Corporate sustainability reports include a content index mapping each disclosure required by a reporting framework (e.g. GRI) to its supporting pages — a resource that potentially benefits nearly every downstream sustainability-NLP task, yet no existing benchmark scores its recovery as a framework-grounded, page-resolved output. We formalize Content Index Detection (CID) as three evaluable stages — index page retrieval (S1), table extraction and disclosure linking (S2), and page-label-to-index resolution (S3) — and release esg_cid_plus: 37 GRI reports and 7,294 rows stratified across three difficulty tiers, from unchanged real reports to an adversarial tier of naturally hard documents and synthetic perturbations. Our main benchmark sweeps open-weight vision-language models from sub-1B to 35B parameters (plus frontier-API references) as S2 extractors under shared, deterministic S1 and S3 stages: the best backends reach S2-F1 of 0.89–0.92 on the easier tiers, while standard OCR-benchmark scores explain only a third of the variance in extraction quality. Composing extraction with the deterministic resolver exposes S3 as the open frontier: under label-scheme resolution alone, where even best extraction caps end-to-end F1 at 0.11 on the hardest tier. esg_cid_plus will be released in huggingface.

Healthcare tasks such as predicting clinical outcomes across medical and surgical populations, disease prediction, predicting patient health journeys, are typically approached with supervised learning on task-specific datasets. We demonstrate that language models begin to learn these tasks without any explicit supervision when trained on a new dataset of billions of administrative claims, which essentially encapsulates the practice of medicine, offering a unique perspective on patient care and treatment patterns. Our model, MediClaimGPT, a 125M parameter Transformer demonstrates strong zero-shot predictive capabilities, accurately forecasting patient health events across four evaluation datasets, with its capabilities further demonstrated in various downstream tasks. A significant application of MediClaimGPT is in generating high-quality, clinically plausible synthetic claims data, enhancing healthcare data utility while preserving patient privacy. This research underscores the potential of language models in handling complex datasets and their strategic application in healthcare and related fields.