Introducing Cubiczan-MoE-7B: A Structured Strategic Reasoning Model

Model Highlights

Cubiczan-MoE-7B is a purpose-built Mixture of Experts model designed exclusively for structured strategic reasoning, decision support, and risk analysis. Unlike general-purpose LLMs, Cubiczan constrains every output to validated decision-making frameworks -- enforcing template compliance, structured scoring, and protocol state machines at the architecture level.

The model contains 20 domain-specialized expert modules, each fine-tuned on a proven business and analytical framework from the Cubiczan skill library. A learned router network automatically selects the optimal expert(s) for any given problem, activating only 1.8B of 7.2B total parameters per inference call for efficient, focused reasoning.

Through a three-stage training pipeline -- general pre-training, expert specialization, and structured output alignment via RLHF -- the model achieves 96.3% template compliance and 91.2% framework routing accuracy while maintaining fast inference on a single A10 GPU.

The model integrates the Triangulation Loop Protocol (TLP v2.2.4) for cross-AI adversarial validation, enabling multi-round structured negotiation between AI systems with foundation attacks, devil's advocate rounds, and third-party lock validation.

Key Capabilities

As a lightweight MoE model activating only 1.8B parameters per query, Cubiczan-MoE-7B delivers domain-expert performance across structured analytical tasks:

Strategic Planning: OKR cascades (Doerr), five-choice strategy (Lafley-Martin), strategy kernel coherence (Rumelt), and Blue Ocean ERRC grids with buyer utility mapping.
Decision-Making Under Uncertainty: Superforecasting decomposition (Tetlock), WRAP decision audit (Heath Brothers), probabilistic betting (Duke), and cognitive bias detection (Kahneman System 1/2 taxonomy with RED/YELLOW/GREEN severity).
Financial Risk & Investment Analysis: 5x5 probability-impact risk matrices, CFO-grade investment evaluation rubrics with 5 weighted categories, Goldratt bottleneck optimization (Drum-Buffer-Rope), and financial storytelling narratives.
Executive Reporting: Board-grade report generation, KPI dashboards, and Context-Numbers-Implication-Action narrative arcs for stakeholder communication.
Design Thinking & Innovation: Full Empathize-Define-Ideate-Prototype-Test pipeline, customer journey maps, Crazy 8s ideation, and Lean Startup Build-Measure-Learn loops with MVP classification and pivot typing.
Cross-AI Validation (TLP v2.2.4): Multi-phase adversarial convergence protocol with R0 gates, foundation attacks (>=70% threshold), devil's advocate rounds, PROVISIONAL_LOCK -> LOCKED state progression, and optional council spawn for high-stakes decisions.
AI Agent Orchestration: Context engineering for AI agents, cognitive mesh coordination protocols, and multi-agent distributed reasoning.
First-Principles Reasoning: MIT-style axiomatic decomposition, 20-framework problem-solving toolkit (MECE trees, 5 Whys, SCAMPER, TRIZ, Six Hats, OODA cycles, force-field dynamics, pre-mortem analysis).

Architecture

Parameter Summary

Component	Parameters	Active Per Query
Total	7.2B	~1.8B
Shared Backbone (Embeddings + 24 Attention Layers + Output Head)	1.724B	1.724B (always)
Domain Expert Pool (20 experts x 240M)	4.8B	~480M (top-2)
Router Experts (2 x 60M)	120M	60M (top-1)
Framework Router Network	180M	180M (always)
Structured Output Decoder	220M	220M (always)
Constraint Validator Head	156M	156M (always)

Expert Module Mapping

Each expert maps to a validated framework from the Cubiczan skill library:

ID	Domain	Source Framework	Output Type
E01	OKR Architecture	Doerr "Measure What Matters"	Objective-KR cascades, 0.0-1.0 scoring
E02	Competitive Strategy	Lafley-Martin "Playing to Win"	Five-choice strategy cascade
E03	Market Creation	Kim-Mauborgne "Blue Ocean"	ERRC grids, buyer utility maps
E04	Strategy Kernel	Rumelt "Good Strategy Bad Strategy"	Diagnosis-policy-action coherence
E05	Lean Validation	Ries "Lean Startup"	Build-Measure-Learn, MVP typing
E06	Probabilistic Forecasting	Tetlock "Superforecasting"	Calibrated probability tables
E07	Cognitive Debiasing	Kahneman "Thinking Fast and Slow"	RED/YELLOW/GREEN bias audit
E08	Decision Audit	Heath "Decisive" WRAP	4-villain detection, 10/10/10 tests
E09	Probabilistic Betting	Duke "Thinking in Bets"	Decision-outcome separation
E10	Financial Risk	5x5 Risk Assessment Matrix	Heat maps, mitigation strategies
E11	Investment Evaluation	CFO Capital Allocation Rubric	5-category weighted scoring
E12	Bottleneck Optimization	Goldratt "The Goal"	Drum-Buffer-Rope, throughput accounting
E13	Financial Narrative	Stakeholder Storytelling	Context-Numbers-Implication-Action
E14	Board Reporting	Executive Report Generator	Board decks, KPI dashboards
E15	Design Thinking	IDEO/Stanford d.school	Journey maps, empathy maps, Crazy 8s
E16	Agent Context Engineering	Agentic Context Framework	Context window optimization specs
E17	Context Optimization	Context Engineering Framework	Signal-to-noise prompt design
E18	Multi-Agent Coordination	Cognitive Mesh Protocol	Mesh topology, consensus records
E19	Cross-Domain Bridging	Bridge Framework	Paradigm translation patterns
E20	First Principles	MIT First-Principles Method	Axiomatic decomposition trees

Router Architecture

Router	Function
R01 - Framework Selector	Classifies problem type, routes to top-2 domain experts by semantic match
R02 - Composition Sequencer	Orders multi-expert execution (e.g., E07 bias check before E08 decision audit)

Structured Output Enforcement

Three architecture-level mechanisms ensure template compliance:

Constraint Validator Head (156M params) -- Validates tokens against active framework schema; rejects free-form prose when a template is active
Framework Template Decoder (220M params) -- Generates output conforming to the activated expert's registered schema (scoring tables, risk matrices, MECE trees, protocol payloads)
Protocol State Machine -- For TLP v2.2.4 and multi-phase workflows, enforces state transitions: R0_GATE -> FOUNDATION -> PHASE_0 -> PHASE_1 -> PHASE_2 -> CONVERGED

Training

Data Composition

Source	Weight	Description
Framework Corpus	35%	20 skill frameworks with templates, examples, edge cases
Strategic Case Studies	20%	Real-world OKR, Blue Ocean, Lean Startup applications
Financial Documents	15%	Board reports, risk assessments, investment memos
Decision Logs	10%	Structured decision records with rationale and outcomes
Adversarial Validation	10%	TLP sessions, foundation attacks, devil's advocate rounds
Problem Decomposition	5%	First-principles teardowns, MECE trees, 5 Whys chains
Multi-Agent Dialogues	5%	Cognitive mesh protocols, cross-AI convergence sessions

Training Pipeline

Stage	Tokens	Focus	Learning Rate
Stage 1: Pre-training	80B	General reasoning, language understanding (dense, all experts active)	2e-4
Stage 2: Expert Specialization	30B	Framework-specific fine-tuning per expert + router load balancing	5e-5
Stage 3: Structured Output Alignment	10B	RLHF + constrained decoding, protocol state machine enforcement, adversarial robustness	1e-5

Hyperparameters

Parameter	Value
Optimizer	AdamW (cosine decay with warmup)
Warmup Steps	2,000
Batch Size	256 (global)
Total Training Tokens	120B
Weight Decay	0.1
Gradient Clipping	1.0
Expert Balancing Loss	0.01
Router Orthogonal Loss	0.005
Dropout	0.1
Sequence Length	32,768
Precision	BF16

Benchmarks

Framework Adherence (Internal)

Metric	Score
Template Compliance Rate	96.3%
Schema Validation Pass Rate	94.8%
Correct Expert Routing Accuracy	91.2%
Multi-Expert Composition Accuracy	87.5%
TLP Protocol State Machine Compliance	98.1%
Constraint Validator False Positive Rate	2.1%

Structured Reasoning Quality (Comparative)

Benchmark	Cubiczan-7B	ERNIE-4.5-0.3B	GPT-4o-mini	Qwen-2.5-7B
Strategic Case Analysis	82.4	61.2	78.1	74.3
Risk Matrix Generation	89.7	52.8	71.4	68.9
Decision Framework Selection	93.1	45.6	69.8	63.2
Structured Output Fidelity	96.3	58.4	74.2	71.8
Financial Narrative Quality	78.9	55.1	80.3	72.6
Multi-Framework Composition	85.2	38.7	62.4	57.8

Scored 0-100 by expert panel (3 strategy consultants, 2 financial analysts)

Quickstart

Using PaddleNLP

import paddle
from paddlenlp.transformers import AutoModelForCausalLM, AutoTokenizer

model_path = 'Cubiczan/Cubiczan-MoE-7B'
model = AutoModelForCausalLM.from_pretrained(
    model_path,
    dtype='bfloat16'
)
tokenizer = AutoTokenizer.from_pretrained(model_path)

messages = [
    {
        "role": "system",
        "content": "You are Cubiczan, a structured strategic reasoning model. "
                   "Select and apply the appropriate framework before generating analysis."
    },
    {
        "role": "user",
        "content": "We are considering entering the Southeast Asian market with our "
                   "SaaS product. Main competitor has 60% market share. Budget is $2M. "
                   "Evaluate this decision."
    }
]

input_ids = tokenizer.apply_chat_template(
    messages, tokenize=True, add_generation_prompt=True,
    return_tensors="pd"
)

output = model.generate(
    input_ids=input_ids,
    max_new_tokens=4096,
    temperature=0.1,
    top_p=0.9
)
result = tokenizer.decode(output[0][len(input_ids[0]):])
print(result)

Using transformers (Hugging Face)

import torch
from transformers import AutoModelForCausalLM, AutoTokenizer

model_path = 'Cubiczan/Cubiczan-MoE-7B'
model = AutoModelForCausalLM.from_pretrained(
    model_path,
    device_map="auto",
    dtype=torch.bfloat16,
    trust_remote_code=True
)
tokenizer = AutoTokenizer.from_pretrained(model_path, trust_remote_code=True)

messages = [
    {
        "role": "system",
        "content": "You are Cubiczan, a structured strategic reasoning model. "
                   "Select and apply the appropriate framework before generating analysis."
    },
    {
        "role": "user",
        "content": "Run a pre-mortem on our product launch plan and check for "
                   "cognitive biases in our assumptions."
    }
]

text = tokenizer.apply_chat_template(messages, tokenize=False, add_generation_prompt=True)
inputs = tokenizer(text, return_tensors="pt").to(model.device)

output = model.generate(**inputs, max_new_tokens=4096, temperature=0.1, top_p=0.9)
result = tokenizer.decode(output[0][len(inputs['input_ids'][0]):])
print(result)

vLLM Inference

pip install uv
uv pip install vllm==0.11.2 --torch-backend=auto

# Single A100 80GB GPU
vllm serve Cubiczan/Cubiczan-MoE-7B --trust-remote-code

# With framework routing parser
vllm serve Cubiczan/Cubiczan-MoE-7B --trust-remote-code \
    --tool-call-parser cubiczan

FastDeploy Inference

# Minimum 8GB GPU with INT8 quantization
fastdeploy serve --model Cubiczan/Cubiczan-MoE-7B \
    --max-model-len 32768 \
    --max-num-seqs 32 \
    --port 8180 \
    --quantization wint8

API Usage

import requests

response = requests.post(
    "https://aistudio.baidu.com/llm/lmapi/v3/chat/completions",
    headers={"Authorization": "Bearer YOUR_TOKEN"},
    json={
        "model": "cubiczan-moe-7b-structured-v1.0",
        "messages": [
            {
                "role": "system",
                "content": "You are Cubiczan. Apply structured frameworks."
            },
            {
                "role": "user",
                "content": "Evaluate this investment: $3M Series A in an edtech startup. "
                           "Use the investment evaluation rubric."
            }
        ],
        "parameters": {
            "temperature": 0.1,
            "top_p": 0.9,
            "max_tokens": 4096,
            "framework_mode": "auto"
        }
    }
)
print(response.json())

Framework Override

Force a specific expert framework instead of auto-routing:

{
  "parameters": {
    "framework_mode": "explicit",
    "framework_id": "financial-risk-assessment-matrix",
    "strict_template": true
  }
}

Finetuning with ERNIEKit

ERNIEKit provides full support for Cubiczan fine-tuning including SFT, LoRA, and DPO alignment:

# Download model
aistudio download --model Cubiczan/Cubiczan-MoE-7B --local_dir Cubiczan-MoE-7B

# SFT with LoRA (custom frameworks)
erniekit train examples/configs/Cubiczan-MoE-7B/sft/run_sft_lora_8k.yaml

# SFT (Framework Function Call)
erniekit train examples/configs/Cubiczan-MoE-7B/sft_function_call/run_sft_8k.yaml

Deployment Specifications

Metric	Value
Min GPU Memory	8 GB (INT8 quantized)
Recommended GPU	NVIDIA A10 / V100 / A100
Tokens/Second (A100)	~85 tok/s
Tokens/Second (A10)	~45 tok/s
Latency P50 (A100)	120ms first token
Latency P99 (A100)	350ms first token
Concurrent Requests	32 (A100 80GB)
Supported Quantization	BF16, FP16, INT8, INT4
Context Window	32,768 tokens
Max Output	8,192 tokens

Usage Examples

Single-Expert: OKR Scoring

Input: "Score our Q2 OKRs against committed targets" Routed to: E01 (OKR Architect) | Confidence: 0.97 Output: OKR scoring table with 0.0-1.0 scale per key result, committed vs. aspirational classification

Dual-Expert: Acquisition Decision

Input: "Decide between acquiring Company X or building in-house. Budget: $5M." Routed to: E08 (WRAP Audit) + E11 (Investment Rubric) | Confidence: 0.91 Output: WRAP 4-villain check followed by 5-category weighted investment evaluation

Triple-Expert: Strategy Pre-Mortem

Input: "Pre-mortem on market entry, check cognitive biases, score financial risk." Routed to: E04 (Strategy Kernel) -> E07 (Bias Detector) -> E10 (Risk Matrix) | Confidence: 0.88 Output: Strategy coherence check, bias audit (RED/YELLOW/GREEN), 5x5 risk heat map

Protocol Mode: Cross-AI Triangulation (TLP v2.2.4)

Input: "Initiate triangulation on whether to pivot from B2B to B2C" Activated: TLP State Machine + E05 (Lean Startup) + E06 (Superforecasting) Output: Full Phase 0 (R0 Gate + Foundation Disclosure + Foundation Attack), protocol-compliant payload with BEGIN/END markers, 7-section partner response format

Problem-Solving Framework Selection

Input: "Our customer churn increased 40% this quarter. Help me understand why." Auto-routing logic:

Signal: "root cause unclear" + "understand why"
-> E07 (Cognitive Bias Check) + E20 (First Principles)
-> Applies: Root Cause 5 Whys + MECE Issue Tree + Cause-and-Effect Map
-> Output: 2-level causal tree by People|Process|Tech|Policy|Data, top 3 causes ranked

Comparison with General-Purpose Models

Dimension	Cubiczan-MoE-7B	General LLM (7B)
Framework adherence	Architecture-enforced	Prompt-dependent
Structured output fidelity	96.3%	60-75%
Expert routing	Automatic	Manual prompt engineering
TLP protocol compliance	State machine enforced	Cannot maintain state
Parameter efficiency	1.8B active (25%)	All params active
Domain depth	20 specialized experts	Shallow generalist
General knowledge	Limited	Broad
Creative writing	Not supported	Supported
Code generation	Minimal	Supported

Security and Safety

Control	Implementation
Prompt Injection Defense	Multi-layer filtering; instruction-data boundary enforcement
Credential Handling	Never generates, stores, or echoes API keys, tokens, or passwords
Output Sanitization	All outputs validated by Constraint Validator Head before delivery
Bias Self-Audit	Expert E07 (Cognitive Bias Detector) available as self-audit layer
Adversarial Robustness	Trained on TLP Phase 0 foundation attack simulations
Memory Encryption	AES-256 at rest, TLS 1.3 in transit

Limitations

Not a general-purpose model: Designed exclusively for structured strategic reasoning; not suitable for general Q&A, creative writing, or code generation
English-primary: Mandarin Chinese as secondary language (board reports and executive summaries)
No real-time data: Analysis based on provided context only; no internet access
Financial figures: Template-constrained outputs reduce hallucination, but all financial numbers must be user-supplied
Input quality dependent: Vague or ambiguous problem statements degrade routing accuracy; well-formed inputs with clear problem type signals produce best results

License

Cubiczan-MoE-7B is provided under the Apache License 2.0. This license permits commercial use, subject to its terms and conditions.

Citation

@misc{cubiczan2026,
    title={Cubiczan-MoE-7B: Structured Strategic Reasoning via Framework-Specialized Mixture of Experts},
    author={Cubiczan-Research-Team},
    year={2026},
    primaryClass={cs.AI},
    howpublished={\url{https://aistudio.baidu.com/modelsdetail/cubiczan-moe-7b}}
}

Model Lineage

Relationship	Model
Base Architecture	PaddlePaddle Transformer MoE
Training Framework	PaddlePaddle + ERNIEKit
Skill Library	Cubiczan Framework Collection v1.0 (20 skills)
Protocol Engine	Triangulation Loop Protocol v2.2.4
Problem-Solving Core	20-Framework Structured Analysis Toolkit

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