Open Cognitive Graph
Knowledge & reasoning infrastructure
Research
AEGT organizes research through three foundational frameworks, eight sustained research programs, and a portfolio of concrete research projects. The structure is designed to make a fast-growing body of work understandable without flattening it into traditional disciplinary boxes.
Research Architecture
Representative projects are shown here to keep the public map legible. The internal project registry is broader.
Knowledge & reasoning infrastructure
Human cognitive agency
Adaptive & collective intelligence
Level 2
Each program is a sustained research agenda. The short descriptions below identify the actual research object rather than merely explaining a coined term.
Studies how semantics become valid within domains, how relations compose legally, and how structures become well-founded, terminating, decidable, and stable under computation.
Core question: In what semantic space is a reasoning relation valid, and under what structural conditions can local relations form stable computation?
Studies how structured cognition and reasoning move from descriptive representations into executable, testable, reusable computational objects.
Core question: What must be made explicit for a reasoning process itself to become inspectable and executable infrastructure?
Studies how knowledge, evidence, judgment, decisions, authority, and responsibility are explicitly organized, reviewed, and governed inside institutions.
Core question: How can institutional AI-mediated judgment remain inspectable, contestable, auditable, and accountable?
Studies how people form, retain, and develop judgment and cognitive autonomy when AI participates in cognitive work.
Core question: When does AI strengthen human judgment, and when does it replace or obscure the process by which judgment is formed?
Studies which cognitive capabilities people need in AI-mediated environments and how those capabilities can be learned, trained, and evaluated.
Core question: What should AI literacy become when specific tools, prompts, and interfaces change faster than educational systems?
Studies how individuals can accumulate, organize, own, and transfer memory, knowledge, authorship, judgment, and cognitive development over time.
Core question: How can AI-supported cognition become a durable personal asset rather than a sequence of disposable interactions?
Studies how groups of humans and agents form shared cognition through contribution, disagreement, correction, coordination, exchange, and feedback.
Core question: What structures allow collective intelligence to emerge without erasing disagreement or centralizing epistemic control?
Studies how institutions turn experience, decisions, outcomes, errors, and corrections into accumulated and continuously updated organizational knowledge.
Core question: How can institutions preserve why a decision was made, not only what was finally decided?
Level 3
The site deliberately shows a smaller public set than the internal registry. Each project has a bounded question, an implementation context, and research evidence or an active path toward it.
Tests whether the same DA structure and computation remain invariant across symbolic, matrix, attention, differentiable, and physical computational substrates.
Turns reasoning, differences, boundaries, actions, and results into explicit records that can be reviewed, compared, computed, and accumulated.
Studies model capability boundaries in relation to concrete tasks, domains, evidence, and constraints rather than a single general leaderboard.
Applies structured cognition to LLM orchestration, self-recursive invocation, model enhancement, persistent intent, and long-lived agents.
Structures privacy, safety, ethics, fairness, accountability, law, and compliance into inspectable institutional governance systems across jurisdictions.
Moves evaluation from final answers toward the visible process of reasoning, conflict handling, boundary revision, and judgment formation.
Develops AI literacy and computer-science learning around cognitive structure, algorithmic understanding, and durable capability rather than tool operation alone.
Builds a longitudinal scientific account around questions, hypotheses, evidence, revisions, writing, and cognitive growth rather than only final papers or grades.
Studies how organizations preserve the reasoning behind implementation choices and convert field judgment into reusable institutional knowledge.
Studies coordination, exchange, value, constraints, and system behavior when persistent agents act and transact autonomously over time.
Research Roadmap
The roadmap describes research maturity and collaboration needs. It is intentionally not a product feature calendar.
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