchmark for detecting AI text across progressive human-AI co-editing workflows
Researchers introduce OpAI-Bench, a benchmark for studying AI-text detection across progressive human-to-AI document revision workflows, covering document, sentence, token, and span granularities. Starting from human-written documents, the benchmark constructs nine sequentially revised versions per sample under five AI edit operations and varying AI coverage levels across four domains. Key findings include that mixed-authorship intermediate versions are often harder to detect than fully human or heavily AI-edited endpoints, revealing non-monotonic detection patterns absent from existing benchmarks. The work addresses a gap in AI-text detection research as real-world documents increasingly result from iterative human-AI co-editing rather than pure generation.
Comments on U.S. National AI Research Resource Interim Report
Hugging Face published commentary on the U.S. National AI Research Resource (NAIRR) interim report, which outlines a proposed federal initiative to provide researchers with shared access to compute, data, and other AI infrastructure. The post likely advocates for open-source and open-science principles in shaping the NAIRR's design. This represents an industry stakeholder weighing in on a significant U.S. AI policy and infrastructure initiative.
Real AI Agents and Real Work
A commentary piece from One Useful Thing examining the practical deployment of AI agents in real work contexts, framing the tension between human-centered work and AI-generated productivity outputs. The piece appears to analyze how autonomous AI agents are changing knowledge work workflows. Published by a Tier 2 source known for applied AI analysis aimed at practitioners and researchers.