RESEARCHMonitorWATCHLIST
The Data Differentiator Framework (DDF): Quantifying Marginal Data Value with Information-Theoretic and Game-Theoretic Measures in an Agentic Evaluation Pipeline (SDSAE)
arXiv cs.LG — Machine Learning
Factual evidence
What the source reports
Researchers introduced the Data Differentiator Framework to quantify the marginal value and cost-effectiveness of new dataset acquisitions.
Inspect the evidence
- Inclusion basis
- Enterprise AI
- Publisher and source type
- arXiv cs.LG — Machine Learning · RESEARCH
- Published by source
- 22 October 2026
- Collected by OneBench
- 23 Oct 2026, 03:02 UK
Stored source excerpt
arXiv:2610.28933v1 Announce Type: new Abstract: In data-rich enterprises the hard question is not whether a candidate dataset is good in isolation, but whether it adds…
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