concept
active
concept:task-difficulty-operationalized-as-the-number-of-discrete-operations-required-to-verify-correctness-of-the-inputTask difficulty operationalized as the number of discrete operations required to verify correctness of the input.
The paper's specific operationalization of task difficulty enabling controlled experiments.
Neighborhood — ranked by edge-count
Frameworks (2)
framework
- Factual task hierarchy (F0–F5)implementsA controlled six-level hierarchy of factual tasks increasing in complexity from simple city-location recall to double-counting constraints.
- Arithmetic task hierarchy (A1–A3)implementsThree synthetic arithmetic datasets of increasing complexity requiring 1, 2, or 3 operations to verify correctness.
Related by similarity (8)
cosine ≥ 0.65 · no typed edgeEntities in the same semantic neighborhood but without a typed relation to this one — candidates for new edges or unrecognized duplicates.
- The paper identifies task difficulty as a key moderator: easy MMLU questions show performative CoT, hard GPQA-Diamond questions show genuine reasoning
- Task difficulty as the key variable distinguishing the two modes of CoT identified in the paper
- Motivation for the proposed method.
- Russell's statement opening Section 2 articulating the core motivation for the Contemplative AI approach
- The ability to generalize across tasks; lacking in latent methods.
- A combinatorial argument that good sequences are astronomically rare, emphasizing the difficulty of discovery.
- Specific sub-question investigated in Appendix B.4 by creating intermediate task variants.
- Design hypothesis that coarse-grained task switching (at commands only) eliminates need for protection mechanisms while maintaining usability.