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Project: SHADOW AI
Architecture: SHADOW-HSR — Hybrid Symbolic Representation Architecture
Edition: Clean Master Edition
Status: Research Prototype Foundation
SHADOW AI is an experimental language-model architecture built around four cooperating capabilities:
Core concept:
SHADOW = Neural Representation + Symbolic Reasoning + Dynamic State + Efficient Computation
The system is designed as a research architecture rather than a claim of superiority over existing AI systems.
Important scientific note: this document proposes an original architecture direction, but it does not prove global novelty, patentability, or publication-level novelty. Those require separate prior-art research.
The objective is to build a small, measurable AI system that can progressively support:
The core research hypothesis is:
A compact neural architecture augmented with symbolic processing, dynamic internal state, and efficient sequence computation may provide useful multilingual and structured reasoning capabilities with lower computational requirements than an equivalently trained conventional baseline.
This is a hypothesis and must be experimentally tested.
The complete conceptual pipeline is:
RAW INPUT
|
v
UTF-8 BYTE REPRESENTATION
|
+-------------------+
| |
v v
NEURAL FEATURES SYMBOLIC FEATURES
| |
+---------+---------+
|
v
CONTEXT MIXER
|
v
DYNAMIC MEMORY
|
v
REASONING CORE
|
v
OUTPUT PLANNER
|
v
BYTE DECODER
|
v
OUTPUT
The architecture has seven logical layers:
Let:
Conceptual fusion:
H_t = Fuse(B_t, S_t, C_t, M_t)
Reasoning:
R_t = Reason(H_t, A_t)
Memory update:
M_(t+1) = Update(M_t, R_t)
Output:
Y_t = Decode(R_t, M_(t+1))
Symbolic representation:
S = Parse(X) + Structure(X) + Relation(X)
Future multi-objective training:
L_SHADOW = λ1 L_text + λ2 L_symbol + λ3 L_reason + λ4 L_consistency
The first prototype does not implement every objective. It begins with next-byte prediction so the complete pipeline can be tested.
The first prototype uses UTF-8 bytes.
Vocabulary:
0–255
This gives the model a universal low-level representation for:
Example:
Hello বাংলা 世界
is converted to its UTF-8 byte sequence.
Advantages:
Main disadvantage:
Byte sequences can be longer than subword sequences, so sequence efficiency becomes an important research problem.
The symbolic engine detects structures that benefit from explicit representation.
Example:
5 + 7
Conceptual representation:
ADD(5, 7)
Example:
x + 5 = 12
Conceptual representation:
EQUATION(
LEFT = ADD(x, 5),
RIGHT = 12
)
Initial symbolic categories:
+
-
*
/
=
>
<
%
numbers
identifiers
equations
structured expressions
The prototype includes deterministic symbol detection and a restricted arithmetic evaluator.
The evaluator must never use unrestricted Python eval().
Dynamic memory is internal working state. It is not RAG and it is not a document database.
Prototype:
Input 1 -> memory
Input 2 -> memory
Input 3 -> memory
...
The memory is bounded.
Future versions may investigate:
The long-term reasoning system is modular:
Reasoning Core
├── Semantic Reasoner
├── Symbolic Reasoner
├── Mathematical Reasoner
├── Planning Reasoner
└── Consistency Checker
A future controller can decide which reasoning mode is appropriate.
Example:
Normal question
-> neural reasoning
Arithmetic
-> symbolic calculator
Equation
-> equation solver
Code
-> code reasoning
Multi-step task
-> planner
SHADOW_AI/
├── README.md
├── LICENSE
├── requirements.txt
├── config.py
├── train.py
├── generate.py
├── evaluate.py
├── data/
│ ├── train.txt
│ └── validation.txt
├── checkpoints/
├── src/
│ └── shadow/
│ ├── __init__.py
│ ├── tokenizer.py
│ ├── symbols.py
│ ├── memory.py
│ ├── data.py
│ ├── model.py
│ ├── generation.py
│ └── engine.py
├── tests/
│ ├── test_tokenizer.py
│ ├── test_symbols.py
│ └── test_memory.py
└── docs/
└── SHADOW_AI_MASTER.md
Initial implementation:
Later research may add:
Do not add databases, microservices, payment systems, web applications, or RAG infrastructure to the first prototype.
Windows PowerShell:
cd C:\path\to\SHADOW_AI
python -m venv .venv
.\.venv\Scripts\python.exe -m pip install --upgrade pip
.\.venv\Scripts\python.exe -m pip install -r requirements.txt
Verify PyTorch:
.\.venv\Scripts\python.exe -c "import torch; print(torch.__version__)"
Check GPU:
.\.venv\Scripts\python.exe -c "import torch; print(torch.cuda.is_available())"
torch
numpy
tqdm
pytest
from dataclasses import dataclass
from pathlib import Path
@dataclass
class Config:
vocab_size: int = 256
hidden_size: int = 128
n_layers: int = 2
n_heads: int = 4
dropout: float = 0.1
sequence_length: int = 128
batch_size: int = 8
learning_rate: float = 3e-4
epochs: int = 5
data_dir: Path = Path("data")
checkpoint_dir: Path = Path("checkpoints")
class ShadowTokenizer:
vocab_size = 256
def encode(self, text: str) -> list[int]:
return list(text.encode("utf-8"))
def decode(self, tokens: list[int]) -> str:
return bytes(
int(token) % 256 for token in tokens
).decode("utf-8", errors="replace")
import ast
import operator
import re
SYMBOLS = {
"+": "ADD",
"-": "SUBTRACT",
"*": "MULTIPLY",
"/": "DIVIDE",
"=": "EQUAL",
">": "GREATER",
"<": "LESS",
"%": "MODULO",
}
OPS = {
ast.Add: operator.add,
ast.Sub: operator.sub,
ast.Mult: operator.mul,
ast.Div: operator.truediv,
ast.Mod: operator.mod,
}
def detect_symbols(text: str) -> list[dict]:
return [
{"symbol": char, "type": SYMBOLS[char]}
for char in text
if char in SYMBOLS
]
def extract_numbers(text: str) -> list[float]:
values = re.findall(
r"(?<![\w.])-?\d+(?:\.\d+)?",
text
)
return [float(v) for v in values]
def safe_calculate(expression: str):
tree = ast.parse(expression, mode="eval")
def evaluate(node):
if isinstance(node, ast.Constant):
if isinstance(node.value, (int, float)):
return node.value
if isinstance(node, ast.UnaryOp):
if isinstance(node.op, (ast.UAdd, ast.USub)):
value = evaluate(node.operand)
return value if isinstance(node.op, ast.UAdd) else -value
if isinstance(node, ast.BinOp):
operation = OPS.get(type(node.op))
if operation is not None:
return operation(
evaluate(node.left),
evaluate(node.right)
)
raise ValueError("Unsupported expression")
return evaluate(tree.body)
from collections import deque
class ShadowMemory:
def __init__(self, max_items: int = 32):
self.items = deque(maxlen=max_items)
def add(self, value):
self.items.append(value)
def get(self) -> list:
return list(self.items)
def clear(self):
self.items.clear()
import torch
from torch.utils.data import Dataset
class ByteTextDataset(Dataset):
def __init__(self, text: str, sequence_length: int = 128):
self.data = torch.tensor(
list(text.encode("utf-8")),
dtype=torch.long
)
self.sequence_length = sequence_length
def __len__(self):
return max(
0,
len(self.data) - self.sequence_length
)
def __getitem__(self, index):
x = self.data[
index:index + self.sequence_length
]
y = self.data[
index + 1:index + self.sequence_length + 1
]
return x, y
def load_text(path):
return path.read_text(encoding="utf-8")
import torch
import torch.nn as nn
class ShadowModel(nn.Module):
def __init__(
self,
vocab_size=256,
hidden_size=128,
n_layers=2,
n_heads=4,
max_seq_len=128,
dropout=0.1,
):
super().__init__()
self.vocab_size = vocab_size
self.max_seq_len = max_seq_len
self.embedding = nn.Embedding(
vocab_size,
hidden_size
)
self.position = nn.Embedding(
max_seq_len,
hidden_size
)
layer = nn.TransformerEncoderLayer(
d_model=hidden_size,
nhead=n_heads,
dropout=dropout,
batch_first=True,
activation="gelu"
)
self.encoder = nn.TransformerEncoder(
layer,
num_layers=n_layers
)
self.norm = nn.LayerNorm(hidden_size)
self.output = nn.Linear(
hidden_size,
vocab_size
)
def forward(self, tokens):
_, seq_len = tokens.shape
if seq_len > self.max_seq_len:
raise ValueError("Sequence too long")
positions = torch.arange(
seq_len,
device=tokens.device
).unsqueeze(0)
x = (
self.embedding(tokens)
+ self.position(positions)
)
causal_mask = torch.triu(
torch.ones(
seq_len,
seq_len,
device=tokens.device,
dtype=torch.bool
),
diagonal=1
)
x = self.encoder(
x,
mask=causal_mask
)
return self.output(
self.norm(x)
)
import torch
@torch.no_grad()
def generate(
model,
tokenizer,
prompt,
max_new_bytes=200,
temperature=0.8,
top_k=40,
device="cpu"
):
model.eval()
ids = torch.tensor(
[tokenizer.encode(prompt)],
dtype=torch.long,
device=device
)
for _ in range(max_new_bytes):
context = ids[:, -model.max_seq_len:]
logits = model(context)[:, -1, :]
if temperature <= 0:
next_token = logits.argmax(
dim=-1,
keepdim=True
)
else:
logits = logits / temperature
if top_k:
k = min(
top_k,
logits.shape[-1]
)
values, indices = torch.topk(
logits,
k
)
filtered = torch.full_like(
logits,
float("-inf")
)
filtered.scatter_(
1,
indices,
values
)
logits = filtered
probabilities = torch.softmax(
logits,
dim=-1
)
next_token = torch.multinomial(
probabilities,
1
)
ids = torch.cat(
[ids, next_token],
dim=1
)
return tokenizer.decode(
ids[0].tolist()
)
from .memory import ShadowMemory
from .symbols import detect_symbols, safe_calculate
class ShadowEngine:
def __init__(self):
self.memory = ShadowMemory()
def inspect(self, text):
result = {
"text": text,
"symbols": detect_symbols(text),
"memory": self.memory.get()
}
self.memory.add(text)
return result
def calculate(self, expression):
result = safe_calculate(expression)
self.memory.add({
"expression": expression,
"result": result
})
return result
Create train.py:
from config import Config
import torch
from torch.utils.data import DataLoader
from tqdm import tqdm
from src.shadow.data import ByteTextDataset, load_text
from src.shadow.model import ShadowModel
def main():
cfg = Config()
cfg.checkpoint_dir.mkdir(
parents=True,
exist_ok=True
)
text = load_text(
cfg.data_dir / "train.txt"
)
dataset = ByteTextDataset(
text,
cfg.sequence_length
)
if len(dataset) == 0:
raise ValueError(
"Training data is too small."
)
loader = DataLoader(
dataset,
batch_size=cfg.batch_size,
shuffle=True,
drop_last=True
)
device = (
"cuda"
if torch.cuda.is_available()
else "cpu"
)
print("Device:", device)
model = ShadowModel(
cfg.vocab_size,
cfg.hidden_size,
cfg.n_layers,
cfg.n_heads,
cfg.sequence_length,
cfg.dropout
).to(device)
optimizer = torch.optim.AdamW(
model.parameters(),
lr=cfg.learning_rate
)
loss_fn = torch.nn.CrossEntropyLoss()
for epoch in range(cfg.epochs):
model.train()
total = 0.0
for x, y in tqdm(
loader,
desc=f"Epoch {epoch + 1}/{cfg.epochs}"
):
x = x.to(device)
y = y.to(device)
logits = model(x)
loss = loss_fn(
logits.reshape(
-1,
cfg.vocab_size
),
y.reshape(-1)
)
optimizer.zero_grad(
set_to_none=True
)
loss.backward()
torch.nn.utils.clip_grad_norm_(
model.parameters(),
1.0
)
optimizer.step()
total += loss.item()
print(
"Average loss:",
total / max(len(loader), 1)
)
checkpoint = (
cfg.checkpoint_dir
/ "shadow-model.pt"
)
torch.save(
{
"model": model.state_dict(),
"config": cfg.__dict__
},
checkpoint
)
print("Saved:", checkpoint)
if __name__ == "__main__":
main()
Create generate.py:
import argparse
import torch
from config import Config
from src.shadow.model import ShadowModel
from src.shadow.tokenizer import ShadowTokenizer
from src.shadow.generation import generate
parser = argparse.ArgumentParser()
parser.add_argument(
"--prompt",
default="Hello Shadow"
)
parser.add_argument(
"--length",
type=int,
default=200
)
args = parser.parse_args()
cfg = Config()
device = (
"cuda"
if torch.cuda.is_available()
else "cpu"
)
checkpoint = torch.load(
cfg.checkpoint_dir / "shadow-model.pt",
map_location=device
)
model = ShadowModel(
cfg.vocab_size,
cfg.hidden_size,
cfg.n_layers,
cfg.n_heads,
cfg.sequence_length,
cfg.dropout
).to(device)
model.load_state_dict(
checkpoint["model"]
)
output = generate(
model,
ShadowTokenizer(),
args.prompt,
args.length,
0.8,
40,
device
)
print(output)
Create evaluate.py.
Measure:
The first automatic metric should be validation loss/perplexity.
Do not use a single metric to claim general intelligence.
The included sample data is only for testing.
A real training dataset requires:
Potential categories:
Books
Articles
Documentation
Code
Mathematics
Educational material
Multilingual text
Structured data
Synthetic reasoning tasks
All data must be used according to its license and applicable law.
Recommended progression:
General high-quality text.
Balanced multilingual material.
Equations, code, JSON, tables, symbols.
Question answering, explanation, transformation.
Arithmetic, logic, planning, consistency.
SHADOW-Code
SHADOW-Math
SHADOW-Reason
SHADOW-Multilingual
RAG is not required by SHADOW's core.
The architecture can operate:
SHADOW CORE
without:
Vector database
Retriever
Document search
External knowledge base
A future optional architecture can be:
+--> Optional Retrieval
|
User -> SHADOW CORE -> Output
This keeps retrieval separate from the core model.
The first implementation uses a small Transformer because it is simple and measurable.
It is explicitly a baseline.
Later experiments should compare:
The replacement architecture must be judged experimentally.
Test:
English
Bengali
Hindi
Arabic
Chinese
Japanese
Spanish
French
mixed-language prompts
Measure:
UTF-8 compatibility does not automatically mean multilingual understanding. Training and evaluation are required.
Future mathematical routing:
Natural language
|
v
Math detector
|
v
Symbolic representation
|
v
Verified computation
|
v
Natural-language explanation
This allows deterministic computation to complement neural generation.
Future code support:
Python
JavaScript
TypeScript
Java
C
C++
Rust
SQL
HTML
CSS
JSON
Shell
Possible structural representation:
CODE
├── FUNCTION
│ ├── NAME
│ ├── PARAMETERS
│ └── BODY
└── RETURN
The symbolic layer can recognize operators, identifiers, brackets, assignments, comparisons, and numbers.
Suggested versions:
SHADOW-Nano
SHADOW-Mini
SHADOW-Core
SHADOW-Large
Specialized versions:
SHADOW-Code
SHADOW-Math
SHADOW-Reason
SHADOW-Multilingual
A fair experiment compares similar model sizes.
Example:
SHADOW-50M
vs
Baseline-50M
Keep approximately equal:
Measure:
Quality
Reasoning
Multilingual performance
Latency
VRAM
Training time
Parameter count
Only measured results should be used for performance claims.
Every experiment should record:
Experiment ID
Architecture
Dataset version
Parameter count
Sequence length
Batch size
Learning rate
Training steps
Hardware
Validation loss
Perplexity
Reasoning score
Multilingual score
Latency
Memory
Example:
EXP-0001
This prevents undocumented changes from invalidating comparisons.
Recommended structure:
checkpoints/
├── shadow-step-010000.pt
├── shadow-step-020000.pt
├── shadow-best.pt
└── metadata.json
For production research releases, use a safe checkpoint format such as SafeTensors and record metadata.
The system should include:
For an eventual public product:
Only after the core model is stable:
Client
|
v
API
|
v
SHADOW Runtime
|
+-- Model
+-- Symbol Engine
+-- Memory
+-- Optional Tools
|
v
Response
Recommended endpoints:
POST /generate
POST /inspect
POST /calculate
GET /health
Do not build a complex microservice architecture for the first prototype.
After quality is established, evaluate:
Target:
Similar quality
+
Less memory
+
Lower latency
Classify failures as:
DATA FAILURE
TOKENIZATION FAILURE
SYMBOLIC FAILURE
MEMORY FAILURE
REASONING FAILURE
DECODING FAILURE
TRAINING FAILURE
GENERALIZATION FAILURE
MULTILINGUAL FAILURE
COMPUTE LIMITATION
Every failure should lead to an experiment rather than an unsupported claim.
IDEA
|
v
ARCHITECTURE
|
v
DATA DESIGN
|
v
IMPLEMENTATION
|
v
UNIT TESTS
|
v
TRAINING
|
v
EVALUATION
|
v
FAILURE ANALYSIS
|
v
ARCHITECTURE IMPROVEMENT
|
v
RETRAIN
|
v
CONTROLLED COMPARISON
|
v
DOCUMENTATION
|
v
REPRODUCTION
|
v
RESEARCH RELEASE
[ ] Environment works
[ ] Source code runs
[ ] Dataset is documented
[ ] Dataset licensing is checked
[ ] Training completes
[ ] Checkpoint saves
[ ] Generation works
[ ] Evaluation works
[ ] Unit tests pass
[ ] UTF-8 multilingual tests pass
[ ] Symbolic tests pass
[ ] Reasoning tests exist
[ ] Efficiency is measured
[ ] Baseline comparison exists
[ ] Failure analysis exists
[ ] Experiment configuration is recorded
[ ] Results are reproducible
[ ] Claims are supported by measurements
After creating the project:
python -m venv .venv
.\.venv\Scripts\python.exe -m pip install -r requirements.txt
.\.venv\Scripts\python.exe -m pytest
.\.venv\Scripts\python.exe train.py
.\.venv\Scripts\python.exe generate.py --prompt "Hello Shadow"
.\.venv\Scripts\python.exe evaluate.py
The first trained checkpoint should be:
checkpoints/shadow-model.pt
SHADOW-HSR is defined as:
Universal Input
+
Neural Representation
+
Symbolic Structure
+
Context Mixing
+
Dynamic State
+
Reasoning
+
Efficient Output
The first model is deliberately small.
The architecture should grow only when experiments demonstrate that a new component provides measurable value.
This Clean Master Edition replaces the earlier fragmented drafts.
The project should be treated as one complete engineering and research specification.
The governing development rule is:
Design → Implement → Train → Measure → Compare → Improve → Reproduce
The model should not be judged by its name or theoretical architecture alone. Its value must ultimately be established through reproducible experiments.
SHADOW AI therefore begins as a small working research system and can evolve into a larger model family only when evidence supports each architectural step.