KACE: Knowledge-Adaptive Context Engineering for Mathematical Reasoning
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 problems exit after two agreeing samples, and the tier at which a problem exits serves as its difficulty label. Offline, a teacher-guided learner distil...
| Authors | Jayant Parashar, Suchendra M. Bhandarkar |
|---|---|
| Primary category | cs.AI |
| Published | 30 May 2026 |
| Last updated at source | 15 October 2026 |
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