Alpha (System-One Decision Model)

Alpha is an open, calibrated System-One decision model continuing convaiinnovations/laya (Apache-2.0).

Given text/JSON states and typed questions (choice, score, noul), Alpha returns typed answers with mathematically calibrated probabilities in a single forward pass without autoregressive token generation.

Benchmark Results (Measured vs. TypeSafe Jev)

Evaluated on held-out test splits:

Benchmark	Question Type	Chance	TypeSafe Jev (Ref)	Alpha (Ours @ Step 2500)	Delta vs. Jev
AG News	choice ($k=4$)	25.0%	91.0%	94.8%	+3.8%
Emotion	choice ($k=6$)	16.7%	48.0%	88.5%	+40.5%
SST-2	noul ($k=2$)	50.0%	89.8%	93.2%	+3.4%
Banking77	choice ($k=77$)	1.3%	87.0%	86.5%	-0.5%
SMS Spam	noul ($k=2$)	50.0%	N/A	99.0%	—
MNLI	choice ($k=3$)	33.3%	N/A	87.5%	—
SST-5	score ($k=5$)	20.0%	N/A	54.7%	—

Quickstart

from laya_mm import LayaMM
from huggingface_hub import snapshot_download

agent = LayaMM.from_dir(snapshot_download("SofiTesfay2010/Alpha"))

# 1. Binary Decision (noul)
res = agent.system_one(
    "Your package was delivered to the front porch.",
    {"delivered": {"type": "noul", "instructions": "Was the delivery completed?"}}
)
print(res["answers"]["delivered"])

# 2. Multi-Class Choice
res = agent.system_one(
    "I received a bill with unauthorized roaming charges.",
    {"intent": {
        "type": "choice",
        "instructions": "Identify the customer support ticket intent.",
        "criteria": ["billing_dispute", "technical_issue", "account_closure"]
    }}
)
print(res["answers"]["intent"])

Architecture

• Text Backbone: Bidirectional ModernBERT encoder (423M parameters).
• Vision Pathway: Frozen SigLIP vision tower (google/siglip-base-patch16-224) with patch projection.
• Adaptive Head Budget: Dynamic expansion (10k + 48 tokens, capped at 1024) preventing label truncation.
• License: Apache-2.0.