Compute Globally, Materialize Locally: The Memory Contract of Sparse Event-KV
A new arXiv paper examines how long‑horizon agents rely on retained KV cache entries as memory, finding that omitted observations still bias answers.
- Omitted observations still affect agent outputs through retained KV entries.
- Current eviction strategies may underestimate the semantic influence of cached data.
- Findings highlight potential bias risks in memory‑efficient serving systems.
The paper titled "Compute Globally, Materialize Locally: The Memory Contract of Sparse Event‑KV" investigates the assumptions behind KV‑cache eviction and episodic‑memory schemes used by long‑horizon agents. By deliberately omitting a prior observation from the served cache while keeping the rest of the agent's history unchanged, the authors measure the impact on downstream answers.
Results show that for items sensitive to the omitted observation, the model's output overwhelmingly follows the missing value, even though the served cache does not explicitly indicate which value is correct. This suggests that retained KV entries remain semantically informative despite the loss of their originating context.
The study raises concerns for systems that rely on selective caching to reduce compute costs, as the hidden dependencies may lead to unintended biases or errors. It also points to the need for more robust memory contracts that account for the influence of unseen observations.
Overall, the work contributes a novel empirical test of memory assumptions in transformer‑based agents, offering a foundation for future research on efficient yet reliable caching mechanisms.
Highlights hidden dependencies in cache‑based inference pipelines.
Implications for cost‑saving strategies that rely on KV eviction.
Signals emerging research challenges for scalable AI services.
Provides a concrete case study on memory contracts in transformer agents.
Shows that AI systems can retain hidden information even after data removal.
- KV cache
- Key‑value cache storing intermediate transformer activations for faster inference.
- Sparse Event‑KV
- A memory structure that stores only selected events to reduce compute while preserving important information.
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