EACache: Entailment-Aware Caching facilitating AI Image Generation

Text-to-image generation is computationally expensive, motivating the reuse of previously generated images through caching. Similarity-based cache retrieval is widely used, but image–text similarity may not always accurately indicate whether a cached image contains sufficient content to satisfy a user request. This distinc- tion is especially important for general prompts, where a valid cached image may contain substantial additional content while still satisfying the requested content, which reflects the entailment effect. We introduce EACache, an entailment-aware caching framework that combines similarity-based retrieval with selective entail- ment verification (e-verification). EACache derives two similarity thresholds with the objective of minimizing the expected system cost of serving a query. When the similarity score exceeds the higher threshold, the cached image is returned directly; when the score falls below the lower threshold, a new image is generated; and when the score lies between the two thresholds, e-verification is performed to determine whether the cached image can be reused. The threshold derivation jointly accounts for the costs of e-verification, image generation, transmission, and recovery from an incorrect cache-hit decision. To reduce online e-verification overhead, EACache captions each image when it is added to the cache and performs textual entailment between the stored caption and the incoming query at runtime. Experiments show that similarity remains an informative signal but cannot cleanly separate reusable from non-reusable candidates, while selective e-verification recovers valid cache hits missed by similarity-only reuse without requiring verification for every request. The optimal threshold analysis further provides practical guidance for selecting an appropriate entailment model for caching system design.