ArbiterOmni — Multimodal System 1 Decision Engine

ArbiterOmni is an open-source, non-autoregressive multimodal decision-making model designed for real-time, safety-critical arbitration across vision, language, video, and audio.

Rather than generating verbose text autoregressively (800–3,000 ms), ArbiterOmni performs single-pass decision arbitration (0.5–15 ms) with calibrated softmax probabilities, Shannon entropy uncertainty, and finite-sample 95% conformal prediction coverage guarantees.

Model Family & Releases

Checkpoint	Parameters	Checkpoint Size (FP16)	Checkpoint Size (FP32)	Manifold Dim	MoE Architecture	Optimization & Training	Target Deployment
ArbiterOmni v6	30.34 M trainable (212 M frozen)	86.22 MB	115.80 MB	512-dim	4 Layers x (1 Shared + 4 Routed Experts)	2 Epochs, DirectML GPU	Edge robotics, embedded real-time safety gates
ArbiterOmni v6-Max	103.88 M trainable (212 M frozen)	198.23 MB	396.36 MB	768-dim (Native SigLIP)	4 Layers x (1 Shared + 7 Routed Experts = 32 Experts)	10 Epochs, CosineAnnealingLR + SWA, DirectML-Native AdamW	High-capacity autonomous systems, surgical AI, enterprise triage

Architectural Highlights

1. Native 768-Dim Manifold (v6-Max)

v6-Max operates natively in SigLIP's uncompressed 768-dimensional latent space (hidden_dim = 768, scoring_dim = 768), completely removing the 512-dim bottleneck of previous versions.

2. DeepSeek-V3 Style Shared + Domain-Specialized MoE

1 Shared Invariant Expert per layer: Always active (100% routing frequency), preserving universal cross-modal representations.
7 Domain-Specialized Routed Experts per layer (32 total experts across 4 transformer layers): Dispatched via Top-2 soft routing with Switch auxiliary load-balancing loss.

3. Tier-0 Speculative Early-Exit Arbiter

A lightweight 128-dim draft projection scores candidates directly from GPU L1/L2 cache. High-confidence states exit in <0.5 ms; uncertain states route to the full MoE pathway.

4. Test-Time Deliberation Tournament (TTC) & Conformal Risk Control

Candidates compete against semantic foils dynamically harvested from a 100,000-candidate resident memory bank in shared host DDR4 RAM. The model returns certified 95% conformal prediction sets and automatically triggers a System 2 escalation gate when decision entropy exceeds empirical calibration thresholds.

Training Progression & Empirical Performance (v6-Max)

Trained for 10 full epochs (8.66 hours) on an AMD Radeon RX 480 GPU via DirectML-native AdamW:

Metric	Epoch 1 (Warmup)	Epoch 3 (Peak Val)	Epoch 6 (Mid)	Epoch 10 (SWA Final)	Total Relative Change
Training Loss	58.67	58.28	51.45	36.07	-38.5%
Validation Loss	55.72	55.14	44.41	26.80	-51.9%
Training Accuracy	55.09%	63.58%	63.91%	64.95%	+9.86%
Validation Accuracy	55.56%	61.55%	59.44%	54.85% (SWA ensembled)	+5.99% peak
Decision Entropy	1.034 nats	0.883 nats	0.809 nats	0.754 nats	-27.1% (Sharper Calibration)
Host Memory (RSS)	2,516 MB	2,650 MB	2,632 MB	3,164 MB	Stable (<3.5 GB across 12 GB pool)

Cross-Generation Benchmark Comparison

Metric / Dimension	v1	v2	v3	v4	v5	v6	v6-Max (Flagship)
Backbone	OpenCLIP ViT-B-32	OpenCLIP ViT-B-16	SigLIP ViT-B-16	SigLIP ViT-B-16	SigLIP ViT-B-16	SigLIP ViT-B-16	SigLIP ViT-B-16 (Native)
Manifold Dim	512	512	768	768	768	512	768 (Native, No Down-Proj)
Visual Patches	49	196	196	980 (Multi-Scale)	980	980	980 (Dynamic Tiling)
Fusion Architecture	2-Layer Dense	4-Layer Dense	4-Layer Dense	4-Layer Dense	4-Layer MoE	4L Shared+MoE	4L Shared+MoE (8 Experts/L)
Trainable Params	2.21 M	12.53 M	13.32 M	13.32 M	25.94 M	30.34 M	103.88 M
Frozen Params	151.28 M	149.62 M	212.07 M	212.07 M	212.07 M	212.07 M	212.07 M (0% drift)
Memory Bank	None	None	None	50,000	100,000	100,000	100,000 Foils (FP16)
Checkpoint (FP16)	8.46 MB	71.45 MB	76.20 MB	76.19 MB	49.53 MB	86.22 MB	198.23 MB
Inference Latency	~50 ms	~30 ms	~22 ms	~20 ms	<15 ms	<0.5 ms (Tier-0)	<0.5 ms (Tier-0), <15 ms (MoE)

Quickstart & Installation

# Direct installation from GitHub
pip install git+https://github.com/cd1234/arbiter-omni.git

# Or clone and install in editable mode:
git clone https://github.com/cd1234/arbiter-omni.git
cd arbiter-omni
pip install -e .

Loading v6-Max (High-Capacity Flagship)

from arbiter_omni import ArbiterOmniEngine

# Load v6-Max directly from Hugging Face Hub
engine = ArbiterOmniEngine.from_pretrained("cd1234/ArbiterOmni", checkpoint_file="arbiter_omni_v6_max.pt")

# Perform calibrated multimodal decision arbitration
result = engine.decide(
    question="What is the safest immediate action?",
    candidates=[
        "decelerate smoothly and increase following distance",
        "proceed at maximum velocity without braking",
        "execute uncontrolled swerve into barrier",
    ],
    text="Dense multi-lane highway, heavy rainfall, vehicle decelerating abruptly 15m ahead.",
    test_time_deliberate=True,
)

print(f"Decision              : {result.decision}")
print(f"Confidence            : {result.confidence * 100:.2f}%")
print(f"Entropy               : {result.entropy:.4f} nats")
print(f"Speculative Exit Path : {result.speculative_early_exit}")
print(f"Conformal Set (95%)   : {result.prediction_set}")
print(f"Stability Index       : {result.stability_index:.3f}")

Loading v6 (Compact Production)

engine = ArbiterOmniEngine.from_pretrained("cd1234/ArbiterOmni", checkpoint_file="arbiter_omni_v6.pt")

Citation

@software{arbiteromni2026,
  title={ArbiterOmni: Multimodal System 1 Decision Engine with Speculative Early Exit, Shared-Routed MoE, and Conformal Risk Control},
  author={Cameron Duff},
  year={2026},
  url={https://huggingface.co/cd1234/ArbiterOmni}
}

License

Apache 2.0

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