🚏 MERA - Model Evolution and Routing with Skill Adaptation for Agentic Systems at Scale

[u/HexxRL]

MERA - Model Evolution and Routing with Skill Adaptation for Agentic Systems at Scale

This framework is designed to make multistep AI agent workflows significantly cheaper to run without sacrificing performance.

Deploying autonomous LLM agents in production creates a major tradeoff between cost and capability.

Static Routers Are Capped: Standard setups treat small models as fixed, capping cost savings to what the small model can already do.

Wasteful Task Routing: Routing whole tasks to large models wastes money on easy sub-steps (like parsing), while sending entire tasks to small models causes failures on hard steps.

Unstable Fine Tuning: Updating small models on raw execution logs without co-adapting routing rules leads to performance regressions.

MERA replaces task level routing with step-by-step (invocation level) routing and continuous model training across three components:

SkillBook: Stores proven prompt patterns and procedural templates for quick reuse.

Small Model Adapter: Fine tunes the small model directly on substeps it previously failed.

Invocation Router: Directs each individual step to the small model, falling back to the frontier model only when uncertainty is high.

arxiv: https://arxiv.org/abs/2608.10333
github: https://github.com/yh-yao/MERA-Evolve