CAS — Cognitive Agent Substrate

A structured knowledge representation and retrieval system built around a Heterogeneous Knowledge Graph (HKG). CAS organizes information across layered node types (L1–L5), enables graph-traversal-based reasoning, and compresses context into topology chains for efficient LLM consumption.

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Architecture

cas/
├── core.py              # HKG storage engine (SQLite + in-memory cache)
├── embeddings.py        # Embedding engine (MiniLM local / OpenAI API)
├── l4_knowledge.py      # L4: graph traversal, path convergence, topology chains
├── l5_personalization.py# L5: user profile modeling and personalized routing
└── synthetic_data.py    # Synthetic graph and data generators for experiments

Node Types

Layer	Type	Description
L1	SEMANTIC	Raw semantic chunks, embedded and clustered
L2	FACT	Extracted facts and structured propositions
L3	TRACE	Causal event traces and audit logs
L4	MACRO	Generalized macro-nodes via cluster inheritance

Edge Types

Type	Discount	Description
SEMANTIC	0.7	Similarity-based links between chunks
CAUSAL	1.0	Directed causal relationships
MACRO	0.9	Cluster-level generalization edges

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Experiments

Eight experiments validate the core claims of the CAS framework. Results and figures are included.

#	Name	Key Result
E1	Epistemic Accuracy	Pearson r = 0.71 (path convergence ↔ answer quality); Top-1 accuracy 80%
E2	Generalization Quality	Macro-node inheritance: precision 0.84 / recall 0.68 at threshold 0.7
E3	Compression Efficiency	Topology chains reduce token count by 72.2% vs raw context
E4	Traversal Scalability	Median latency 1.26 ms at 25K nodes / 250K edges
E5	Personalization Cost	Routing savings via L5 user profiles
E6	Causal Reasoning	Causal blast-radius propagation and containment
E7	End-to-End Quality	L4 topology chains: judge score 4.52 vs baseline-RAG 4.36, with 22% fewer tokens
E8	Personalization Quality	Cross-domain user adaptation metrics

Reproduce figures

python experiments/run_all.py

Each experiment can also be run individually:

python -m experiments.e1_epistemic.run
python -m experiments.e3_compression.run
# etc.

Figures are written to experiments/<name>/figures/ and results to experiments/<name>/results.json.

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Setup

Requirements: Python 3.10+

pip install -r requirements.txt

API key (optional — only needed for OpenAI embedding backend):

cp .env.example .env
# edit .env and add your key

The default embedding backend is local MiniLM (all-MiniLM-L6-v2) and requires no API key.

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Quick Start

from cas.core import HKGStore, Node, Edge, NodeType, EdgeType
from cas.embeddings import EmbeddingEngine
from cas.l4_knowledge import L4KnowledgeConsolidation

# Build a graph
store = HKGStore()
engine = EmbeddingEngine()

node = Node(node_id="n1", node_type=NodeType.L1_SEMANTIC, content="The cat is a mammal")
node.embedding = engine.encode("The cat is a mammal")[0]
store.add_node(node)

# Traverse and produce a topology chain
l4 = L4KnowledgeConsolidation(store, engine)
result = l4.query("What animals are mammals?")
print(result.chain_text)

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Experiment Results

E1 — Epistemic Accuracy

Path convergence score reliably predicts answer quality (Pearson r = 0.71).

Metric	Value
Mean answer similarity	85.2%
Top-1 accuracy	80%
Hit-10 accuracy	90%
Mean traversal latency	11 ms

E3 — Compression Efficiency

Topology chains compress graph context to ~28% of raw token count while preserving reasoning structure.

Metric	Value
Mean raw tokens	69.8
Mean chain tokens	19.0
Mean reduction	72.2%

E4 — Traversal Scalability

Traversal latency stays sub-linear as graph size grows from 1K to 25K nodes.

Nodes	Median latency	p99 latency	Memory
1,000	0.76 ms	2.01 ms	1 MB
5,000	1.17 ms	4.07 ms	5 MB
10,000	1.12 ms	2.73 ms	10 MB
25,000	1.26 ms	3.16 ms	24 MB

E7 — End-to-End Quality

L4 topology chains match or exceed baseline RAG quality with fewer tokens.

Condition	Judge score (/5)	Input tokens
Baseline RAG	4.36	493
RAG filtered	4.32	387
L4 Topology	4.52	386

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Author

Ahmet Yigit Sertel — April 2026