This roadmap is about building a Universal Theory Engine (UTE): a platform where theories are executable, checkable, revisable, and comparable. The goal is not “general AGI” in the nebulous sense; it is a pragmatic kind of AGI for science and engineering: agents that can represent, review, argue, prove, and revise scientific theories with evidence and auditability.
Our core bet is that UTE requires a hybrid stack: symbolic semantics for correctness and explicit traces, plus HDC/VSA strategies for fast retrieval and capacity experiments under growth (elastic/dynamic size representations).
See Universal Theory Engine (UTE) and the research specs DS32–DS38 in the Specs matrix.
AGISystem2 is already a coherent research platform: you can express micro-theories in DSL, run symbolic reasoning with proofs/traces, and run capacity and retrieval experiments via multiple HDC strategies (including elastic and lossless variants).
DSL parsing and execution, Session runtime, query/prove, traces/proofs, eval harness.
See Specs matrix for implemented vs research items.
Dense / Sparse / Metric / EMA (elastic) / EXACT (lossless) as swappable strategy contract for controlled experiments.
See HDC strategy directions and dynamic size representations.
AutoDiscovery-style workflows treat eval data as a judge: regressions become artifacts (tests, minimal repros, documented fixes).
See agentic code generation and DS20.
Goal: Expand deterministic coverage of “structured reasoning language” patterns (high precision, reproducible behavior).
Impact: Reduces dependency on external services, enables offline operation, deterministic parsing.
Goal: Use LLMs as proposers and disambiguators while keeping the system’s semantics checkable by DSL + evaluation.
Impact: Handles edge cases that grammar alone cannot, enables natural user interaction.
Goal: Make formal reasoning legible to humans: fluent explanations derived from proofs/traces (not post-hoc narratives).
Impact: Makes formal reasoning accessible to non-technical users, enables audit documentation.
Goal: Generate structured, evidence-linked reports from query/proof results (research notes, audits, review memos).
Impact: Transforms knowledge bases into publishable content, enables automated reporting.
Goal: Assist theory authoring: propose candidate theory fragments from structured text, validate via eval suites and proofs.
Impact: Bootstraps knowledge bases from existing content, accelerates domain modeling.
Goal: Advance the theoretical and practical foundations of hyperdimensional computing for reasoning.
Impact: A controlled substrate for retrieval and capacity research; informs UTE-scale theory manipulation.
Goal: Understand when holographic steps help and when symbolic validation is required; build safe hybrid workflows.
Impact: Fast retrieval and candidate generation for large theory sets, with auditable fallbacks.
Goal: Build an ecosystem of domain libraries (theory fragments + eval suites) that make UTE progress cumulative.
Impact: Domain specialization is the path to scientific-theory assistants; community contribution model.
Improve the “Linux layer”: stable semantics, clearer docs/spec statuses, richer eval coverage, and better developer ergonomics.
Anchor: implemented DS + evaluation-driven development.
Prototype UTE-layer primitives where they can be validated: evidence/provenance objects, contradiction reports, and revision workflows.
Anchor: DS34 (provenance/revision) + DS19 direction.
Integrate causal/mechanistic, uncertainty, numeric modeling, and experiment planning into closed-loop “theory improvement” workflows.
Anchor: DS35–DS38 (research) as the roadmap.