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Language models exhibit strong reasoning capabilities, yet adapting them to structured domains remains challenging and can yield inconsistent outcomes. We identify representation compatibility, the extent to which a model effectively processes a representation for a structured task, as a key factor in adaptation. We st...
Executable families support repeatable benchmark renewal, with validation improving task quality and selection shaping the delivered challenge, with validation improving task quality and selection shaping the delivered challenge.
The results of AhaBench make continual learning an operational target: experience should yield capabilities that remain effective as guidance, inputs, and business states change.
This work presents ACT-Eval, an evaluation framework that decomposes chess commentary into atomic claims and routes them to engine-supported tools and expert-annotated gold references to assess factual correctness, conceptual coverage, and move-quality judgment.
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