Etemaro: An LLM Agent with Federated Experiential Learning for Autonomous Concentrated-Liquidity Management on Solana

We present Etemaro, an autonomous architecture that utilizes an LLM inside a ReAct reasoning loop to screen candidate pools, deploy and manage DLMM positions, and execute exits based on live profit-and-loss, yield, and bin drift data. Etemaro introduces three primary contributions beyond standard trading-agent frameworks: (1) a Darwinian signal-weighting mechanism that reweights heuristic screening signals online using a quartile-lift statistic computed from realized win/loss outcomes; (2) role-scoped and intent-filtered tool access, strictly constraining which on-chain actions the LLM may execute across screening, management, and chat contexts; and (3) HiveMind, a lightweight federated experiential-learning protocol where independent agent instances exchange natural-language lessons and scored strategy presets without sharing raw transaction logs, private keys, or model weights.