LongForge: Measuring and Improving Long-Context Instruction Data via Evidence-Assertion Reasoning Graphs

arXiv:2610.23167v1 Announce Type: new Abstract: Existing quality control for long-context instruction-tuning data is a one-shot judgment inside the generation pipeline that leaves only a scalar, revealing neither which document parts an answer uses nor how many reasoning steps it writes down; the data can thus be neither audited nor controlled. We formalize data quality as a property of the alignment structure among document, question, and answer, and build LongForge, which pairs an analyzer that measures long-context data with a synthesizer that generates it. The analyzer reverse-reconstructs

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