aiqbench analysis
This is an early-stage research paper from a NeurIPS workshop proposing a way to select scientific documents for training corpora by tracing each choice to a reference knowledge graph, rather than relying on opaque relevance scores. The benchmarks, comparisons, and downstream results needed to judge it are not in the abstract, so the claim of better auditability remains untested. Readers building scientific training data should watch whether this traceability holds up at scale.
SciGraphSelect: Auditable Scientific Data Selection through Knowledge--Reasoning Graphs
arXiv:2610.41280v1 Announce Type: new Abstract: Scientific data selection is typically driven by relevance or utility scores, providing limited insight into why a document is retained. We introduce SciGraphSelect, a reference-grounded framework for auditable scientific data selection. SciGraphSelect organizes domain-specific knowledge, reusable reasoning operations, and reference-attested relations into a typed knowledge--reasoning graph, then scores candidate documents using concept coverage, relation and path co-support, and directional alignment. To avoid over-interpreting document embeddings, rich relational structure is preserved on the reference side while candidate-side evidence is treated conservatively without assuming relation entailment. By exposing the reference-grounded evidence behind each selection, SciGraphSelect brings traceability to the upstream construction of scientific training corpora and complements downstream scientific verification.
Abstract:Scientific data selection is typically driven by relevance or utility scores, providing limited insight into why a document is retained. We introduce SciGraphSelect, a reference-grounded framework for auditable scientific data selection. SciGraphSelect organizes domain-specific knowledge, reusable reasoning operations, and reference-attested relations into a typed knowledge--reasoning graph, then scores candidate documents using concept coverage, relation and path co-support, and directional alignment. To avoid over-interpreting document embeddings, rich relational structure is preserved on the reference side while candidate-side evidence is treated conservatively without assuming relation entailment. By exposing the reference-grounded evidence behind each selection, SciGraphSelect brings traceability to the upstream construction of scientific training corpora and complements downstream scientific verification.
40th Conference on Neural Information Processing Systems (NeurIPS 2026). Workshop: AI4Science
Artificial Intelligence (cs.AI); Computation and Language (cs.CL)
arXiv:2610.41280 [cs.AI]
(or arXiv:2610.41280v1 [cs.AI] for this version)
https://doi.org/10.48550/arXiv.2610.41280 Focus to learn more arXiv-issued DOI via DataCite (pending registration)
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Full text under the publisher's open licence · 217 words