pith:A4NJWH45
AdapShot: Adaptive Many-Shot In-Context Learning with Semantic-Aware KV Cache Reuse
AdapShot selects the optimal number of in-context examples for each query by measuring output entropy in a probe run and reuses KV cache with reordering to enable efficient many-shot learning.
arxiv:2605.03644 v2 · 2026-05-05 · cs.AI
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Claims
Extensive experiments demonstrate that AdapShot achieves an average performance gain of around 10% and a 4.64x speedup compared to state-of-the-art DBSA.
That output entropy from a short probe run is a sufficient and unbiased signal for selecting the globally optimal shot count, and that the decoupling-plus-re-encoding step for KV cache reordering introduces no accuracy degradation.
AdapShot adaptively tunes shot count via entropy probes and reuses semantically-matched KV caches with position decoupling to deliver ~10% accuracy gains and 4.64x speedup over fixed-shot baselines.
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| First computed | 2026-06-03T01:05:50.890531Z |
|---|---|
| Builder | pith-number-builder-2026-05-17-v1 |
| Signature | Pith Ed25519
(pith-v1-2026-05) · public key |
| Schema | pith-number/v1.0 |
Canonical hash
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· · · · ·Agent API
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Canonical record JSON
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