Token-Efficient Data Reasoning Agents via Adaptive Structuring of Unstructured Data
The paper’s core claim is that agents can make unstructured-document reasoning cheaper by saving structure as they read.
The proposed “agentic data cracking” method extracts reusable, grounded structure whenever an agent opens a document for a query. Future related questions can then hit that structured store instead of rereading the source text. On FanOutQA with one related question added per test question, the authors report a 53% cost reduction while preserving accuracy. HF Daily Papers' note
The proposed “agentic data cracking” method extracts reusable, grounded structure whenever an agent opens a document for a query. Future related questions can then hit that structured store instead of rereading the source text. On FanOutQA with one related question added per test question, the authors report a 53% cost reduction while preserving accuracy. HF Daily Papers' note
score 5