title: AI-Driven Scholarly Peer Review via Persistent Workflow Prompting,
    Meta-Prompting, and Meta-Reasoning
summary: Critical peer review of scientific manuscripts presents a significant
  challenge for Large Language Models (LLMs), partly due to data limitations and
  the complexity of expert reasoning. This report introduces Persistent Workflow
  Prompting (PWP), a potentially broadly applicable prompt engineering
  methodology designed to bridge this gap using standard LLM chat interfaces
  (zero-code, no APIs). We present a proof-of-concept PWP prompt for the critical
  analysis of experimental chemistry manuscripts, featuring a hierarchical,
  modular architecture (structured via Markdown) that defines detailed analysis
  workflows. We develop this PWP prompt through iterative application of
  meta-prompting techniques and meta-reasoning aimed at systematically codifying
  expert review workflows, including tacit knowledge. Submitted once at the start
  of a session, this PWP prompt equips the LLM with persistent workflows
  triggered by subsequent queries, guiding modern reasoning LLMs through
  systematic, multimodal evaluations. Demonstrations show the PWP-guided LLM
  identifying major methodological flaws in a test case while mitigating LLM
  input bias and performing complex tasks, including distinguishing claims from
  evidence, integrating text/photo/figure analysis to infer parameters, executing
  quantitative feasibility checks, comparing estimates against claims, and
  assessing a priori plausibility. To ensure transparency and facilitate
  replication, we provide full prompts, detailed demonstration analyses, and logs
  of interactive chats as supplementary resources. Beyond the specific
  application, this work offers insights into the meta-development process
  itself, highlighting the potential of PWP, informed by detailed workflow
  formalization, to enable sophisticated analysis using readily available LLMs
  for complex scientific tasks.
paper_authors._id: 68263e83543459fc150218d3
paper_authors.name: Evgeny Markhasin
paper_authors.status: claimed_verified
paper_authors.statusLastChangedAt: 2025-05-16T07:12:13.763Z
paper_authors._id: 68264aa0e6a0ae8670403081
paper_authors.avatarUrl: https://cdn-avatars.huggingface.co/v1/production/uploads/68264aa0e6a0ae8670403081/a6V9yE1cf6-lFf7G8Ih-H.png
paper_authors.fullname: Evgeny Markhasin
paper_authors.type: user
paper_authors.user: PChemGuy
paper_title: AI-Driven Scholarly Peer Review via Persistent Workflow Prompting,
    Meta-Prompting, and Meta-Reasoning
paper_summary: Critical peer review of scientific manuscripts presents a significant
  challenge for Large Language Models (LLMs), partly due to data limitations and
  the complexity of expert reasoning. This report introduces Persistent Workflow
  Prompting (PWP), a potentially broadly applicable prompt engineering
  methodology designed to bridge this gap using standard LLM chat interfaces
  (zero-code, no APIs). We present a proof-of-concept PWP prompt for the critical
  analysis of experimental chemistry manuscripts, featuring a hierarchical,
  modular architecture (structured via Markdown) that defines detailed analysis
  workflows. We develop this PWP prompt through iterative application of
  meta-prompting techniques and meta-reasoning aimed at systematically codifying
  expert review workflows, including tacit knowledge. Submitted once at the start
  of a session, this PWP prompt equips the LLM with persistent workflows
  triggered by subsequent queries, guiding modern reasoning LLMs through
  systematic, multimodal evaluations. Demonstrations show the PWP-guided LLM
  identifying major methodological flaws in a test case while mitigating LLM
  input bias and performing complex tasks, including distinguishing claims from
  evidence, integrating text/photo/figure analysis to infer parameters, executing
  quantitative feasibility checks, comparing estimates against claims, and
  assessing a priori plausibility. To ensure transparency and facilitate
  replication, we provide full prompts, detailed demonstration analyses, and logs
  of interactive chats as supplementary resources. Beyond the specific
  application, this work offers insights into the meta-development process
  itself, highlighting the potential of PWP, informed by detailed workflow
  formalization, to enable sophisticated analysis using readily available LLMs
  for complex scientific tasks.
paper_ai_summary: Persistent Workflow Prompting (PWP) enhances Large Language Models (LLMs) for peer reviewing scientific manuscripts by using structured prompts to guide systematic evaluations and meta-reasoning.
paper_ai_keywords: Persistent Workflow Prompting meta-prompting meta-reasoning prompt engineering LLM chat interfaces