Paper page - AI for Auto-Research: Roadmap & User Guide

Source: https://huggingface.co/papers/2605.18661 Published on May 18

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Abstract

AI systems demonstrate varying reliability across research stages, excelling in structured tasks but struggling with novel ideas and scientific judgment, necessitating human oversight for credible outcomes.

AI-assisted researchis crossing a threshold: fullyautomated systemscan now generateresearch papersfor as little as $15, whilelong-horizon agentscan execute experiments, draft manuscripts, and simulate critique with minimal human input. Yet this productivity frontier exposes a deeper integrity problem: under scientific pressure, even frontier LLMs still fabricate results, miss hidden errors, and fail to judge novelty reliably. Studying developments through April 2026, we present anend-to-end analysisof AI across the complete research lifecycle, organized into fourepistemological phases: Creation (idea generation, literature review, coding & experiments, tables & figures), Writing (paper writing), Validation (peer review, rebuttal & revision), and Dissemination (posters, slides, videos, social media, project pages, and interactive agents). We identify a sharp, stage-dependent boundary between reliable assistance and unreliable autonomy: AI excels at structured, retrieval-grounded, and tool-mediated tasks, but remains fragile for genuinely novel ideas, research-level experiments, and scientific judgment. Generated ideas often degrade after implementation, research code lags far behind pattern-matching benchmarks, and end-to-end autonomous systems have not yet consistently reached major-venue acceptance standards. We further show that greater automation can obscure rather than eliminate failure modes, makinghuman-governed collaborationthe most credible deployment paradigm. Finally, we provide a structured taxonomy,benchmark suite, andtool inventory, cross-stage design principles, and a practitioner-oriented playbook, with resources maintained at our project page.

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