KACE: Knowledge-Adaptive Context Engineering for Mathematical Reasoning

arXiv:2606.00532v2 Announce Type: replace Abstract: Context-engineering methods improve a frozen language model by learning text from execution feedback and placing it in the prompt. When one learned prompt carries everything that was learned, irrelevant lessons compete with relevant ones, and the system can keep only what fits in that prompt. We present Knowledge-Adaptive Context Engineering (KACE), which decides per problem both how much inference to spend and which learned knowledge to show. A fixed tiered self-consistency schedule (2, 3, then 5 samples behind agreement gates) lets easy pro

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