Sharing KV-caches between AI agents instead of text summaries

What happened and what to do

The post describes research attributed to MIT CSAIL and IBM in which AI agents pass the raw KV-cache, the attention memory the model has already computed, to one another instead of handing off text summaries in English. According to the post's author, the approach yields a 40% gain in accuracy and four times faster speed. These figures are the author's claims and have not been verified here.

For a company running multi-agent pipelines, the key question is where cost and latency accumulate: each text handoff forces the next agent to reprocess context. A concrete test would start by measuring inference time, token consumption and response quality in the current flow, then compare text handoffs with sharing intermediate state on models and infrastructure the company controls. This requires an inference stack that allows access to and reuse of the cache, which hosted model APIs do not always permit; feasibility depends on the infrastructure chosen.

How the consultancy can help

Diagnosis of a multi-agent pipeline: mapping of context handoffs, measurement of latency, cost and quality, and assessment of whether sharing inference state is viable on your infrastructure. Then a scoped implementation of a comparison prototype and monitored operation.

Next step

Send a short description of your agent workflow, call volume and where you see cost or delay. Wendelmaques returns a scoped proposal, starting with a diagnosis before any implementation.