KR vocabularies, semantic execution layers and AI orchestration (K3D contribution proposal)

Hi Paola, Dave, Milton, Tyson, and all,

     Thank you, Paola, for the recent notes on welcoming constructive 
feedback and on the upper‑level ontic vocabulary bubble. Since you 
explicitly invited comments and alternative views on KR vocabularies and 
diagrams, I wanted to connect the work I’ve been doing in Knowledge3D 
(K3D) to:

  * Your “KR Languages / Formalisms” and “Knowledge Representation
    Learning” bubbles,

  * Milton’s recent messages on domains of discourse and the VentureBeat
    article about Karpathy’s orchestration prototype,

  * Dave’s and Tyson’s points about creativity, semantic execution
    layers, and adequacy.

I’ll keep the focus on KR, NS‑AI, and spatial reasoning; the current PTX 
implementation is just one possible substrate, not the main subject here.
All K3D vocabulary and architecture specs referenced below live under:
https://github.com/danielcamposramos/Knowledge3D/tree/main/docs/vocabulary/

1. KR vocabularies and ontic categories (top bubble)

Over the last months I’ve been formalizing the K3D KR layer as a set of 
vocabularies and interface specifications designed to sit exactly in the 
blue bubbles you drew for “KR Languages / Formalisms”, “Knowledge 
Representation Learning”, and “Reliability Engineering”, while treating 
concrete domain Houses as “domains of discourse” that may indeed be out 
of scope for AI‑KR.

In particular:

  * K3D Node Specification
    (https://github.com/danielcamposramos/Knowledge3D/tree/main/docs/vocabulary/K3D_NODE_SPECIFICATION.md)
    Defines the atomic KR unit in K3D: a node that co‑locates
    – human‑oriented geometry and visual encodings,
    – machine‑oriented embeddings and RDF/OWL‑compatible metadata,
    – provenance, confidence and relational edges, all in a single
    structure (extras.k3d) at a given (x, y, z) coordinate.

    Conceptually, this is a spatial KR language where “semantic
    similarity = spatial proximity” and ontic categories (concept,
    event, reality_atom, etc.) are explicit fields.


  * Dual‑Client Contract Specification
    (https://github.com/danielcamposramos/Knowledge3D/tree/main/docs/vocabulary/DUAL_CLIENT_CONTRACT_SPECIFICATION.md)
    Specifies how the same nodes support both
    – human clients (3D/VR, audio, Braille) and
    – AI clients (embeddings, graphs, action buffers), with guarantees
    that both are operating on identical data (hashes, timestamps,
    versions). This sits squarely in the “KR Languages / Formalisms” +
    “Reliability Engineering” bubbles: it is a contract for explainable,
    shared KR views.


  * Sovereign NSI Specification
    (https://github.com/danielcamposramos/Knowledge3D/tree/main/docs/vocabulary/SOVEREIGN_NSI_SPECIFICATION.md)
    Describes how symbolic structures (RDF/OWL in House) and neural
    processing (embeddings, similarity search, procedural reasoning) are
    integrated spatially rather than glued together via APIs. Symbolic
    constraints are enforced against the same nodes that the neural
    layer retrieves.
    This is my contribution toward the “neurosymbolic AI” and “KR
    learning” aspects of the CG’s charter.


  * Spatial UI Architecture (SUAS)
    (https://github.com/danielcamposramos/Knowledge3D/tree/main/docs/vocabulary/SPATIAL_UI_ARCHITECTURE_SPECIFICATION.md)
    and Universal Accessibility Specification
    (https://github.com/danielcamposramos/Knowledge3D/tree/main/docs/vocabulary/UNIVERSAL_ACCESSIBILITY_SPECIFICATION.md)
    Treat the House / Galaxy pattern as a spatial KR user interface:
    rooms encode cognitive roles (Library, Workshop, etc.), and
    accessibility modalities (Braille, sign, audio) become additional
    facets on the same nodes. This is very much “KR as symbolic, natural
    language representation” plus spatial semantics.


 From my side, these documents are already an attempt to do what you’re 
now asking for in the ontic‑categories work: mapping conceptual KR 
categories onto a concrete, inspectable representation. I’m happy to 
adapt terminology to your table if that helps.

2. Semantic Execution Layer and KR‑aware agent behavior

Tyson’s description of a Semantic Execution Layer—above KR and below 
agent runtime—matches closely what K3D is trying to define at the 
representation level:

  * Reality Enabler Specification
    (https://github.com/danielcamposramos/Knowledge3D/tree/main/docs/vocabulary/REALITY_ENABLER_SPECIFICATION.md)
    Defines “reality nodes” (reality_atom, reality_molecule,
    reality_system, etc.) with:
    – visual_rpn: how something appears in 3D,
    – behavior_rpn / meaning_rpn: how it behaves under domain laws,
    – compositional component_refs to lower‑level entities.
    In more abstract KR terms, this is a layer where ontic categories
    (forces, resources, agents, institutions, constraints) become
    executable, auditable transition laws over state, expressed in a
    compact domain‑agnostic RPN language.


  * Math Core Specification and RPN Domain Opcode Registry
    (https://github.com/danielcamposramos/Knowledge3D/tree/main/docs/vocabulary/MATH_CORE_SPECIFICATION.md,https://github.com/danielcamposramos/Knowledge3D/tree/main/docs/vocabulary/RPN_DOMAIN_OPCODE_REGISTRY.md)
    Together, they define the shared “vocabulary of operations” for that
    semantic execution layer: a small set of typed operations over
    vectors, sets, graphs, and temporal structures that
    physics/chemistry/biology programs are written in. This is intended
    as a KR‑level language for procedural knowledge, independent of any
    particular numerical backend.


In the “Semantic Agent Communication” thread, Paola, you wrote that you 
“could not currently see the relation between KR and the execution layer 
and semantic agents” and then asked Tyson to clarify. After his detailed 
reply, you answered that you felt “great relief because I understand 
every word, and it answers the question ‘how does this relate to KR as 
we know it’”.

The K3D specs above were written with exactly that question in mind: 
they are my attempt to explain, in KR terms, how a semantic execution 
layer (for physics, contracts, workflows, etc.) can be grounded in 
explicit ontic categories, vocabularies, and domains of discourse. If 
they are failing to communicate that connection, I would very much 
appreciate concrete feedback on what is still unclear on what I'm not 
expressing clearly.

3. Creativity, domains of discourse, and the “mathematical ceiling”

In the thread Milton started (“A mathematical ceiling limits generative 
AI to amateur‑level creativity”) and Dave’s reply, a few ideas converge:

  * domains of discourse instead of one monolithic space,

  * a separation between System‑1‑style pattern engines and
    System‑2‑style reasoning,

  * creativity as structured recombination within and across explicit
    domains.

K3D’s memory architecture is an attempt to pin those ideas down in KR terms:

  * Three‑Brain System Specification
    (https://github.com/danielcamposramos/Knowledge3D/tree/main/docs/vocabulary/THREE_BRAIN_SYSTEM_SPECIFICATION.md)
    separates Cranium (reasoning + learning), Galaxy (active memory) and
    House (persistent KR), with a “Shadow Copy” mechanism for learning
    procedural patterns during inference without turning everything into
    undifferentiated parameters.


  * SleepTime Protocol
    (https://github.com/danielcamposramos/Knowledge3D/tree/main/docs/vocabulary/SLEEPTIME_PROTOCOL_SPECIFICATION.md)
    defines when and how Galaxy state is consolidated into House, with
    explicit pruning/merging and provenance, so knowledge is attached to
    specific domains (Houses, rooms, galaxies) rather than to a global
    opaque model.


  * Adaptive Procedural Compression
    (https://github.com/danielcamposramos/Knowledge3D/tree/main/docs/vocabulary/ADAPTIVE_PROCEDURAL_COMPRESSION_SPECIFICATION.md)
    is where Milton’s prediction that “domains of discourse will make
    models smaller” becomes measurable: different Matryoshka dimensions
    and PD04 codecs serve different semantic domains and levels of
    detail, with fidelity guarantees.


 From a CG perspective, the point is that adequacy and creativity are 
enforced at the level of explicit KR structures and domains of 
discourse, not just by making one big model more expressive. That seems 
aligned with both Milton’s and Dave’s concerns.

4. Orchestration and multi‑agent reasoning (Karpathy’s “LLM Council” and 
MVCIC)

Milton’s VentureBeat link about Karpathy’s “LLM Council” shows a basic 
multi‑model deliberation pattern (several models generate, critique, and 
a “chair” synthesizes), and frames this as an orchestration layer 
between applications and volatile model providers.

In my work, the analogous pattern is the Multi‑Vibe Code In Chain 
(MVCIC) method, but it is important to clarify how it actually runs today:

  * MVCIC is currently a human‑orchestrated methodology, not yet an
    in‑engine layer inside K3D (future plans).
    – On the implementation side, I’ve been using VS Code plus paid
    access to Codex/Claude Code, and a rotating set of free‑tier
    browser‑based AI partners (Qwen, Kimi, GLM, DeepSeek, Gemini, etc.).
    – All orchestration, prompt engineering, and record‑keeping is
    manual: I maintain Markdown chain files, craft partner‑specific
    briefings, paste context/results across tools, and keep a written
    registry of what each partner proposed and why.
    – This workflow is documented in
    https://github.com/danielcamposramos/Knowledge3D/tree/main/docs/multi_vibe_orchestration/
    <https://github.com/danielcamposramos/Knowledge3D/tree/main/docs/multi_vibe_orchestration/>
    and summarized in
    https://github.com/danielcamposramos/Knowledge3D/tree/main/docs/MVCIC_TECH_NOTE.md
    <https://github.com/danielcamposramos/Knowledge3D/tree/main/docs/MVCIC_TECH_NOTE.md>
    in the K3D repo.

  * The connection to K3D is that MVCIC chains are designed to feed into
    (or be mirrored by) the KR layer above: chain steps become nodes
    with provenance; decisions and alternatives become explicit edges
    and procedures. That part is aspirational—but the KR vocabulary for
    it is already defined in the specs linked at the start.


So in terms of AI‑KR scope, MVCIC today is a practical, 
human‑in‑the‑loop orchestration method for multi‑agent systems, and K3D 
provides a candidate KR substrate where those chains could be 
represented in a standardized, inspectable way. If and when providers 
adopt a K3D‑like substrate, the orchestration patterns we are all 
discussing (Karpathy’s council, MVCIC, Tyson’s AgentIDL‑based agents) 
could then sit on a shared representational base.

5. Where I am confused and what I’m asking

Practically, I believe I have already done much of the work you are now 
asking for:

  * The suite under docs/vocabulary/ and the earlier W3C insertion
    drafts were written specifically to map K3D’s KR structures onto the
    CG’s vocabulary, diagrams, and mission (KR languages/formalisms, KR
    learning, reliability, NS‑AI, accessibility).

  * I have shared these links several times on the list and via the
    wiki, and have tried to keep each document tightly within the KR
    scope, deliberately separating them from low‑level implementation
    details.

At the same time:

  * In AI‑KR, I have often been told that this work “has nothing to do
    with the scope”, even when it sits exactly in the KR‑language /
    KR‑learning / reliability bubbles;

  * in one revision, the bubble where most of this work sits was
    explicitly marked “NOT IN SCOPE”, and later that area was reduced or
    removed;

  * in contrast, in the Semantic Agent Communication thread, Tyson’s
    execution‑layer work received exactly the kind of “how does this
    relate to KR as we know it” question I’ve been trying to answer,
    followed by a very positive response once he explained the mapping.

I don’t want to turn this into a personal dispute; I’m trying to 
understand how to contribute effectively.

So my concrete questions are:

 1. For the KR‑language / KR‑learning / reliability bubbles in your
    current diagram, is there a specific criterion that the K3D
    vocabularies and specs fail to meet?

 2. If so, could you please spell out what is missing or incompatible
    (terminology, level of formality, dependence on spatial metaphors,
    etc.) so I can adjust accordingly?

 3. Or, if the decision is that these aspects of K3D are simply not of
    interest to AI‑KR regardless of scope, it would help to hear that
    explicitly so I can focus my efforts in better‑aligned W3C groups
    (CogAI, s‑agent‑comm, Semantic Web, WoT, etc.), where this kind of
    KR‑plus‑execution work is already under active discussion.

I’ve tried to keep my communications factual, respectful, and grounded 
in concrete, version‑controlled artifacts. I’m very willing to adapt 
vocabulary and presentation if there is a clear path to doing so within 
AI‑KR’s scope.

Right now, however, it feels as though the work is being treated as 
out‑of‑scope despite overlapping the published goals, and I’d appreciate 
some direct guidance on how to interpret that.


Best regards,
Daniel Ramos
Knowledge3D / AI‑RLWHF
https://github.com/danielcamposramos/Knowledge3D