arXiv:2605.19156v1 Announce Type: new Abstract: Recent auto-research systems can produce complete papers, but feasibility is not the same as quality, and the field still lacks a systematic study of how good agent-generated papers actually are. We introduce ResearchArena, a minimal scaffold that lets off-the-shelf agents (Claude Code using Opus 4.6, Codex using GPT-5.4, and Kimi Code using K2.5) carry out the full research loop themselves (ideation, experimentation, paper writing, self-refinement) under only lightweight guidance. Across 13 computer science seeds and 3 trials per agent-domain pa
The proliferation of advanced large language models (LLMs) and agentic architectures is enabling the development of systems capable of increasingly autonomous and complex intellectual tasks.
This development indicates a significant step towards fully autonomous R&D, potentially accelerating scientific discovery and collapsing traditional research pipelines, fundamentally altering how knowledge is generated.
The ability of AI agents to autonomously ideate, experiment, write, and refine research papers suggests a shift from AI as a research tool to AI as a research generator itself.
- · AI platform developers
- · Cloud compute providers
- · Organizations leveraging auto-research
- · Scientific fields with strong computational bases
- · Traditional research institutions
- · Human researchers performing rote experimentation
- · Publishing houses relying on human-generated content
AI agents begin to generate a substantial portion of new research data and publications across various fields.
The definition of intellectual property and authorship in scientific discovery becomes increasingly complex and contested.
The rate of scientific and technological progress accelerates dramatically, leading to unforeseen societal and economic transformations.
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