OmniCoder-9B-Zero-Phase2

CARL Phase 1' — VLM grounding checkpoint. EVAL: PASS (94.6% click accuracy).

A LoRA adapter trained with vision GRPO for GUI grounding. The model understands screenshots and produces structured coordinate output for click targets.

Results

Metric Value
Click accuracy 94.61%
Format compliance 100%
Eval samples 167 held-out
Status PASS

Training

Phase Transition Observed

During SFT, the model exhibited a first-order phase transition:

  • Steps 0-10: Baseline (3% accuracy, entropy 1.0)
  • Steps 10-20: Melting (entropy spikes to 9.3)
  • Steps 20-25: Transition (accuracy jumps 57 points in 5 steps)
  • Steps 25-35: Crystallization (99% accuracy, entropy 0.4)
  • Steps 35-46: Converged (99.3%, entropy 0.12)

Consistent with Kuramoto synchronization in coupled oscillator systems.

Theoretical Foundation

  1. Bounded Informational Time Crystals — DOI: 10.5281/zenodo.18906944
  2. Material Reality — DOI: 10.5281/zenodo.18992029
  3. Semantic Realizability — DOI: 10.5281/zenodo.18992031

Usage

from transformers import AutoModelForImageTextToText, AutoProcessor
from peft import PeftModel

base = AutoModelForImageTextToText.from_pretrained(
    "Tesslate/OmniCoder-9B",
    torch_dtype="bfloat16",
    device_map="cuda:0",
)
model = PeftModel.from_pretrained(base, "wheattoast11/OmniCoder-9B-Zero-Phase2")
model = model.merge_and_unload()

processor = AutoProcessor.from_pretrained(
    "Tesslate/OmniCoder-9B",
    min_pixels=256*28*28,
    max_pixels=1280*28*28,
)

Citation

@article{desai2026carl,
  title   = {Coherence-Aware Reinforcement Learning},
  author  = {Desai, Tej},
  year    = {2026},
  url     = {https://github.com/wheattoast11/carl},
  note    = {Intuition Labs LLC}
}

License

Apache 2.0 — Intuition Labs LLC

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