Show HN: Meadow Mind – a 7B diffusion LLM plays Gym games with zero training
A language-rule decision mind: write the policy as one sentence, describe the state as one sentence, and a local 7B model makes a real decision every ~0.4 s. Every frame below corresponds to one real model decision; no scripted policy, no edited speed-ups. Real-time reflex (wall-clock, not turn-based) The model runs in a thread while obstacles fall in real time.
- ▪A language-rule decision mind: write the policy as one sentence, describe the state as one sentence, and a local 7B model makes a real decision every ~0.4 s.
- ▪Every frame below corresponds to one real model decision; no scripted policy, no edited speed-ups.
- ▪Real-time reflex (wall-clock, not turn-based) The model runs in a thread while obstacles fall in real time.
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Meadow Mind Zero training. Second-level reactions (~400 ms). A language-rule decision mind: write the policy as one sentence, describe the state as one sentence, and a local 7B model makes a real decision every ~0.4 s. No RL, no reward engineering, no gradients, no samples. 🌐 Demo site: meadow-mind.pages.dev (中文) · English · 繁體中文 README pip install meadow-mind # weights auto-download on first use from meadow_mind import MeadowMind, tasks mind = MeadowMind() # loads once, runs on-device task = tasks.mountaincar() mind.check(task) # sanity gate: decision-table exam action, info = mind.decide(task, obs) # obs in, env action out (~0.4s) Results All on official Gymnasium environments, untouched physics, zero training.
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