Massive paper from Meta.

I like this one because it shows the use of agent harnesses for production-grade recommender systems.

Details below:

This is one of the more convincing agent deployments I've seen.

It runs against a live production recommender serving billions of people and reports A/B results.

Sustaining a recommender is continual optimization work. Content shifts, user behavior shifts, upstream models shift, and the choices governing retrieval, ranking and serving have to be revisited.

Human engineers test those changes through online experiments, which is slow enough that parts of the system go unrevised.

In CORAL, each cycle the agent observes operating signals, reasons over a memory of past decisions and their measured outcomes, and invokes tools including a numerical optimizer that keeps every change inside a fixed operating budget.

The policy improves in context from its own prior actions, with no parameter updates.

Across two large social platforms, the same harness improves engagement at no additional serving cost on one and reduces serving cost without degrading engagement on the other.

Performance improves as the loop iterates.

The guardrail design carries as much weight as the agent. A bounded change budget makes this safe to run against production.

Paper: https://t.co/G46EgVuPMR

Chat with Paper: https://t.co/KlYFT8dAFD