MERMaid: Universal multimodal mining of chemical reactions from PDFs using vision-language models
Why this work is in the frame
A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.
Bibliographic record
Abstract
Data digitization of scientific literature is essential for creating machine-actionable knowledge bases to advance data-driven research and integrate with self-driving laboratories. It is especially critical to extract, interpret, and structure data from graphical elements, the primary medium for conveying complex scientific insights. However, this remains challenging due to the inherent lack of semantic structure in the prevalent PDF format, the complexity of visual content, and the need for multimodal integration. We present MERMaid (multimodal aid for reaction mining), an end-to-end pipeline that converts disparate visual data across PDFs into a coherent knowledge graph. Leveraging the emergent visual cognition and reasoning capabilities of vision-language models, MERMaid demonstrates chemical context awareness, self-directed context completion, and robust coreference resolution to achieve 87% end-to-end accuracy across three chemical domains. Its modular design facilitates future application to diverse data beyond reaction mining, promising to unlock the full potential of scientific literature for knowledge-intensive applications.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.004 | 0.000 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from it