arXiv:2606.13361v1 Announce Type: new Abstract: Right now, across the world, AI agents are repeating the same absurd act: to read one document, they each recompute it from scratch. Every agent re-runs prefill, the most compute-intensive step a large model takes, over identical text, only to rebuild a key-value (KV) cache identical to the one the agent before it just built. The same answer, computed a million times. We make a proposal that is almost offensively simple: compute it once. Let a publisher precompute a document's KV cache, and let every other agent buy the right to load it and skip
The proliferation of AI agents and the increasing computational demands of large models make the repetitive re-computation of KV caches an unsustainable and inefficient practice.
This proposal addresses a critical bottleneck in AI agent efficiency and scalability, potentially leading to significant cost reductions and faster processing for AI-driven applications.
The paradigm shifts from every AI agent individually re-computing identical data to a model where pre-computed KV caches can be shared and potentially monetized, reducing duplicate compute effort on a massive scale.
- · AI service providers
- · Cloud compute providers
- · AI agent developers
- · AI infrastructure companies
- · Inefficient AI compute models
- · Companies with high redundant compute costs
AI agents become significantly more efficient, reducing inference costs and latency.
A new market emerges for pre-computed, shared KV caches, potentially creating specialized data services.
Increased accessibility and affordability of AI agent deployment could accelerate the development and adoption of AI-driven automation across various sectors.
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Read at arXiv cs.AI