What it measures
True/False claim verification over recently published full-length English novels (60k–300k tokens) requiring global plot comprehension.
Reading the measurement
Length is a test condition, not a capability score. Locate the relevant evidence, then examine whether the model resolves conflicts and combines details correctly. Keep document extraction, input length and answer grading fixed.
Published evaluation
NoCha · approximately 128k; NoCha · matched-length comparison. The source’s task split, model configuration and scoring units are retained.
Where this fits in an evaluation suite
A large context window is a capacity specification. Successful retrieval and cross-document reasoning are observed capabilities. Test both at the actual lengths your workflow needs.
Start with a capability hypothesis, then choose a second test with a different failure mode. Keep the checkpoint, reasoning effort, agent tools, sampling policy, and scoring procedure fixed when measuring a training intervention. Record failures and missing outputs in the denominator.
A reproducible comparison
NoCha · approximately 128k; NoCha · matched-length comparison. The source’s task split, model configuration and scoring units are retained.
Publish the exact run configuration, item coverage, score units, and available uncertainty. If a source supplies only an aggregate or a single run, preserve that limit. A new model name or a rounded score alone does not establish a capability improvement.
PRIMARY EVIDENCE & FURTHER READING
Reviewed 2026-10-26. Explanation and interpretation by RLTasks. Benchmark ownership remains with its original authors.