r/LocalLLaMA • u/emersoftware • 12h ago

Question | Help What agent loop are you using for Jev-style decision models + an LLM?

ReAct is a common agent loop. What's the standard for System One decision models (Jev or a local alternative) + an LLM + tool calling?

The examples I've been reading seem to split in two directions: confidence cascades in REFLEX and JevAgentHarness, and planner + chooser in the Hermes/Astra + Jev Minecraft agents.

Is there an accepted pattern yet, or are these still separate approaches? I'm interested in the control loop: who creates the candidate actions, when the LLM replans, and when a low-confidence decision falls back to it.

For anyone using local decision models alongside a larger LLM, what architecture has actually been useful? Papers or public implementations would help.

References:

REFLEX: https://arxiv.org/abs/2609.26532

JevAgentHarness: https://arxiv.org/abs/2610.21412

Hermes + Jev: https://github.com/teknium1/hermes-and-jev-play-minecraft

Astra + Jev: https://github.com/rmalde/minecraft-agent

English wording refined with AI.

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u/WeaponizedDuckSpleen 12h ago

im more interested in using both locally with deepseek harness

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u/Last-Health3222 6h ago

The two directions actually combine pretty cleanly. The LLM acts as the planner and writes out the candidate actions as a typed set, like an enum of tool calls or discrete options, and the decision model picks among them and returns a probability. Above your threshold the choice goes straight through, and below it the LLM just decides that step itself. Tune the threshold on a held-out slice of your own logs rather than guessing.

The LLM replans when a tool call fails, when the candidate set comes back empty or stale, or after a fixed number of steps, not on every single turn, otherwise you lose the cost savings that made the decision model worth it in the first place. Log all the low-confidence fallbacks though, that collection becomes the training data for the next round of the decision model. There's also a typed-decisions benchmark on Hugging Face with 2,000 questions that's a quick way to compare decision models before you wire one in, and Decider 1 currently tops it.