title: LLM Context Conditioning and PWP Prompting for Multimodal Validation of
    Chemical Formulas
summary: Identifying subtle technical errors within complex scientific and technical
  documents, especially those requiring multimodal interpretation (e.g., formulas
  in images), presents a significant hurdle for Large Language Models (LLMs)
  whose inherent error-correction tendencies can mask inaccuracies. This
  exploratory proof-of-concept (PoC) study investigates structured LLM context
  conditioning, informed by Persistent Workflow Prompting (PWP) principles, as a
  methodological strategy to modulate this LLM behavior at inference time. The
  approach is designed to enhance the reliability of readily available,
  general-purpose LLMs (specifically Gemini 2.5 Pro and ChatGPT Plus o3) for
  precise validation tasks, crucially relying only on their standard chat
  interfaces without API access or model modifications. To explore this
  methodology, we focused on validating chemical formulas within a single,
  complex test paper with known textual and image-based errors. Several prompting
  strategies were evaluated: while basic prompts proved unreliable, an approach
  adapting PWP structures to rigorously condition the LLM's analytical mindset
  appeared to improve textual error identification with both models. Notably,
  this method also guided Gemini 2.5 Pro to repeatedly identify a subtle
  image-based formula error previously overlooked during manual review, a task
  where ChatGPT Plus o3 failed in our tests. These preliminary findings highlight
  specific LLM operational modes that impede detail-oriented validation and
  suggest that PWP-informed context conditioning offers a promising and highly
  accessible technique for developing more robust LLM-driven analytical
  workflows, particularly for tasks requiring meticulous error detection in
  scientific and technical documents. Extensive validation beyond this limited
  PoC is necessary to ascertain broader applicability.
paper_authors._id: 682c105927a587e5a6ebacdd
paper_authors.name: Evgeny Markhasin
paper_authors.status: claimed_verified
paper_authors.statusLastChangedAt: 2025-05-20T07:20:15.756Z
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: LLM Context Conditioning and PWP Prompting for Multimodal Validation of
    Chemical Formulas
paper_summary: Identifying subtle technical errors within complex scientific and technical
  documents, especially those requiring multimodal interpretation (e.g., formulas
  in images), presents a significant hurdle for Large Language Models (LLMs)
  whose inherent error-correction tendencies can mask inaccuracies. This
  exploratory proof-of-concept (PoC) study investigates structured LLM context
  conditioning, informed by Persistent Workflow Prompting (PWP) principles, as a
  methodological strategy to modulate this LLM behavior at inference time. The
  approach is designed to enhance the reliability of readily available,
  general-purpose LLMs (specifically Gemini 2.5 Pro and ChatGPT Plus o3) for
  precise validation tasks, crucially relying only on their standard chat
  interfaces without API access or model modifications. To explore this
  methodology, we focused on validating chemical formulas within a single,
  complex test paper with known textual and image-based errors. Several prompting
  strategies were evaluated: while basic prompts proved unreliable, an approach
  adapting PWP structures to rigorously condition the LLM's analytical mindset
  appeared to improve textual error identification with both models. Notably,
  this method also guided Gemini 2.5 Pro to repeatedly identify a subtle
  image-based formula error previously overlooked during manual review, a task
  where ChatGPT Plus o3 failed in our tests. These preliminary findings highlight
  specific LLM operational modes that impede detail-oriented validation and
  suggest that PWP-informed context conditioning offers a promising and highly
  accessible technique for developing more robust LLM-driven analytical
  workflows, particularly for tasks requiring meticulous error detection in
  scientific and technical documents. Extensive validation beyond this limited
  PoC is necessary to ascertain broader applicability.
paper_ai_summary: Persistent Workflow Prompting is investigated as a method to enhance Large Language Models' reliability in identifying errors in scientific and technical documents through structured context conditioning.
paper_ai_keywords: Persistent Workflow Prompting structured LLM context conditioning LLM behavior Gemini 2.5 Pro ChatGPT Plus o3 chemical formulas error identification image-based formula error analytical workflows error detection