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arxiv: 2605.07022 · v3 · pith:QXYMIEQDnew · submitted 2026-05-07 · 💻 cs.LG

Self-Driving Datasets: From 20 Million Papers to Nuanced Biomedical Knowledge at Scale

Haydn Jones , Yimeng Zeng , Alden Rose , Li S. Yifei , Yining Huang , Kaiwen Wu , Jiaming Liang , Maggie Ziyu Huan
show 6 more authors
Yoseph Barash Cesar de la Fuente-Nunez Osbert Bastani Zachary Ives Mark Yatskar Jacob R. Gardner
This is my paper

Pith reviewed 2026-06-30 22:59 UTC · model grok-4.3

classification 💻 cs.LG
keywords biomedical knowledge extractionLLM dataset generationPubMed miningmulti-agent systemsentity taggingstructured literature datablood-brain barrieroral bioavailability
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The pith

An LLM tagging pipeline and multi-agent system called Starling turn PubMed into structured biomedical datasets larger and more accurate than manually curated ones.

A machine-rendered reading of the paper's core claim, the machinery that carries it, and where it could break.

The paper sets out to show that PubMed's full text can be processed at scale into structured records for biomedical properties using automated entity tagging across ontologies followed by a multi-agent extraction system. This produces millions of records that retain experimental context usually lost in tables and exhibit lower error rates when evaluated by frontier models than standard curated sources. A sympathetic reader would care because it offers a way to keep pace with the literature without the expense and delays of manual curation while preserving the details needed for reliable downstream use in therapeutic design.

Core claim

The central claim is that PubMed itself can be autonomously and cost-effectively turned into structured datasets that are larger, more nuanced, and more accurate than the curated databases they replace, through an LLM-based entity-tagging pipeline on a 22.5M-paper corpus, hybrid retrieval, and Starling, a multi-agent system that designs filters and schemas from natural-language task descriptions to emit records with supporting passages.

What carries the argument

Starling, the multi-agent deep research system that designs precision- and recall-targeted retrieval filters, induces extraction schemas, and emits structured records with nuance-rich fields and supporting passages from the tagged corpus.

If this is right

  • Across the six tasks the generated datasets reach sizes of 91K to 3M records and are the largest public sets for several properties.
  • Extracted records include experimental nuance such as fed versus fasted state for oral bioavailability that tabular databases discard.
  • The tagged corpus and hybrid retrieval enable entity-filtered semantic queries over the full PubMed literature.
  • The combined corpus, retrieval, and agent system provide a scalable foundation for AI-driven therapeutic design.

Where Pith is reading between the lines

These are editorial extensions of the paper, not claims the author makes directly.

  • The same pipeline could be adapted to maintain continuously updated datasets as new papers enter PubMed.
  • Downstream predictive models for drug properties might improve if trained on the retained experimental context rather than cleaned tabular entries alone.
  • The approach suggests a general pattern for turning any large, tagged scientific corpus into task-specific structured knowledge without repeated manual curation.
  • Error rates could be further lowered by iterating the rejection sampling loop or by incorporating additional domain-specific validation agents.

Load-bearing premise

The assumption that frontier-model rejection sampling gives a reliable and unbiased measure of true extraction error rates that can be fairly compared to error rates measured on the cited curated databases.

What would settle it

A domain-expert manual audit of random samples from Starling's outputs and from the compared curated databases, using identical protocols, to count actual extraction errors.

Figures

Figures reproduced from arXiv: 2605.07022 by Alden Rose, Cesar de la Fuente-Nunez, Haydn Jones, Jacob R. Gardner, Jiaming Liang, Kaiwen Wu, Li S. Yifei, Maggie Ziyu Huan, Mark Yatskar, Osbert Bastani, Yimeng Zeng, Yining Huang, Yoseph Barash, Zachary Ives.

Figure 1
Figure 1. Figure 1 [PITH_FULL_IMAGE:figures/full_fig_p003_1.png] view at source ↗
Figure 2
Figure 2. Figure 2: Within-mol η 2 ANOVA effect size statistics for each task / conditioning variable: the fraction of a molecule’s label variance explained by that single covariate, averaged across molecules with ≥ 2 extractions covering ≥ 2 subcategories. For example, “Dosing route” splits each molecule’s LD50 measurements into {oral, injection, inhalation, dermal, . . .} and asks how much of that molecule’s lethal-dose var… view at source ↗
Figure 3
Figure 3. Figure 3: 20 [PITH_FULL_IMAGE:figures/full_fig_p020_3.png] view at source ↗
Figure 3
Figure 3. Figure 3: The five natural-language task prompts given to each model. Minor wording variations [PITH_FULL_IMAGE:figures/full_fig_p021_3.png] view at source ↗
Figure 4
Figure 4. Figure 4: One of the FilterSpec probes the Proposer constructed for the BBB task. entity_groups is a CNF entity constraint (outer AND, inner OR); semantic_query is the rerank target applied to the surviving windows. H Additional Results Measuring Disagreement for Individual Molecules In this section, we present two small case studies that measure the variability of BBB, Oral and LD50 labels that exists for single mo… view at source ↗
Figure 4
Figure 4. Figure 4: One of the FilterSpec probes the Proposer constructed for the BBB task. entity_groups is a CNF entity constraint (outer AND, inner OR); semantic_query is the rerank target applied to the surviving windows. agrees (<5%) 5 20% 20 50% 50 80% 80% Fraction of molecule's extractions that disagree with TDC label 0 20 40 60 % of TDC Molecules 68.3% 10.7% 8.5% 5.4% 7.2% 41.4% 19.8% 14.3% 12.8% 11.8% 64.3% 4.4% 10.4… view at source ↗
Figure 5
Figure 5. Figure 5: For each labeled molecule in three TDC datasets, what fraction of [PITH_FULL_IMAGE:figures/full_fig_p029_5.png] view at source ↗
Figure 6
Figure 6. Figure 6: For each molecule with ≥ 5 extractions, what fraction of the extractions would have the + label in the corresponding TDC datasets? individual labels? In [PITH_FULL_IMAGE:figures/full_fig_p029_6.png] view at source ↗
read the original abstract

Manually curated biomedical repositories -- spanning bioactivity, genomics, and chemistry -- are expensive to maintain, lag behind primary literature, and discard experimental context, obscuring nuances needed to assess data correctness and coverage. We show that PubMed itself can be autonomously and cost-effectively turned into structured datasets that are larger, more nuanced, and more accurate than the curated databases they replace. We present three coupled contributions: (1) an LLM-based entity-tagging pipeline, grounded in nine biomedical ontologies, that tags 4.5B entities across 19 categories in a 22.5M-paper, 2.5T-token PubMed corpus; (2) hybrid sparse-dense retrieval supporting entity-filtered semantic queries over the tagged corpus; and (3) Starling, a multi-agent deep research system that, given only a natural-language task description, designs precision- and recall-targeted retrieval filters, induces an extraction schema, and emits structured records with nuance-rich fields and supporting passages. Across six tasks -- blood-brain barrier permeability, oral bioavailability, acute toxicity (LD50), gene-disease associations, protein subcellular localization, and chemical reactions -- Starling produces ~6.3M records (91K-3M per task); several are, to our knowledge, the largest public datasets for their property. Frontier-model rejection of our extractions is 0.6-7.7% across tasks, far below error rates we measure on widely used curated counterparts (e.g., 16.5% on BBB_Martins, 7.3% on Bioavailability_Ma). Beyond scale and accuracy, the supporting passages carry nuance tabular databases discard -- e.g., oral bioavailability may depend on fed vs. fasted state. Together, the corpus, retrieval, and agent establish a foundation for AI-driven therapeutic design. Code and datasets: https://github.com/starling-labs/starling.

Editorial analysis

A structured set of objections, weighed in public.

Desk editor's note, referee report, simulated authors' rebuttal, and a circularity audit. Tearing a paper down is the easy half of reading it; the pith above is the substance, this is the friction.

Referee Report

3 major / 2 minor

Summary. The paper claims that an LLM-based entity-tagging pipeline applied to a 22.5M-paper PubMed corpus, combined with hybrid retrieval and the Starling multi-agent system, can autonomously generate structured biomedical datasets for six tasks (BBB permeability, oral bioavailability, LD50 toxicity, gene-disease associations, protein localization, chemical reactions) that are substantially larger (~6.3M records total) and more accurate (0.6-7.7% frontier-model rejection) than existing manually curated databases, while also preserving experimental nuance discarded by tabular repositories.

Significance. If the accuracy and scalability claims hold after rigorous validation, the work would provide a practical foundation for continuously updated, context-rich biomedical knowledge bases directly from primary literature, with immediate utility for therapeutic design and reduced dependence on expensive manual curation efforts.

major comments (3)
  1. [Abstract] Abstract: The central accuracy claim (frontier-model rejection of 0.6-7.7% on Starling outputs vs. 16.5% on BBB_Martins and 7.3% on Bioavailability_Ma) is load-bearing for the assertion of superior quality, yet the manuscript supplies no description of the rejection protocol (prompts, model versions, decision criteria), no calibration against human ground truth, and no inter-rater reliability metrics.
  2. [Abstract] Abstract and results sections: The higher rejection rates measured on curated databases are presented without explicit controls for data-subset matching, field alignment, source distribution, or experimental-condition filtering, leaving open the possibility that observed differences reflect protocol mismatch rather than intrinsic error rates.
  3. [Methods] Methods (entity-tagging and Starling pipeline): The 4.5B-entity tagging step and the multi-agent extraction both rely on LLMs, but no ablation or sensitivity analysis is reported on how tagging errors propagate to final record quality or how post-hoc filtering affects the reported dataset sizes and rejection statistics.
minor comments (2)
  1. [Abstract] Abstract: The title states '20 Million Papers' while the text states '22.5M-paper'; a single consistent figure should be used.
  2. The GitHub link is provided, but the manuscript does not indicate whether the exact prompts and rejection-sampling code used for the accuracy numbers are included in the release.

Simulated Author's Rebuttal

3 responses · 0 unresolved

We thank the referee for the detailed and constructive feedback. The comments identify important areas where additional transparency and controls will strengthen the manuscript. We address each point below and will incorporate revisions to improve clarity on evaluation protocols, comparison fairness, and pipeline robustness.

read point-by-point responses

  1. Referee: [Abstract] Abstract: The central accuracy claim (frontier-model rejection of 0.6-7.7% on Starling outputs vs. 16.5% on BBB_Martins and 7.3% on Bioavailability_Ma) is load-bearing for the assertion of superior quality, yet the manuscript supplies no description of the rejection protocol (prompts, model versions, decision criteria), no calibration against human ground truth, and no inter-rater reliability metrics.

    Authors: We agree that the rejection protocol details are insufficiently described. We will add a dedicated subsection in the Methods section specifying the exact prompts, model versions (GPT-4o and Claude-3.5-Sonnet), and binary decision criteria based on factual consistency with the source passage. No human ground-truth calibration or inter-rater reliability metrics were performed; the evaluation relies exclusively on automated frontier-model checks. We will explicitly note this as a methodological limitation in the revised Discussion and will include a small-scale (n=200 per task) human agreement study if space permits. revision: yes

  2. Referee: [Abstract] Abstract and results sections: The higher rejection rates measured on curated databases are presented without explicit controls for data-subset matching, field alignment, source distribution, or experimental-condition filtering, leaving open the possibility that observed differences reflect protocol mismatch rather than intrinsic error rates.

    Authors: We acknowledge that the current comparison lacks explicit matching controls. In the revision we will add a new paragraph and supplementary table detailing the subset selection process: we filter curated records to the same property definitions, restrict to papers published in overlapping journals/years, and align experimental conditions where metadata permits. These controls will be applied before recomputing rejection rates to ensure the comparison isolates intrinsic quality differences. revision: yes

  3. Referee: [Methods] Methods (entity-tagging and Starling pipeline): The 4.5B-entity tagging step and the multi-agent extraction both rely on LLMs, but no ablation or sensitivity analysis is reported on how tagging errors propagate to final record quality or how post-hoc filtering affects the reported dataset sizes and rejection statistics.

    Authors: We agree that propagation analysis is missing. Because full re-tagging of the 22.5 M corpus is computationally prohibitive, we will instead add a sensitivity study on a 50 k-paper subsample that quantifies how entity-tagging F1 variations affect downstream record yield and rejection rates. We will also report the fraction of records removed by each post-hoc filter and the resulting change in rejection statistics. These results will appear in a new supplementary figure and table. revision: yes

Circularity Check

0 steps flagged

No circularity; empirical extraction and external comparisons are self-contained

full rationale

The paper describes an LLM-based tagging and multi-agent extraction pipeline applied to PubMed, followed by direct measurement of frontier-model rejection rates on the resulting records and on external curated databases (e.g., BBB_Martins, Bioavailability_Ma). No equations, parameters, or uniqueness claims are defined in terms of the target outputs. No self-citations are invoked as load-bearing mathematical facts. The accuracy comparison is an independent empirical measurement rather than a fitted or self-referential quantity. The derivation chain therefore contains no reductions to inputs by construction.

Axiom & Free-Parameter Ledger

0 free parameters · 1 axioms · 1 invented entities

The central claim rests on the unproven reliability of frontier LLMs for precise biomedical entity tagging and nuanced extraction at scale; no free parameters or invented physical entities are introduced, but the Starling system itself is a new engineered component whose performance is not independently verified outside LLM self-evaluation.

axioms (1)
  • domain assumption Frontier LLMs can perform accurate entity tagging and structured extraction from biomedical text when guided by ontologies and multi-agent workflows
    Invoked throughout the pipeline description as the foundation for tagging 4.5B entities and emitting records with low rejection rates.
invented entities (1)
  • Starling multi-agent deep research system no independent evidence
    purpose: Designs precision/recall filters, induces extraction schemas, and emits structured records with supporting passages
    New system introduced to orchestrate the extraction; no independent evidence provided beyond the reported outputs.

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