but they do not reliably handle complex knowledge tasks.
Open Cognitive Graph · AI Cognitive Infrastructure
OCG moves AI from probability-based text generation to deterministic reasoning based on cognitive structure.
We transform domain knowledge, expert judgment, evaluation standards, and evidence boundaries into cognitive graphs and operational substrates that AI systems can run, evaluate, and trace.
Cognitive Structure
c → r@D → C'
OCG turns AI from “able to generate” into “able to operate reliably through cognitive structure.”
It is not another knowledge base, and not merely RAG. It turns knowledge, evidence, boundaries, and governance actions into a substrate AI can reliably call.
Why do so many AI projects produce demos but struggle to land in real operations?
with weak evidence boundaries and few verifiable paths.
evaluation, traceability, reuse, and scale remain fragile.
The problem is not whether AI can generate. The problem is whether it can run reliably in real work.
OCG builds the layer beneath AI applications.
structures concepts, paths, rules, and common sense.
lets AI reason within task conditions and boundaries.
grounds outputs in verifiable, traceable evidence.
turns diagnosis, recommendation, and action into a closed loop.
The core value OCG brings
More accurate
More efficient
Evaluable
Traceable
Scalable
AI generation, recommendation, diagnosis, evaluation, and execution no longer depend on model improvisation alone.
Advanced cognition, educational assessment, psychological measurement, learning science, AI governance.
Learning paths, adaptive assessment, capability diagnosis, personalized learning, AI assistants.
Compliance evaluation, environmental governance, professional knowledge systems, regulated scenarios.
Collaboration model: build the infrastructure together
Current collaboration directions
If what you need is not another app, but a substrate
Let's turn complex knowledge work into AI systems that are runnable, researchable, and scalable.
Principle 1
Structural Transparency:
Every Judgment Is Inspectable
OCG's key contribution is not being "smarter" — it is placing epistemic authority at an abstraction level that humans can inspect. When AI says "you're wrong", there must be a structure behind it that domain experts can review, dispute, and revise — not a black box.
Black-box LLM
Opaque
OCG Governance
Inspectable
OCG's key contribution is not being "smarter" — it is placing epistemic authority at an abstraction level that humans can inspect: concepts, relations, provenance — not parameters. When AI says "you're wrong", there must be a structure behind it that a domain expert can audit, contest, and revise.
Principle 2
Targeted Fix:
Errors Have a Structural Address
In OCG, finding the structural address enables a targeted fix — inserting scaffold nodes immediately affects every output that depends on that path. Black-box models cannot do this: the root cause is diffused across parameter space.
▸ Click “Diagnose” to reveal the structural defect
⚠
Issue found
This edge directly links “Energy as Property” to “Conservation of Energy”, assuming a cognitive leap that students cannot make — intermediate bridge concepts are missing.
✓
Fix applied
2 scaffold nodes inserted (Energy Transfer, System Boundary). The cognitive span of each step is now valid. All outputs depending on this path benefit immediately.
Problem: Cognitive Leap
The raw prerequisite_of edge assumes students can jump directly from “energy is a property” to “energy is conserved” — an empirically proven over-stretch.
Black-box
Issue found → wait for vendor to retrain the entire model, root cause unlocatable
OCG
Issue found → locate node → propose → validate → update
Fix: Scaffolding Strategy
Insert intermediate concept nodes so that the cognitive span of every step stays within learnable range. New nodes use the scaffolds relation type, making their role semantically explicit.
Energy Transfer
System Boundary
scaffolds@Science
A structural address means every fix has a precise scope — no unintended ripple effects, no hidden side-effects elsewhere in the graph.
Every error has a structural address — not a “global model bias”, but a single edge that assumed an unjustified cognitive leap. In OCG, finding the address enables a targeted fix that immediately propagates to every output that uses this path.
Principle 3
Pluralism:
Branches Contain Disagreement
OCG's trunk-branch architecture does not resolve "which view is correct" — it specifies "under which epistemic domain a view holds". Branches let plural perspectives coexist legitimately while the trunk remains stable.
Trunk-Branch Architecture
Consensus trunk · 3 domain branches
Trunk Rules
The trunk stores only cross-domain consensus concepts. Any contested content must go into a branch — never the trunk — ensuring the stability of the shared consensus layer.
Branch Isolation
Replacing P5 in non-Euclidean geometry does not "pollute" the Euclidean branch. Each branch maintains its own axiomatic environment; divergences are stored explicitly as structure.
Cross-Domain Links
The analogous_to relation enables cross-branch analogy without unifying ontologies, building semantic bridges while preserving each domain's independence.
OCG's trunk-branch model achieves “disagreements don’t need to be arbitrated — they just need to be located”. The divide between Rationalism and Empiricism doesn't need an Academic Committee ruling; both exist legitimately in their own branches.
Principle 4
Governance Process:
Authority Through Due Process
Governance is not an external constraint — it is a process built into the structure itself. Every change has a proposer, evidence, validator, and timestamp, giving a structural answer to "who may change", "what changed", and "on what grounds".
Governance Lifecycle
✓
Teacher observes a problem
Students consistently struggle with the "energy property → conservation" transition across multiple assessments
✓
Locate the structural address
OCG reveals: this edge assumes a cognitive leap that does not hold — scaffold nodes needed
3
Proposal: insert scaffold nodes
Branch maintainers submit change → automated consistency check → enters review queue
4
Expert review + pilot
Learning science researchers validate → small-scale pilot (3 schools, 450 students) → data collected
5
Trunk updated, propagated to all branches
Change written to Trunk OCG → all students benefit immediately, history preserved
provenance_record.yaml
node: EnergyTransfer@Science
type: scaffolds
proposed_by: "Middle School Teachers Alliance"
evidence: "3 schools, 450 students, 4-week pilot"
validated_by: "Learning Science Committee"
validated_at: 2025-03-10
trunk_version: v1.2
status: "propagated_to_all_branches"
Core Contrast with Black-box
Black-box
Issue found → unlocatable → wait for full model retrain → still unexplainable
OCG
Issue found → locate node → propose → validate → update → fully auditable
Epistemic authority comes from due process, not "the AI said so" — every node change is traceable to a specific proposer, evidence base, and validator.
450
pilot students
3
schools
v1.2
current trunk
Every change has a proposer, evidence base, validator, and timestamp. This provenance mechanism ensures that governance authority does not rest on any single organisation's unilateral decision, but is derived from the process itself.