INDRA is a research platform that integrates multiple archival collections—such as UCSF’s Industry Documents Library, Columbia and CUNY’s ToxicDocs, and Stanford’s SRITA—into a single, LLM‑readable corpus. It employs three safeguards: a closed evidentiary sandbox, real‑time provenance tagging, and a deterministic system‑level protocol to ensure that model outputs are clearly distinguished from archival evidence and from the model’s own inferences. The platform enables large‑language‑model‑powered investigations across these archives while keeping the conditions of knowledge production transparent and auditable.
By Daniel Akselrad, Robert N. Proctor
arXiv:2609.16519v1 Announce Type: new
Abstract: Scientific research increasingly relies on large, heterogeneous data sources, motivating interest in retrieval-augmented generation (RAG) systems that...
By Bernie Boscoe, Srinath Saikrishnan, Vikram Seenivasan, Jack Stark, Andrew Lizarraga, Morgan Himes, Jonathan Soriano, PJ Allen, Tuan Do
arXiv:2608.23263v1 Announce Type: new
Abstract: The FAIR Digital Object (FDO) framework mandates that metadata attribute values be expressed as persistent identifiers (PIDs) wherever possible, to pro...
By Zeyd Boukhers, Lingxiao Kong, Xenophon Zabulis, Georgios Toubekis
Scientific research increasingly relies on large, heterogeneous data sources, motivating interest in retrieval-augmented generation (RAG) systems that provide natural language access to scientific kno...
The FAIR Digital Object (FDO) framework mandates that metadata attribute values be expressed as persistent identifiers (PIDs) wherever possible, to produce a fully machine-actionable graph in which ev...
arXiv:2508. 12232v5 Announce Type: replace-cross Abstract: Issue-to-commit link recovery in software repositories is fundamental to software traceability and project management, yet it remains a challenging task.
By Arshia Akhavan, Alireza Hoseinpour, Abbas Heydarnoori, Hamid Bagheri, Mehdi Keshani