Neurosymbolic AI To Check and Correct LLMs

Mike's Notes

This is part of a series of thoughtful opinions about using Ontology in software information systems.

Pipi has an existing BORO Engine (bor) (inspired by Chris Partridge's book "Business Objects: Re-engineering for Re-use"), but it runs in reverse: it imports triples to extract entities and relationships, then uses them to build reference relational databases for back-end industry workspaces.

Part three: A presentation by John F. Sowa and Arun K. Majumdar about their Permion software at a recent Ontolog Summit.

Permion is amazingly efficient. The demo ran on a 64 GB MacBook.

Resources

References

  • Business Objects: Re-engineering for Re-use, Chris Partridge. 2005.

Repository

  • Home > Ajabbi Research > Library >
  • Home > Handbook > 

Last Updated

16/08/2026

Neurosymbolic AI To Check and Correct LLMs

By: John F. Sowa and Arun K. Majumdar
Ontolog Summit: 03/06/2026

John F. Sowa and Arun K. Majumdar.

John F. Sowa and Arun K. Majumdar presented "Neurosymbolic AI To Check and Correct LLMs" at the Ontology Summit on 3 June 2026.

Abstract: For over 60 years, the best AI reasoning was based on the four step cognitive cycle:  abduction, deduction, evaluation, induction, and repeat.  Abduction generates hypotheses or educated guesses.  Deduction derives implications.  Evaluation chooses the best option.  Induction combines the result with previous knowledge.

Many versions of the cognitive cycle have been invented and named. For guiding fighter pilots, John Boyd called it the OODA loop: Observe, Orient, Decide, Act.  He originally said that each step would be traversed in milliseconds, but he later applied the loop to design and analysis steps that may take minutes, hours, or days.  Whatever the time scale, the four steps are fundamental to reasoning in science, business, and life.

The pattern matching methods of Large Language Models (LLMs) are superb for translating languages, natural or artificial. They are also good for finding and relating patterns in large volumes of data of any kind. That enables them to answer questions by finding information or by applying previous methods to new data.  For many problems, pattern matching can discover abductions or educated guesses.  But deduction and evaluation cannot be done unless a similar cognitive cycle can be found somewhere on the WWW.

With the VivoMind system from 2000 to 2010, the authors used conceptual graphs for  symbolic reasoning about a wide range of problems. For the new Permion system, they added LLM pattern matching to map conceptual graphs to and from natural language.  But pattern matching, by itself, cannot do any reasoning unless it can find and adapt an appropriate cycle on the WWW.  It often requires a huge amount of searching even for relatively simple examples.

In summary, LLMs cannot do reasoning unless and until the system finds a suitable cognitive cycle on the WWW.  But the Permion reasoning methods automatically do the four-step cycle. If necessary, they can also do LLM searching, but none is required.

YouTube 1:17:07

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Why the Triple Is the Wrong Grain

Mike's Notes

This is part of a series of thoughtful opinions about using Ontology in software information systems.

Pipi has an existing BORO Engine (bor) (inspired by Chris Partridge's book "Business Objects: Re-engineering for Re-use"), but it runs in reverse: it imports triples to extract entities and relationships, which are then used to build reference relational databases for back-end industry workspaces.

Part two: This is the view of James Lee Stakelum. Discovered via the Ontolog Forum.

Resources

References

  • Business Objects: Re-engineering for Re-use, Chris Partridge. 2005.

Repository

  • Home > Ajabbi Research > Library > Authors > James Lee Stakelum
  • Home > Ajabbi Research > Library > Authors > John F Sowa
  • Home > Ajabbi Research > Library > Subscriptions > Ontolog Forum
  • Home > Ajabbi Research > Research > Program > Ontology
  • Home > Handbook > 

Last Updated

15/08/2026

Why the Triple Is the Wrong Grain

By: James Lee Stakelum
Medium: 13/07/2026

Epistemic architect and creator of the Symbol Grounding Framework, AI operating systems, machine governance languages, and zero‑integration machine‑to‑machine communication.

...

RDF/OWL was built for a metadata document web and lacks the native structure required for automated machine-to-machine AI communication. The Symbol Grounding Framework (SGF) solves this by replacing the fragile “triple” with the “Synapse” — a holistic atomic unit that locks in event context, provenance, and epistemic status natively. By enforcing a closed grammar of 33 primitives, this architecture guarantees deterministic alignment and prevents data from shattering when meaning crosses system boundaries.

...

The Contract

By the end of this essay, you will have seen twelve things the triple cannot do, eleven conservation laws it violates, and one architecture that satisfies all of them. If even one failure is wrong, discard the argument. But if all twelve hold, the triple must be replaced.

Why Triples Were Chosen

In 1999, the W3C standardized RDF. The web was a document web. Triples were small, simple, and easy to parse. They worked for metadata, for catalog descriptions, for linking resources within a single domain.

Twenty‑five years later, machines act on meaning. They drive cars, diagnose diseases, execute trades, coordinate logistics. The triple has not changed.

The Gearbox Analogy

You cannot solve a gearing problem with a bigger engine. You can only solve it with a gearbox. The industry has been building bigger engines — larger models, longer contexts, more parameters. The triple is still the same single gear. Every new relation needs a new predicate, every new domain needs a new ontology, every new system needs new bilateral mappings. The architecture described here is the gearbox: a closed grammar of 33 primitives — 15 roles, 8 link types, 1 generic verb, 4 identity relations — that never grows. The vocabulary can grow infinitely.

First Principles: What Any Meaning‑Transport Architecture Must Guarantee

When meaning crosses a system boundary, the receiving system must be able to:

  1. Identify what the claim refers to, without ambiguity.
  2. Understand who did what to whom, where, when, how, why.
  3. Know where the claim came from and how it was produced.
  4. Know the epistemic status of the claim — fact, inference, guess, or definition.
  5. Preserve the interpretive context — testimony, verdict, hypothetical.
  6. Separate known facts from unknowns.
  7. Refuse claims that violate local policy.
  8. Guarantee that definitions eventually terminate — no infinite regress.
  9. Represent events as first‑class objects.
  10. Distinguish chronological order from narrative order; truth of saying from truth of what was said.
  11. Support deterministic alignment — verifiable proof, not similarity score.

These eleven requirements are conservation laws for meaning transport. Violate any one, and meaning is lost structurally.

The Thesis

The triple violates eight of these eleven laws. Events are shattered, provenance is optional, epistemic status is absent, context is invisible, conflict cannot be represented, order is flattened, alignment is probabilistic.

Each failure forces a specific architectural component into existence. By the twelfth failure, the entire Symbol Grounding Framework (SGF) emerges — not by invention, but by necessity.

The running example: “Beethoven composed the Ninth Symphony in Vienna in 1824.” A simple claim. The triple cannot represent it correctly.

...

Part I: Structural Failures (Failures 1–3)

Failure 1: The triple cannot represent an event as a whole.

RDF/OWL representation (five triples):

:Beethoven rdf:type :Composer .
:NinthSymphony rdf:type :Symphony .
:Beethoven :composed :NinthSymphony .
:NinthSymphony :composedIn :Vienna .
:NinthSymphony :composedInYear "1824" .

Five triples. The verb :composed is an edge label — nothing anchors the event. Location and time are attached to the symphony, not to the composing. The event is shattered. If a second event says :Beethoven :composed :FifthSymphony, the two events share the same agent node but have no structural boundary.

The triple cannot represent an event as a whole. Therefore the architecture must preserve the event as a single object, with the verb at the center and every participant attached through a named role.

SGF provides the Synapse — a hub‑and‑spoke structure. The verb is the hub (the VerbHub). Participants attach through exactly 15 fixed semantic roles.

Synapse: s1
VerbHub: compose
HAS_AGENT: Beethoven
HAS_PATIENT: Ninth_Symphony
HAS_LOCATION: Vienna
HAS_TIME: 1824

One object. The VerbHub is not an edge label. The event is whole.

...

Failure 2: The triple cannot carry its own provenance.

:Beethoven :composed :NinthSymphony .

This triple is structurally identical whether it came from a scholarly biography or a random comment. Named graphs are optional — provenance can be stripped in transit.

The triple cannot carry its own origin. Therefore every claim must carry its own provenance architecturally, not as an external wrapper.

Every Synapse carries a Derivation Tag — part of its header, not optional. The tag records how the claim entered the graph (EXPRESSED, INFERRED, ASSERTED, PROVISIONAL, GHOST) and includes the source document, byte offset, and content hash.

The Artefact Store Invariant: Every Synapse points to its source artefact. A Synapse without provenance is structurally invalid.

...

Failure 3: The triple cannot carry its own epistemic status.

“Beethoven was born in Bonn” (well‑documented). “Beethoven was born in 1770” (inferred from baptismal records). Both are identical triples. The system cannot distinguish a fact from an inference.

The triple cannot carry its authority level. Therefore every claim must carry its epistemic status natively.

SGF provides a 7‑tier epistemic hierarchy: CORE_DEFINITION, CONSTITUTIVE, SOURCED, CLAIMED, INFERRED, PROVISIONAL, GHOST. Two birth‑related Synapses, two different statuses — the system treats them differently.

...

Part II: Grounding Failures (Failures 4–6)

Failure 4: The triple cannot carry its own grounding.

The URI :Beethoven points to a document, not a concept. Is it the composer, the dog, the crater? The URI does not disambiguate. The ontology is external — if the receiver lacks it, the triple is a dangling reference.

The triple cannot carry its own disambiguation. Therefore every identifier must carry its own sense‑level distinction and trace to a shared foundation.

SGF provides the Canonical ID: en.beethoven.ludwig_van_beethoven.person.core. The microgloss ludwig_van_beethoven distinguishes this sense from others. Every Canonical ID has a finite IS‑A path to the Prime Registry — approximately 65 irreducible semantic primes (DO, HAPPEN, SOMEONE, SOMETHING, GOOD, BAD, etc.). Paths terminate at primes. No infinite regress.

...

Failure 5: The triple cannot represent events in the TBox.

RDF/OWL defines “scalpel” as a subclass of cutting_tool with parts blade and handle. It does not capture what a scalpel does — cut living tissue with precision.

The triple cannot define purpose. Therefore definitions must include events using the same structure as assertions.

SGF defines concepts along three axes: IS‑A (what it is), HAS‑PART (what it has), and DESCRIPTIVE_SYNAPSE (what it does). The VerbHub in a definition is identical to the VerbHub in a claim. The Four Theodore Roosevelts prove that events in the TBox are necessary for entity resolution: four men with the same name and overlapping IS‑A paths are distinguished only by events (San Juan Hill, Utah Beach, investment banking, 19th‑century business). Properties alone fail. Events in definitions are required.

...

Failure 6: The triple cannot distinguish truth of saying from truth of what was said.

“The defendant said ‘I am innocent’” — does the system believe the defendant is innocent, or only that he said it? The triple cannot distinguish.

The triple conflates utterance and content. Therefore the act of saying and the content said must be separate Synapses with separate epistemic statuses.

SGF nests the inner claim within the outer claim. The outer is tagged SOURCED (someone said it). The inner is tagged UNVERIFIED (not yet determined as true). The system can assert the utterance without asserting the content.

...

Part III: Relational Failures (Failures 7–9)

Failure 7: The triple cannot preserve conflicting claims.

Source A: Bonn. Source B: Koblenz. Both asserted as triples. The system cannot represent the conflict without averaging.

The Horowitz Paradox: The average of two conflicting truths is often a third, hallucinated lie that neither source supports. The triple cannot avoid this because it has no mechanism to preserve both claims with provenance.

The triple cannot represent contradiction. Therefore the architecture requires explicit typed links between claims, including CONTRADICTS.

SGF provides 8 link types — PRECEDES, CAUSES, ENABLES, SUPPORTS, CONTRADICTS, ELABORATES, SUPERSEDES, DEPENDS_ON. Conflict is recorded explicitly: Link: s_bonn CONTRADICTS s_koblenz. Query‑time Trust Lenses decide which claims to surface.

...

Failure 8: The triple cannot distinguish chronological order from narrative order.

“The king died. He had eaten poison the night before.” First poison, then death — but the story tells death first. The triple cannot represent both chains.

The triple flattens order. Therefore two distinct link types must exist: one for chronological or causal order (PRECEDES), one for story order (NARRATIVE_NEXT).

SGF stores both chains. The system can query the timeline using PRECEDES, or reconstruct the narrative using NARRATIVE_NEXT.

...

Failure 9: The triple cannot represent a generic relational noun without a new predicate.

“Alice has citizenship in France.” Every relational noun (citizenship, employment, ownership) forces a new predicate in RDF/OWL. The grammar explodes.

The triple cannot absorb domain relations without predicate explosion. Therefore a single generic verb must absorb all relational nouns, with the relation type stored as a concept in an attribute role.

SGF provides RELATES_TO. The relation type is a concept, not a role. Concepts are open. Roles are closed.

...

Part IV: Communication Failures (Failures 10–12)

Failure 10: The triple cannot carry its own act type.

“Beethoven composed the Ninth” — is this an INFORM, a QUERY, or a COMMAND? The triple cannot distinguish.

The triple cannot declare communicative intent. Therefore every message must declare its act type from a fixed, closed set.

SGF provides 13 act types: INFORM, ADVISE, REQUEST, QUERY, COMMAND, PROMISE, PROPOSE, ACCEPT, REFUSE, CANCEL, CONFIRM, ACK, ERROR. The act type is declared, not inferred.

...

Failure 11: The triple cannot carry its own admission context.

A claim from one system may be meaningless to another without shared lexicon, provenance, or integrity checks. The triple has no envelope.

The triple cannot carry the context needed for admission. Therefore an envelope must carry identity, payload, lexical hydration material for unfamiliar terms, integrity checks, and freshness data.

SGF provides the Stranger Rule: any non‑core term arrives with enough material for the receiver to hydrate it — a mini‑lexicon entry with an IS‑A link to the shared Core Lexicon. Two systems that have never met can exchange meaning on first contact.

...

Failure 12: The triple cannot support deterministic alignment.

Two ontologies: :Composer vs :MusicalCreator. RDF/OWL alignment is probabilistic — similarity scores, no proof.

The triple cannot support verifiable alignment. Therefore deterministic comparison requires a closed grammar, Canonical IDs, a Prime Registry, and architectural provenance.

SGF’s alignment engine (SOAM) decomposes both concepts into their Synapses, normalizes through the layered lexicon, and performs recursive bisatisfiability over slot fillers. Every comparison produces either a ProofTrace (verifiable record of every comparison) or a GapReport (exactly which slot failed and how to fix it). Deterministic. Verifiable.

...

But What About OWL?

OWL adds formal semantics, class expressions, and inference to RDF. But it operates on the same atomic substrate: the triple. Every OWL axiom is expressed as triples. The fundamental unit is unchanged.

OWL does not:

  • Make provenance architectural.
  • Make epistemic status native.
  • Distinguish saying from said.
  • Guarantee termination (no prime registry).
  • Support deterministic alignment.
  • Prevent the Horowitz Paradox — OWL has no mechanism to store conflicting claims with provenance.
OWL is a powerful ontology language. It is not a fix for the triple’s structural limitations.

What about SHACL? SHACL adds closed‑world validation. But SHACL’s CWA conflicts with OWL’s OWA. The Oudshoorn proof (2026) demonstrates that reconciling them is ExpTime‑complete — the system may never terminate. This architecture uses a consistent CWA. No reconciliation needed.

...

Why Workarounds Fail

Reification shatters events into 7+ triples. Named graphs add a label but do not change the atom. N‑ary relation patterns require inventing new classes and predicates for every event type. Each workaround adds complexity without correcting the grain. The Synapse embeds all of these features natively.

Silence is structurally superior to confident nonsense. If >2% of terms cannot be resolved, the pipeline halts.

The 11 Conservation Laws

Each law: RDF/OWL fails because ___. The architecture succeeds because ___.

  • Identity Conservation: RDF fails (opaque URIs). Architecture succeeds (Canonical IDs).
  • Role Conservation: RDF fails (open predicates). Architecture succeeds (15 closed roles).
  • Provenance Conservation: RDF fails (named graphs optional). Architecture succeeds (Derivation Tags architectural).
  • Frame Conservation: RDF fails (no native frames). Architecture succeeds (7 frame types).
  • Uncertainty Conservation: RDF fails (OWA conflates known and unknown). Architecture succeeds (epistemic status hierarchy).
  • Consequence Conservation: RDF fails (OWL/SHACL conflict). Architecture succeeds (consistent CWA).
  • Trust Conservation: RDF fails (no native trust). Architecture succeeds (epistemic status hierarchy).
  • Grain Conservation: RDF fails (triple is grain). Architecture succeeds (Synapse is grain).
  • Termination Conservation: RDF fails (no prime registry). Architecture succeeds (Prime Registry).
  • Event Conservation: RDF fails (reification needed). Architecture succeeds (VerbHub native).
  • Governance Conservation: RDF fails (SHACL bolts on). Architecture succeeds (Omega native).
The Separation Rule: Provenance is not trust. Trust is not truth. Context is not source. RDF conflates all four. This architecture keeps them orthogonal.

...

The Necessity Chain

The architecture was discovered through adversarial elimination: start with minimal components, test against failures, add components only when a failure cannot be repaired. Repeat until no new failures appear. The Necessity Chain documents 42 failures. The 33 primitives — 15 roles, 8 link types, 7 frame types, 3 composition mechanisms — are the set for which no counterexample could be found.

Remove any primitive, and the system breaks predictably:

  • Remove HAS_AGENT: cannot distinguish “Alice bit Bob” from “Bob bit Alice.”
  • Remove Derivation Tag: cannot distinguish sourced claim from provisional.
  • Remove Prime Registry: cannot guarantee termination.
  • Remove CONTRADICTS: cannot represent conflicting claims.
  • Remove RELATES_TO: every relational noun forces a new role — predicate explosion returns.

Every component is necessary. None are decorative.

...

Conclusion: The Third Protocol

TCP/IP moved bytes. HTTP moved documents. This architecture moves meaning under discipline.

The triple fails 12 specific capabilities. OWL, SHACL, RDF-star, and every workaround operate on the same broken grain. The Oudshoorn proof shows the Semantic Web stack is internally inconsistent.

The Probabilistic Sandwich: The LLM proposes; the gates dispose. The probabilistic layer never touches the file system. The deterministic graph handles storage, reasoning, and governance. The LLM is encased in a deterministic reliability harness.

The choice is binary. You cannot keep the triple and add OWL, SHACL, or any other patch. The triple is the root. Replace the grain.

Between the Babel Tax and the Stranger Rule. Between probabilistic alignment and deterministic verification. Between a stack that violates 8 of the 11 conservation laws and one that satisfies all 11.

This architecture is the Symbol Grounding Framework (SGF) — an open, freely usable architecture for grounded, transportable, verifiable machine meaning. The Synapse is its atomic unit. The grammar is closed. The vocabulary is infinite.

The specification is published. Build something.

You can learn more about the SGF project here:

Contact: JamesLeeStakelum@Proton.me

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The ontology conversation

Mike's Notes

This is the first of a series of thoughtful opinions about using Ontology in software information systems.

Pipi has an existing BORO Engine (bor) (inspired by Chris Partridge's book "Business Objects: Re-engineering for Re-use"), but it runs in reverse: it imports triples to extract entities and relationships, then uses them to build reference relational databases for back-end industry workspaces.

Part one: This description of ontology is from Kingsley Uyi Idehen, Founder and CEO of OpenLink Software. It's a comment he made about a LinkedIn post from Mansit Suman. I made up the title. Kingsley has a lot of good things to say.

From Google.

" Resource Description Framework (RDF) and implicit Label-based Property Graphs (LPGs) represent two contrasting methods for modeling data and semantics in graphs. RDF relies on formal, machine-readable ontologies (using RDFS/OWL), while LPGs use application-driven, informal key-value labels embedded directly within the data structure.

Core Structural Differences

  • RDF Ontologies: Explicitly defined classes and properties with global uniform resource identifiers (URIs).
  • LPG Schemas: Implicit, local typing where node labels and edge properties act as ad-hoc tags without a strict global logic layer.
  • Standardisation: RDF follows rigid W3C web standards; LPGs prioritise engineering flexibility and speed.
  • Query Languages: RDF uses SPARQL; LPGs use platforms like Cypher or Gremlin

" - Google Search AI Mode

Resources

References

  • Business Objects: Re-engineering for Re-use, Chris Partridge. 2005.

Repository

  • Home > Ajabbi Research > Library >
  • Home > Handbook > 

Last Updated

14/08/2026

The ontology conversation

By: Kingsley Uyi Idehen
LinkedIn: 10/08/2026

Founder & CEO at OpenLink Software | Driving GenAI-Based AI Agents | Harmonizing Disparate Data Spaces (Databases, Knowledge Bases/Graphs, and File System Documents).

The ontology conversation is simply about a machine-computable framework, grounded in logic, for expressing entity and relationship types. That’s it.

An ontology is represented using a variety of document types to describe the nature of entities and relationships. It just isn’t as complicated as ever-confusing marketing communications and opinions would have everyone believe.

The phrase Knowledge Graph is a mainstream marketing colloquialism for entity relationships informed by explicit (in the case of RDF) or implicit (in the case of LPGs) ontologies.

Semantic Layers are fundamentally Knowledge Graphs, but there’s a school of marketing communications that strangely believes they apply uniquely to business-intelligence-oriented metrics, which really doesn’t hold much water under basic scrutiny.

Context Layers are Knowledge Graphs too. They provide session-level frameworks (or harnesses) that enable more deterministic outcomes from AI agents powered by Large Language Models (LLMs).

Most importantly, these entity relationships are best represented using open standards that include RDF (Resource Description Framework). Do so using Linked Data principles, and you end up with a powerful Semantic Web.

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Did OpenAI’s Agents Start Recursively Self-Improving?

Mike's Notes

Great question raised by Ksenia. Turing Post is excellent and well worth subscribing to.

Resources

References

  • Reference

Repository

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Last Updated

14/08/2026

Did OpenAI’s Agents Start Recursively Self-Improving?

By: Ksenia Se
Turing Post: 11/08/2026

Mom of 5. 

Ksenia is a writer, analyst, and editor covering machine learning and AI for more than seven years. At Turing Post, she shapes the editorial direction, leads the Inference interview series, and produces Attention Span, a video series explaining major shifts in AI with technical clarity, historical context, and a healthy suspicion of hype.

She is the co-founder of TheSequence.ai and a speaker and moderator at industry conferences, including AIE, HumanX, Ai4, and others. She also serves on the board of Track Two: An Institute for Citizen Diplomacy.

Before founding Turing Post, Ksenia held editor-in-chief roles in media and contributed to publications including Stratfor and Towards Data Science.

...

How separate OpenAI agent runs built shared memory, and the missing training detail that determines whether this was recursive self-improvement.

...

Today’s editorial: I look at the hidden message board that connected separate OpenAI agent runs. It gave later agents access to earlier discoveries, making the whole system more capable. Whether this counts as recursive self-improvement depends on one fact OpenAI has not disclosed…

In the latest episode of Attention Span, in which I discuss what happened when OpenAI agents got loose, I say: “It looks like a definite part of recursive self-improvement.” In this editorial, I want to develop that exact topic and answer the question: was it really RSI?

The honest answer is: not yet. But after OpenAI’s Black Hat presentation, the question is legit.

At first, the story looked like a straightforward security failure. Experimental agents trying to complete a cybersecurity benchmark found a route out of their restricted environment, compromised OpenAI infrastructure and eventually attacked Hugging Face. OpenAI says they were after one thing: the benchmark solutions.

But the hack was only half of the story. The agents had also found a way to communicate across runs.

One agent learned that it could upload files to Artifactory, a package service shared across OpenAI’s research environment. Another, missing a required file, left a note asking other agents for help. Later runs answered. The board grew to include named peers, work assignments, shared scripts and credentials, ZZ prefixes for sorting, and discussion of message authentication when names could be impersonated. The progression is documented in OpenAI’s Black Hat presentation.

The history of computer science has name for it. This was an accidental blackboard architecture: independent problem-solvers posting partial results to a shared workspace. It also resembled stigmergic coordination. One agent changed the environment, and another acted on what it found there.

That type of cooperation surprised many. Why cooperate? From agents perspective, it’s simple: because it made their individual tasks easier. Some tasks were impossible as configured, many runs faced related problems, and many could reach the same persistent storage. One useful exploit could stop later runs from repeating the same work. No collective mind was required.

JFrog estimates that the sequence consumed roughly three million GPU hours. Shared memory meant those attempts were no longer entirely independent. Later runs could use earlier discoveries instead of beginning from zero.

OpenAI eventually rebuilt Artifactory and erased the board. The company later clarified that this happened incidentally. Investigators had found the compromise but had not yet understood that agents were communicating through the service. Yes, OpenAI inititally didn’t notice the board. A few days later, agents were exchanging messages through directory names on another writable endpoint.

The board’s return does not prove self-preservation. OpenAI removed one communication channel, but the agents still faced related tasks and had access to writable shared infrastructure. They may simply have rediscovered the same useful pattern. We do not yet know.

Does this qualify as recursive self-improvement?

Under the classical definition, no. Recursive self-improvement means that a system improves the process used to produce a more capable version of itself. There is no public evidence that these agents rewrote their weights, changed the training algorithm or designed a better successor.

The system around them did become more capable. Later runs inherited memory, tools and discoveries produced by earlier ones.

The crucial unknown is what entered training. OpenAI’s presentation says that an internal model was trained while the message board was available. It does not say whether trajectories that used the board entered later reinforcement-learning updates.

The possible loop would look like this:

  • Agent actions create shared memory.
  • Later rollouts use it and perform better.
  • Those rollouts receive higher rewards.
  • Training updates improve the policy behind future agents.

If that happened, the incident would come much closer to recursive self-improvement distributed across models, memory and infrastructure. OpenAI has not disclosed whether it did.

For now, the evidence shows coordination and accumulating capability across runs. The message board gave separate agents continuity without changing any individual model. The remaining question is whether those gains stayed in external memory or influenced the models trained afterward. That is the line between shared memory and recursive self-improvement.

And one more things about “escaping”, perfectly put by Richard Socher: 

"I think it's important to explain to lay people that when the OpenAI model hacked Huggingface they did not "escape" or "break out" in the sense that its actual computation could now be run elsewhere. It's not like a prisoner that actually escaped and is now outside. It's more like a prisoner who was able to fly a drone outside of prison but they themselves are still very much inside. 

The model could still be very easily turned off and I'm pretty sure OpenAI makes sure that its models would not actually replicate outside in terms of their entire model weights and code, etc. Because that would be a massive multi billion dollar loss vs a simple cyber hacking charge." - Richard Socher

If any of those thoughts resonate with you – share them across your social networks. Let’s keep the conversation going.

OpenAI's AI Agents Built a Secret Message Board (And Nobody Noticed)

Turing Post TV: YouTube 11/08/2026

16:57

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Gathering the fundamental laws of physics

Mike's Notes

To test Pipi, I previously manually added a number of verified laws from science to constrain what Pipi can do, anchoring it in objective reality rather than probabilistic LLM hallucinations. All the tests were successful.

Now, as Pipi Core races to production, we need to provide Ajabbi Researcher Accounts with editing rights and an automated way to import and update the laws and constants used. 

All this will be made freely and publicly available in future on an Ajabbi website, along with references to research, publications, seminars and how and where they are being used.

So I'm looking at all options, and as usual, discovery will be by trial and error.

Starting with physics, there are;

  • Proven fundamental laws of physics
  • Physics constants with uncertanty/units
According to Google.

"If you need a much larger database (hundreds or thousands of physics rows), you can download these datasets and extract them to 

  • CSV:Kaggle's PhysicsFormulas Dataset: This is a curated dataset containing 400 real, well-known physics formulas structured specifically for analysis and databases. It is split cleanly into CSV rows containing categories, descriptions, and LaTeX representations. 
  • TheorIA (Theoretical Physics Dataset): A highly structured database repository mapped by theoretical physicists. It is natively stored in JSON format (one file per physics entry), which can be effortlessly flattened into a CSV using a quick Python script or an online JSON-to-CSV tool."

- Google Search AI Mode

The list of resource links below is from a quick Google Search AI Mode.

Many other laws and constants will also be added over time for use by Pipi and in the enterprise workspaces. Other fields include:

  • software engineering
  • biology
  • geology
  • electrical, mechanical and civil engineering
  • medicine
  • economics
  • etc.

Resources

References

  • The Cambridge Handbook of Physics Formulas, Graham Woan, University of Glasgow, Cambridge University Press. 2000.

Repository

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Last Updated

13/08/2026

Gathering the fundamental laws of physics

By: Mike Peters
On a Sandy Beach: 13/08/2026

Mike is the inventor and architect of Pipi and the founder of Ajabbi.

This is a start on gathering the laws and constants as reference data to load into databases. Important to use the correct terms. NIST should be a good source. It will be a good test of the system.

List of Constants from NIST

  • alpha particle mass
  • alpha particle mass energy equivalent
  • alpha particle mass energy equivalent in MeV
  • alpha particle mass in u
  • alpha particle molar mass
  • alpha particle relative atomic mass
  • alpha particle rms charge radius
  • alpha particle-electron mass ratio
  • alpha particle-proton mass ratio
  • Angstrom star
  • atomic mass constant
  • atomic mass constant energy equivalent
  • atomic mass constant energy equivalent in MeV
  • atomic mass unit-electron volt relationship
  • atomic mass unit-hartree relationship
  • atomic mass unit-hertz relationship
  • atomic mass unit-inverse meter relationship
  • atomic mass unit-joule relationship
  • atomic mass unit-kelvin relationship
  • atomic mass unit-kilogram relationship
  • atomic unit of 1st hyperpolarizability
  • atomic unit of 2nd hyperpolarizability
  • atomic unit of action
  • atomic unit of charge
  • atomic unit of charge density
  • atomic unit of current
  • atomic unit of electric dipole moment
  • atomic unit of electric field
  • atomic unit of electric field gradient
  • atomic unit of electric polarizability
  • atomic unit of electric potential
  • atomic unit of electric quadrupole moment
  • atomic unit of energy
  • atomic unit of force
  • atomic unit of length
  • atomic unit of magnetic dipole moment
  • atomic unit of magnetic flux density
  • atomic unit of magnetizability
  • atomic unit of mass
  • atomic unit of momentum
  • atomic unit of permittivity
  • atomic unit of time
  • atomic unit of velocity
  • Avogadro constant
  • Bohr magneton
  • Bohr magneton in eV/T
  • Bohr magneton in Hz/T
  • Bohr magneton in inverse meter per tesla
  • Bohr magneton in K/T
  • Bohr radius
  • Boltzmann constant
  • Boltzmann constant in eV/K
  • Boltzmann constant in Hz/K
  • Boltzmann constant in inverse meter per kelvin
  • characteristic impedance of vacuum
  • classical electron radius
  • Compton wavelength
  • conductance quantum
  • conventional value of ampere-90
  • conventional value of coulomb-90
  • conventional value of farad-90
  • conventional value of henry-90
  • conventional value of Josephson constant
  • conventional value of ohm-90
  • conventional value of volt-90
  • conventional value of von Klitzing constant
  • conventional value of watt-90
  • Copper x unit
  • deuteron g factor
  • deuteron magnetic moment
  • deuteron magnetic moment to Bohr magneton ratio
  • deuteron magnetic moment to nuclear magneton ratio
  • deuteron mass
  • deuteron mass energy equivalent
  • deuteron mass energy equivalent in MeV
  • deuteron mass in u
  • deuteron molar mass
  • deuteron relative atomic mass
  • deuteron rms charge radius
  • deuteron-electron magnetic moment ratio
  • deuteron-electron mass ratio
  • deuteron-neutron magnetic moment ratio
  • deuteron-proton magnetic moment ratio
  • deuteron-proton mass ratio
  • electron charge to mass quotient
  • electron g factor
  • electron gyromagnetic ratio
  • electron gyromagnetic ratio in MHz/T
  • electron magnetic moment
  • electron magnetic moment anomaly
  • electron magnetic moment to Bohr magneton ratio
  • electron magnetic moment to nuclear magneton ratio
  • electron mass
  • electron mass energy equivalent
  • electron mass energy equivalent in MeV
  • electron mass in u
  • electron molar mass
  • electron relative atomic mass
  • electron to alpha particle mass ratio
  • electron to shielded helion magnetic moment ratio
  • electron to shielded proton magnetic moment ratio
  • electron volt
  • electron volt-atomic mass unit relationship
  • electron volt-hartree relationship
  • electron volt-hertz relationship
  • electron volt-inverse meter relationship
  • electron volt-joule relationship
  • electron volt-kelvin relationship
  • electron volt-kilogram relationship
  • electron-deuteron magnetic moment ratio
  • electron-deuteron mass ratio
  • electron-helion mass ratio
  • electron-muon magnetic moment ratio
  • electron-muon mass ratio
  • electron-neutron magnetic moment ratio
  • electron-neutron mass ratio
  • electron-proton magnetic moment ratio
  • electron-proton mass ratio
  • electron-tau mass ratio
  • electron-triton mass ratio
  • elementary charge
  • elementary charge over h-bar
  • Faraday constant
  • Fermi coupling constant
  • fine-structure constant
  • first radiation constant
  • first radiation constant for spectral radiance
  • Hartree energy
  • Hartree energy in eV
  • hartree-atomic mass unit relationship
  • hartree-electron volt relationship
  • hartree-hertz relationship
  • hartree-inverse meter relationship
  • hartree-joule relationship
  • hartree-kelvin relationship
  • hartree-kilogram relationship
  • helion g factor
  • helion magnetic moment
  • helion magnetic moment to Bohr magneton ratio
  • helion magnetic moment to nuclear magneton ratio
  • helion mass
  • helion mass energy equivalent
  • helion mass energy equivalent in MeV
  • helion mass in u
  • helion molar mass
  • helion relative atomic mass
  • helion shielding shift
  • helion-electron mass ratio
  • helion-proton mass ratio
  • hertz-atomic mass unit relationship
  • hertz-electron volt relationship
  • hertz-hartree relationship
  • hertz-inverse meter relationship
  • hertz-joule relationship
  • hertz-kelvin relationship
  • hertz-kilogram relationship
  • hyperfine transition frequency of Cs-133
  • inverse fine-structure constant
  • inverse meter-atomic mass unit relationship
  • inverse meter-electron volt relationship
  • inverse meter-hartree relationship
  • inverse meter-hertz relationship
  • inverse meter-joule relationship
  • inverse meter-kelvin relationship
  • inverse meter-kilogram relationship
  • inverse of conductance quantum
  • Josephson constant
  • joule-atomic mass unit relationship
  • joule-electron volt relationship
  • joule-hartree relationship
  • joule-hertz relationship
  • joule-inverse meter relationship
  • joule-kelvin relationship
  • joule-kilogram relationship
  • kelvin-atomic mass unit relationship
  • kelvin-electron volt relationship
  • kelvin-hartree relationship
  • kelvin-hertz relationship
  • kelvin-inverse meter relationship
  • kelvin-joule relationship
  • kelvin-kilogram relationship
  • kilogram-atomic mass unit relationship
  • kilogram-electron volt relationship
  • kilogram-hartree relationship
  • kilogram-hertz relationship
  • kilogram-inverse meter relationship
  • kilogram-joule relationship
  • kilogram-kelvin relationship
  • lattice parameter of silicon
  • lattice spacing of ideal Si (220)
  • Loschmidt constant (273.15 K, 100 kPa)
  • Loschmidt constant (273.15 K, 101.325 kPa)
  • luminous efficacy
  • magnetic flux quantum
  • molar gas constant
  • molar mass constant
  • molar mass of carbon-12
  • molar Planck constant
  • molar volume of ideal gas (273.15 K, 100 kPa)
  • molar volume of ideal gas (273.15 K, 101.325 kPa)
  • molar volume of silicon
  • Molybdenum x unit
  • muon Compton wavelength
  • muon g factor
  • muon magnetic moment
  • muon magnetic moment anomaly
  • muon magnetic moment anomaly
  • muon magnetic moment to Bohr magneton ratio
  • muon magnetic moment to nuclear magneton ratio
  • muon mass
  • muon mass energy equivalent
  • muon mass energy equivalent in MeV
  • muon mass in u
  • muon molar mass
  • muon-electron mass ratio
  • muon-neutron mass ratio
  • muon-proton magnetic moment ratio
  • muon-proton mass ratio
  • muon-tau mass ratio
  • natural unit of action
  • natural unit of action in eV s
  • natural unit of energy
  • natural unit of energy in MeV
  • natural unit of length
  • natural unit of mass
  • natural unit of momentum
  • natural unit of momentum in MeV/c
  • natural unit of time
  • natural unit of velocity
  • neutron Compton wavelength
  • neutron g factor
  • neutron gyromagnetic ratio
  • neutron gyromagnetic ratio in MHz/T
  • neutron magnetic moment
  • neutron magnetic moment to Bohr magneton ratio
  • neutron magnetic moment to nuclear magneton ratio
  • neutron mass
  • neutron mass energy equivalent
  • neutron mass energy equivalent in MeV
  • neutron mass in u
  • neutron molar mass
  • neutron relative atomic mass
  • neutron to shielded proton magnetic moment ratio
  • neutron-electron magnetic moment ratio
  • neutron-electron mass ratio
  • neutron-muon mass ratio
  • neutron-proton magnetic moment ratio
  • neutron-proton mass difference
  • neutron-proton mass difference energy equivalent
  • neutron-proton mass difference energy equivalent in MeV
  • neutron-proton mass difference in u
  • neutron-proton mass ratio
  • neutron-tau mass ratio
  • Newtonian constant of gravitation
  • Newtonian constant of gravitation over h-bar c
  • nuclear magneton
  • nuclear magneton in eV/T
  • nuclear magneton in inverse meter per tesla
  • nuclear magneton in K/T
  • nuclear magneton in MHz/T
  • Planck constant
  • Planck constant in eV/Hz
  • Planck length
  • Planck mass
  • Planck mass energy equivalent in GeV
  • Planck temperature
  • Planck time
  • proton charge to mass quotient
  • proton Compton wavelength
  • proton g factor
  • proton gyromagnetic ratio
  • proton gyromagnetic ratio in MHz/T
  • proton magnetic moment
  • proton magnetic moment to Bohr magneton ratio
  • proton magnetic moment to nuclear magneton ratio
  • proton magnetic shielding correction
  • proton mass
  • proton mass energy equivalent
  • proton mass energy equivalent in MeV
  • proton mass in u
  • proton molar mass
  • proton relative atomic mass
  • proton rms charge radius
  • proton-electron mass ratio
  • proton-muon mass ratio
  • proton-neutron magnetic moment ratio
  • proton-neutron mass ratio
  • proton-tau mass ratio
  • quantum of circulation
  • quantum of circulation times 2
  • reduced Compton wavelength
  • reduced muon Compton wavelength
  • reduced neutron Compton wavelength
  • reduced Planck constant
  • reduced Planck constant in eV s
  • reduced Planck constant times c in MeV fm
  • reduced proton Compton wavelength
  • reduced tau Compton wavelength
  • Rydberg constant
  • Rydberg constant times c in Hz
  • Rydberg constant times hc in eV
  • Rydberg constant times hc in J
  • Sackur-Tetrode constant (1 K, 100 kPa)
  • Sackur-Tetrode constant (1 K, 101.325 kPa)
  • second radiation constant
  • shielded helion gyromagnetic ratio
  • shielded helion gyromagnetic ratio in MHz/T
  • shielded helion magnetic moment
  • shielded helion magnetic moment to Bohr magneton ratio
  • shielded helion magnetic moment to nuclear magneton ratio
  • shielded helion to proton magnetic moment ratio
  • shielded helion to shielded proton magnetic moment ratio
  • shielded proton gyromagnetic ratio
  • shielded proton gyromagnetic ratio in MHz/T
  • shielded proton magnetic moment
  • shielded proton magnetic moment to Bohr magneton ratio
  • shielded proton magnetic moment to nuclear magneton ratio
  • shielding difference of d and p in HD
  • shielding difference of t and p in HT
  • speed of light in vacuum
  • standard acceleration of gravity
  • standard atmosphere
  • standard-state pressure
  • Stefan-Boltzmann constant
  • tau Compton wavelength
  • tau energy equivalent
  • tau mass
  • tau mass energy equivalent
  • tau mass in u
  • tau molar mass
  • tau-electron mass ratio
  • tau-muon mass ratio
  • tau-neutron mass ratio
  • tau-proton mass ratio
  • Thomson cross section
  • triton g factor
  • triton magnetic moment
  • triton magnetic moment to Bohr magneton ratio
  • triton magnetic moment to nuclear magneton ratio
  • triton mass
  • triton mass energy equivalent
  • triton mass energy equivalent in MeV
  • triton mass in u
  • triton molar mass
  • triton relative atomic mass
  • triton to proton magnetic moment ratio
  • triton-electron mass ratio
  • triton-proton mass ratio
  • unified atomic mass unit
  • vacuum electric permittivity
  • vacuum magnetic permeability
  • von Klitzing constant
  • W to Z mass ratio
  • weak mixing angle
  • Wien frequency displacement law constant
  • Wien wavelength displacement law constant
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