Paper Reconstruction Evaluation: Evaluating Presentation and Hallucination in AI-written Papers
This paper introduces the first systematic evaluation framework for quantifying the quality and risks of papers written by modern coding agents. While AI-driven paper writing has become a growing concern, rigorous evaluation of the quality and potential risks of AI-written papers remains limited, and a unified understanding of their reliability is still lacking. We introduce Paper Reconstruction Evaluation (PaperRecon), an evaluation framework in which an overview (overview.md) is created from an existing paper, after which an agent generates a full paper based on the overview and minimal additional resources, and the result is subsequently compared against the original paper. PaperRecon disentangles the evaluation of the AI-written papers into two orthogonal dimensions, Presentation and Hallucination, where Presentation is evaluated using a rubric and Hallucination is assessed via agentic evaluation grounded in the original paper source. For evaluation, we introduce PaperWrite-Bench, a benchmark of 51 papers from top-tier venues across diverse domains published after 2025. Our experiments reveal a clear trade-off: while both ClaudeCode and Codex improve with model advances, ClaudeCode achieves higher presentation quality at the cost of more than 10 hallucinations per paper on average, whereas Codex produces fewer hallucinations but lower presentation quality. This work takes a first step toward establishing evaluation frameworks for AI-driven paper writing and improving the understanding of its risks within the research community.
69cddb59acf91530b21df97e
Atsuyuki Miyai
claimed_verified
2026-04-02T15:29:50.824Z
6527b37c0ae663e384eb1b85
https://cdn-avatars.huggingface.co/v1/production/uploads/6527b37c0ae663e384eb1b85/zKWa8h6YU4BWfcitpM5Pl.png
Atsuyuki Miyai
AtsuMiyai
user
AtsuMiyai
69cddb59acf91530b21df97f
Mashiro Toyooka
69cddb59acf91530b21df980
Zaiying Zhao
69cddb59acf91530b21df981
Kenta Watanabe
69cddb59acf91530b21df982
Toshihiko Yamasaki
69cddb59acf91530b21df983
Kiyoharu Aizawa
Paper Reconstruction Evaluation: Evaluating Presentation and Hallucination in AI-written Papers
This paper introduces the first systematic evaluation framework for quantifying the quality and risks of papers written by modern coding agents. While AI-driven paper writing has become a growing concern, rigorous evaluation of the quality and potential risks of AI-written papers remains limited, and a unified understanding of their reliability is still lacking. We introduce Paper Reconstruction Evaluation (PaperRecon), an evaluation framework in which an overview (overview.md) is created from an existing paper, after which an agent generates a full paper based on the overview and minimal additional resources, and the result is subsequently compared against the original paper. PaperRecon disentangles the evaluation of the AI-written papers into two orthogonal dimensions, Presentation and Hallucination, where Presentation is evaluated using a rubric and Hallucination is assessed via agentic evaluation grounded in the original paper source. For evaluation, we introduce PaperWrite-Bench, a benchmark of 51 papers from top-tier venues across diverse domains published after 2025. Our experiments reveal a clear trade-off: while both ClaudeCode and Codex improve with model advances, ClaudeCode achieves higher presentation quality at the cost of more than 10 hallucinations per paper on average, whereas Codex produces fewer hallucinations but lower presentation quality. This work takes a first step toward establishing evaluation frameworks for AI-driven paper writing and improving the understanding of its risks within the research community.
A systematic evaluation framework called PaperRecon is proposed to assess AI-generated papers by separating quality assessment into presentation and hallucination dimensions using a benchmark of 51 recent papers.
PaperRecon PaperWrite-Bench AI-written papers presentation quality hallucination assessment agentic evaluation rubric-based evaluation