Stories about OEIS
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Where Induction Runs Out: Description-Length Difficulty and the Memorisation Gap in Integer-Sequence Benchmarks
AI InsightUsing an exactly computable MDL reference model, the study analyzes OEIS integer-sequence benchmarks, finding that MDL difficulty is essentially a parameter count and that the discovery point is exactly predicted by a combinatorial identifiability bound, independent of term magnitude. This suggests current benchmarks may measure memorization rather than true inductive reasoning.Key TakeawayCompared to prior benchmark evaluations based solely on model performance, this work introduces computable MDL to reveal that benchmarks measure memorization rather than induction.Why It MattersIt directly questions the validity of common math reasoning benchmarks and provides a theoretical tool for improving benchmark design and model evaluation.Who's Affected- AI ResearchersGain a computable reference model to re-evaluate the true difficulty of sequence reasoning benchmarks.
- DevelopersReminded to distinguish memorization from reasoning when training or evaluating models, avoiding overfitting to OEIS-like benchmarks.
What's NextWatch whether the MDL method is applied to other math benchmarks and how model performance differs between real inductive tasks and OEIS.Importance 75/100