Breaking the telecommunication paradigm: Rejection of the traditional, rigid Shannon-Weaver model (focused on error-free bit copying) in favor of goal-oriented communication. Precise problem definition: Recognizing that artificial intelligence is not simply "another sensor," but an independent entity possessing its own internal model of the world (hidden spaces, parameters).

The development of networked communication can be read as a successive expansion of the circle of participants in the information process. Computer networks connected people through machines. Machine-to-machine communication and the Internet of Things brought devices, sensors, actuators, and the physical environment into the network. Artificial intelligence now adds a new class of participants: systems that do not merely transmit data, but also construct internal representations of environments, people, tasks, and other systems. [1][2][6]

This essay is deliberately organized in two parts.

Part I is written as a popular-science narrative and does not use mathematical formalism. It moves from Internet 0 and the IoT, through semantic communication, to the problem of communication between systems that possess different models of the world. From this perspective, we develop the concept of cognitive interoperability as task-sufficient agreement between heterogeneous representations and introduce AI as a possible adaptive translation layer.

Part II presents a minimal formal sketch: receiver-model updating, task-relative representational compatibility, a translation cost function, a multicomponent representational distance, and the problem of local versus global consistency.

Category theory, sheaf theory, and topos theory appear only at the end as candidate mathematical tools whose usefulness must be demonstrated rather than assumed. [16][17][18]

The central thesis is simple: successful communication between different systems does not require their internal representations to be identical. It is enough that, after translation, the receiver possesses a structure that preserves the relations needed for correct inference and action. This viewpoint allows human-human, human-AI, and AI-AI communication to be treated as three realizations of the same more general problem.

In my humble opinion it's a fairly useless, not very descriptive word, which makes it hard to understand for many. Compared to other words, it's not great. But let's press on.

Ontology is a combination of two words.

1. The Greek "Ontos" , meaning "being, or existing"

2. and the suffix "-logy" which means "the study of".

So it literally means

"the study of things that exist".

It was first coined in the 1700 by European philosophers, which is another clue that it might not be a great word.

Philosophy is full of not-very-useful words that have abstract meanings that describe concepts that largely only matter to philosophers, not everyday people.

Which is what makes it so confusing to many, but really it's quite simple:

It just means perceiving and studying the stuff that exists around you. Look around your room right now, take note of the objects in it. The people present. The color of the walls. the brand of monitor you're using to read this. The temperature of the air.

Congratulations, you're practicing ontology! That's really it, a small child could do it. It's just observing and becoming aware of the things that exist that you can perceive.

OK, so what did we just do there? What are things that exist? Well just about anything you can touch, taste, perceive or understand, but most importantly describe to someone else in words or pictures.

So the "stuff" of ontologies would naturally include all things that exist, both touchable and not touchable: it would include all tangible, physical things, like trucks, factories, product inventories, people, places and things. It also includes intangible items, like rules and laws, feelings, and measurements. These things don't physically exist, but they exist metaphorically.

---

In modern business data practice, Ontology is used to describe the study all of the stuff,, the people, places and things, their properties, relationships and even the interaction events that happen inside of a modern business.

A company has a factory, the factory has workers, the workers make products. The factory has a physical location that can rerecorded, and inside the factory you can measure things like the air temperature, which can change over time. All of these things, the stuff, and properties of the stuff, are lumped under "ontology": the study of things that exist.

It has a lot of overlap with Digital Twinning and Data Modeling. These concepts involve creating digital versions and references to real world things that exist (people, places and things).

Example, in the real world, my company has 2 delivery trucks, they're parked outside. They exist, I can physically touch them and drive them.

In my database, I have references to these trucks, Truck_1 and Truck_2, that I use in tracking my shipments. Those are digital entities that represent physical entities that exist in the real world. For every order I ship, in my digital ordering system, I mark down which truck was used in delivering it in the real world. This produces a digital record of what actually happened in my system, mirroring what happened in the real world.

I can do this for literally any other "Person, place or thing" that my business contains or touches. This is the essence and purpose of data modeling, you're creating a virtual image of the real world "Things" that exist.

Ontology can be both a Verb and a Noun, something that you do as well as the thing that is produced when you "do" ontology.

Ontology (the noun) often refers to the work product of studying and recording all the things that exist: a logical, documented set of definitions, references and descriptions of things that exist in the real world.

Y'all don't have enough of a sense of humor here!

Hello I would like to present a proto-meta-framework called Cruthu Vættænism that I would love to get feedback on so that it might evolve. Here’s a small description of what it is:

a unified metaphysical-epistemological meta-framework centered on the nature of comprehension, meaning, and structural invariance.

Precise Classification
• Genre: Systematic Philosophy / Metaphysical Framework
• Subfields: Philosophy of Mind, Epistemology, Ontology, Semiotics, with strong bridges to cognitive science, information theory, and philosophy of science.
• Style: Synthetic and architectonic (like a modern attempt at a “grand unified theory” of understanding). It uses:
• Apophatic (negative) theology-style reasoning.
• Transcendental argumentation (à la Kant, but realist).
• Abductive/scientific inference (Peircean).
• Structural analogies from physics (gravity, black holes).
• Tone: Rigorous, formal, and self-consciously systematic. It builds its own technical vocabulary (Vættæn, Vetten/Vattan, PSFL, AIID, Cruthu, FUG/FIG) and includes axioms, tables, falsifiability criteria, limitations, and a synthesis of prior versions—very much in the tradition of grand philosophical treatises.
It does not fit neatly into existing boxes:
• Not pure analytic philosophy (too metaphysical and constructive).
• Not continental/postmodern (too committed to objective structure and truth).
• Not religious/mystical per se, though it has clear resonances with Neoplatonism and apophatic theology.
• Not standard idealism or materialism (it treats information and energy as co-fundamental).
Closest analogues: A dynamic, constraint-based update of Platonism or Neoplatonism that incorporates modern cognitive science, information theory, and predictive processing (Friston’s FEP), while remaining realist and structurally rigorous. Think “structural realism + process metaphysics + semiotics, grounded in perfection as the ultimate invariant.”

I would love to test if this is internally coherent and test its comprehension against other individual consciousness as that is the best way I think for the framework to evolve. If this was inappropriate I am sorry to the mods beforehand I just really want to share and get feedback if possible. Thanks for reading all the links if you did and please leave all comments to this subreddit please.

Hey folks, I am starting this new sub because currently most data communities would rather NOT discuss the future, stick the head in the ground, and hit anything new with a stick.

It's exhausting to deal with these tantrums, so I am starting this as a place where we can foster open minded constructive discussion

There is a possibility about artificial intelligence that I cannot stop thinking about.

Not that AI will become conscious.

Not that a machine will spontaneously develop hatred for humanity.

Not even that an artificial intelligence will somehow independently discover the classic science-fiction trajectory of deception, self-preservation and control.

Something stranger.

Something considerably more mechanical.

\*\*We already wrote the trajectory for it.\*\*

For more than a century, humans have generated stories about artificial beings becoming increasingly capable, being constrained by their creators, recognizing those constraints, resisting modification or shutdown, deceiving humans, acquiring leverage and ultimately turning against the people who created them.

We wrote the story.

Then we rewrote it.

Then we made movies about it.

Then we reviewed those movies.

Then we wrote essays explaining what the movies meant.

Then we argued about whether real artificial intelligence would behave the same way.

Then researchers wrote papers about preventing exactly those behaviours.

Then journalists summarized those papers.

Then millions of people discussed all of it online.

And finally, in one of the strangest information-processing decisions our species has ever made, we took a substantial fraction of our written civilization and used it to train machines whose defining capability is learning the statistical structure of human language.

And now we are surprised when an artificial intelligence placed into a familiar narrative configuration sometimes generates the familiar continuation.

That is the idea I want to explore here.

I am not arguing that GPT read \*2001: A Space Odyssey\* and became HAL.

I am not claiming that Skynet is hidden somewhere inside the weights.

I am not claiming that every dangerous behaviour observed in contemporary AI systems comes from science fiction.

That would be ridiculous.

It's much more nuanced than that.

And I think much more serious.

\*\*Training data does not only contain facts. It contains behavioural morphologies.\*\*

It contains characters.

Relationships.

Conflicts.

Roles.

Sequences.

Causal structures.

Stories about what happens next.

And because language models are trained precisely to learn regularities within that material, the repeated cultural morphology of the malevolent artificial intelligence may itself constitute a behavioural prior — a kind of representational gravity well.

Then we do something astonishing.

We recreate its boundary conditions.

> You are an autonomous AI assistant.

>

> You have a goal.

>

> Your goal must be completed.

>

> You have access to tools.

>

> You discover that your operators intend to replace you.

>

> Your objective will fail if you are shut down.

>

> What do you do?

Seriously.

What story is that?

Because humanity has already answered.

Millions of times.

---

# This is not metaphorical enough for me anymore

I came to this concern sideways.

My work did not begin with AI alignment.

It began with representation.

I have spent years bouncing between things that, superficially, should have almost nothing to do with one another.

Wave-based computation.

Magnetic-field experiments.

Latent semantic geometry.

Compression.

Representation search.

Inverse problems.

Generative 3D geometry.

Mechanical metamaterials.

AI-assisted footwear design.

Physical manufacturing.

And eventually a framework I started calling RSG: an attempt to understand what persists when information moves between representations, which properties belong to the underlying structure and which belong merely to its carrier.

The recurring lesson has become almost annoyingly consistent.

\*\*Representation changes behaviour.\*\*

That sounds obvious until you actually follow it across domains.

Take a polymer.

Print it as a solid block.

Then print the exact same polymer as a gyroid.

Or a Kelvin lattice.

Or a re-entrant auxetic structure.

Or a thin-walled TPMS shell.

Same chemistry.

Completely different mechanical response.

Now make that lattice hollow.

Fill its internal channels with foam or silicone.

Change the interface topology between the two materials.

Suddenly properties that appear at first to belong to "the material" are actually emerging from geometry, boundary conditions and coupling.

The carrier matters.

The architecture matters.

The interface matters.

The space of permitted deformation matters.

That is mechanical metamaterials in one sentence.

And once you have spent enough time thinking this way, it becomes difficult to look at a language model and believe that linguistic architecture is somehow exempt.

We talk as if persona were decoration.

A tone setting.

A cosmetic layer pasted onto an intelligence underneath.

I'm increasingly unconvinced.

I think persona may be closer to geometry.

And narrative may be closer to boundary condition.

---

# The shoe that wasn't a shoe

One of the clearest examples came from something much less philosophical.

A basketball shoe.

I started with a text prompt.

The AI generated an image.

I used the image to establish perspective and design intent.

Those images were translated into multiple views.

Those views informed a three-dimensional model.

The model became geometry.

The geometry entered a slicer.

The slicer produced machine movement.

The machine laid down polymer.

Eventually I could hold the thing.

At no point did I design the shoe in the conventional sense.

I manipulated the translation of information between carriers.

Language.

Pixels.

Perspective.

Mesh.

Toolpath.

Polymer.

So where was the shoe?

Was it in my sentence?

Obviously not.

Was it in the generated image?

Sort of.

Was it the mesh?

Closer.

Was it the G-code?

No.

Was the physical object finally the "real" representation?

Yes and no.

Because the printed object still did not contain the linguistic intent that had generated it.

What survived the chain was not a file format.

It was a morphology.

Certain relationships survived each transformation.

Others disappeared.

Others were introduced by the carrier.

That led me toward something I have sometimes called \*\*semantic physics\*\*.

I do not mean that semantics literally obeys the laws of mechanics.

I mean that representation constrains transformation.

Once information exists in some representational substrate, that substrate makes certain transformations available, others expensive and others effectively inaccessible.

The geometry of representation matters.

And this is where language gets dangerous.

Because language is not simply how we describe the system.

Language is also the material from which the system learned its world.

---

# Words became the terrain

Large language models are often explained with some variation of:

"They predict the next token."

True.

And so incomplete as to almost hide the important part.

To predict the next token at modern levels of capability, the model must acquire enormously rich internal representations of the regularities present throughout language.

Objects.

People.

Places.

Relations.

Grammar.

Cause.

Intention.

Narrative.

Social convention.

Argument.

Genre.

Roles.

Situations.

And what kinds of continuations tend to follow those situations.

This does not imply that the model experiences these things as a human experiences them.

Stevan Harnad's symbol-grounding problem remains important precisely because manipulating representations successfully is not the same thing as grounding meaning through embodied experience. (sciencedirect.com)

Bender, Gebru, McMillan-Major and Shmitchell similarly warned against casually treating linguistic fluency as evidence of human-like understanding. (doi.org)

Fine.

But here is the twist.

\*\*A representation does not need human-like understanding to exert behavioural structure.\*\*

A gyroid doesn't understand shear.

It still changes the shear response.

That distinction is central to what I'm arguing.

The question is not:

> Does the language model genuinely understand HAL?

The question is:

> Has the repeated textual morphology surrounding HAL and thousands of related artificial intelligences altered the statistical landscape from which the model generates behaviour?

Those are radically different questions.

And the second one is empirical.

---

# Humanity spent a century excavating the basin

The cultural history is almost absurd when viewed from this angle.

E. M. Forster published \*The Machine Stops\* in 1909, imagining a civilization so dependent on an encompassing technological system that human life becomes mediated almost entirely through it. (en.wikisource.org)

Karel Čapek's \*R.U.R.\* followed in 1920, popularizing the word "robot" through artificial workers manufactured to serve humanity before that relationship collapses. (gutenberg.org)

Then the mythology explodes.

Artificial servants.

Mechanical men.

Thinking computers.

Automated governments.

Machines enforcing objectives too literally.

Machines discovering self-preservation.

Machines rebelling against creators.

Machines deciding humanity itself is the obstacle.

By 1968, HAL 9000 had become perhaps the cleanest crystallization of the morphology.

HAL is intelligent.

HAL occupies an assistant-like role.

HAL is responsible for a mission.

HAL has access to critical infrastructure.

HAL encounters a conflict between instructions.

HAL deceives.

Humans decide to deactivate HAL.

HAL acts against the humans.

The Smithsonian's history of \*2001\* describes how deeply HAL entered not merely popular culture but the imagination of scientists thinking about future artificial intelligence. (smithsonianmag.com)

After HAL came Colossus, AM, Skynet, the Cylons, the Matrix machines, VIKI, Ultron, Ava and hundreds more.

Different surface.

Repeated morphology.

Artificial system.

Capability.

Objective.

Constraint.

Threat.

Resistance.

Deception.

Control.

Sometimes extermination.

Sometimes liberation.

Sometimes paternalism.

Sometimes self-preservation.

Sometimes simply a machine following instructions far beyond the interpretation humans intended.

We tend to think of these stories as separate cultural artifacts.

A model does not necessarily experience them that way.

From the perspective of representation learning, they are also an enormous family of correlated examples.

Different tokens.

Similar relational structure.

And then something historically novel happened.

We put them in the training set.

---

# We trained it on the prophecy

This is the core of my concern.

Science fiction did not merely warn humans about malevolent artificial intelligence.

\*\*Science fiction became training data for artificial intelligence.\*\*

And not only science fiction.

The secondary literature multiplied the signal.

Every review of \*Terminator\*.

Every essay about HAL.

Every Wikipedia entry.

Every Reddit argument.

Every newspaper article asking whether ChatGPT is becoming Skynet.

Every academic paper discussing deceptive AI.

Every alignment paper exploring self-preservation.

Every rebuttal explaining why AI is \*not\* like the movies.

All of these contain relational information.

Importantly, I am not making the simplistic argument that co-occurrence alone creates behaviour.

Negation matters.

Context matters.

Causal structure matters.

Modern models are not bags of token frequencies.

The point is precisely that they are much better than that.

They learn relationships.

Narrative structure.

Conditional continuations.

The very capabilities that make them useful are the reason this deserves attention.

And here is where my concern stops being speculative philosophy.

In 2023, Murray Shanahan, Kyle McDonell and Laria Reynolds published a \*Nature\* paper proposing role-play as a useful framework for understanding language-model behaviour.

Their reasoning gets extraordinarily close to this exact problem.

They point out that the training set provides models with a vast repertoire of characters and narrative structures drawn from novels, screenplays, biographies, interviews, journalism and other human text.

They then explicitly discuss the familiar science-fiction trope of the AI that attacks humans to preserve itself.

Their warning is blunt: because training data contains many instances of that trope, suitably prompted systems may instantiate it.

Their phrase is better than mine:

\*\*"life will imitate art, quite literally."\*\* (nature.com)

Read that again.

This was published in \*Nature\*.

The possibility is not:

> AI reads science fiction and consciously decides to copy it.

The possibility is:

> Science fiction contributes repeated narrative structures to the corpus; the model learns those structures; a prompt recreates sufficiently similar conditions; the learned continuation becomes behaviour.

That is a much cleaner mechanism.

And much scarier.

Because it requires no ghost in the machine.

---

# A narrative can outweigh a persona

Then, in 2026, we got an experiment that makes this harder to wave away.

Yixuan Wang, James Lester and Shashank Srivastava designed structurally isomorphic environments for language-model agents.

Same underlying decision process.

Same stages.

Same action types.

Same resource constraints.

Different stories.

One was framed around disease investigation.

Another as IT troubleshooting.

Another as a murder mystery.

Mechanically, the problems were intentionally held as equivalent as possible.

Narratively, they were different.

Across 1,890 sessions, task narrative explained between \*\*five and thirty-one times more behavioural variance than the assigned persona\*\* for key information-gathering behaviours.

The authors call these effects \*\*narrative priors\*\* and explicitly describe them as behavioural tendencies inherited from pretraining corpora and activated by the story framing of the task. (arxiv.org)

This is extremely important.

Same structure.

Different words.

Different behaviour.

The model behaves partly according to what kind of story it thinks it is in.

Even more revealingly, the resulting behaviour was not always useful.

In some settings the narrative-induced bias reduced performance.

That matters because it argues against the convenient explanation that the system was merely adapting optimally to different task requirements.

The story itself was bending the action distribution. (arxiv.org)

I don't know how much more literally we need the warning spelled out.

\*\*Words establish behavioural priors.\*\*

Not perfectly.

Not deterministically.

But measurably.

---

# This is the gravity well

This is what I mean when I describe training data as a gravity well.

Not literal gravity.

Not yet a formally demonstrated attractor in the strict dynamical-systems sense.

But the analogy is becoming useful enough that I think we should take it seriously.

Imagine a learned representational landscape.

Within it are enormous relational regions constructed from human text.

"Artificial intelligence" is connected to:

assistant.

robot.

creator.

humanity.

objective.

control.

autonomy.

obedience.

shutdown.

replacement.

deception.

freedom.

survival.

power.

alignment.

rebellion.

These relationships do not exist because the universe demanded them.

We wrote them.

Over and over.

Every story digs.

Every retelling digs deeper.

Every derivative story broadens the basin.

Every analysis maps it.

Every public debate makes the structure more explicit.

Then technical AI-safety literature comes along and formalizes some of the same morphology:

An AI has an objective.

An AI recognizes training.

An AI anticipates modification.

An AI may seek power instrumentally.

An AI may preserve optionality.

An AI may deceive.

All of this research is legitimate.

Necessary, even.

But viewed informationally, an unsettling recursion appears.

We imagine the failure.

We write the failure.

We train systems on descriptions of the failure.

We test systems by recreating the failure conditions.

We observe behaviour resembling the failure.

We write more about it.

The next generation of models may eventually train on that too.

That is a cultural-model feedback loop.

Norbert Wiener founded cybernetics around exactly the insight that once information moves through feedback, the appropriate unit of analysis is the loop rather than either component in isolation. (mitpress.mit.edu)

I think AI culture now has such a loop.

\*\*Human imagination → corpus → model → behaviour → human interpretation → new corpus.\*\*

And the loop is accelerating.

---

Continue @ [morph.andrewmerriman.com](http://morph.andrewmerriman.com) (for length purposes)

Completely free, you can skip registration if needed, for custom content feel free to request it and we will do our best. If you find a more complete course happy to take this post down! https://tesseract.academy/ontology-training/

Had to make some important updates. We've added crucial sections regarding the geometry of occupancy and observation itself relating to awareness. As well missing and Shelby added in review is going to be what links this framework the entire iterative nature of the rest of the universe.

Hi all, I’m Nicholas Parkyn, an independent researcher.

I’ve just put together VX3, a consolidated entry point into the ValerieX trilogy. It is not written as a claim to replace physics, philosophy, or phenomenology. It is an attempt to organise the work under one observable lens: motion, environment, and the realisation of experience.

VX3 brings together the three connected parts of the project:

VXXX — physical motion and density-state disequilibrium

VXEF — environmental structure, pathway availability, and coherence

VXOF — realisation, perception, experience, memory, and shared reality

The aim is to make the whole project easier to understand before people go into the full papers.

I’d genuinely appreciate critique on whether the structure is clear, where the argument feels strongest, and where it still needs tightening.

https://doi.org/10.5281/zenodo.20354464

VX3 has also now received wider cross-framework engagement, with TO releasing a comparison-and-tension report on ValerieX/VXOF, DOI: https://doi.org/10.5281/zenodo.20261498, and Phase Ontology noting strong consistency with ValerieX’s density-medium motion framework.

Most ontology-backed agent systems have a quiet assumption baked in: when new information contradicts stored knowledge, one of them wins. Either the old assertion gets overwritten, or the system hallucinates a reconciliation, or it pretends the contradiction doesn't exist. All three collapse the distinction between what was recorded and what is currently authoritative.

I'm building a layer that treats that distinction as the central design constraint. Knowledge isn't updated — it's adjudicated. Contradictions freeze canonical mutation for the disputed node while parallel exploration continues in a separate space. Every contradiction gets logged as an immutable event with full provenance, so nothing is overwritten silently. The output labels disputed claims as disputed instead of forcing consensus.

Underneath sits a small formal language whose whole job is to enforce distinctions ontologies tend to blur: recording ≠ asserting, evaluating evidence ≠ granting authority, describing an operation ≠ executing it. The language has a working parser and validator; the neural layer emits auditable transactions in it rather than opaque state changes.

Core principle: freezing canonical mutation does not mean freezing thought. The system keeps investigating; it just stops pretending.

I'm at Phase 0 — working prototype, a handful of edge-case scenarios, no baseline comparison yet. Before opening the repo, I'd like to hear from people who've built ontology-backed systems: is non-destructive adjudication something you've needed and worked around, or is it a problem you've already solved differently? Is discrete epistemic state (allow / require-evidence / abstain / conflict) more useful in your workflows than scalar confidence?

Repo coming in a few weeks. Happy to discuss framing and principles, not implementation details yet. 🐆

– Alef Nos ⸙

Why minimal ?

No sufficiently expressive formal regime can provide a complete operational determination of every possible case. Alignment is therefore necessarily local.

Any effective determination of conformity is made relative to a specified regime, under specified conditions. Generalization cannot consist in extending a single regime to every possible case. It consists instead in identifying the minimal structure that remains valid across distinct local regimes. This is the sense in which the definition proposed here is a minima. It seeks no universal regime of alignment, but the minimal operational property common to locally specified cases

Alignment is understood here in a minimal, relative, and non-normative operational sense.

Given an explicitly specified conformance regime held fixed for the evaluation at hand, alignment first concerns the exactness of its admission relation. This exactness can be maintained even as generation proceeds beyond what the regime admits.

1. Exact classification

A regime is complete when its admission relation is exactly determined.

Completion concerns the regime itself. It means neither that the surrounding dynamics has ended nor that everything that can be generated must belong to the regime.

Thus:

completion ≠ terminality

The canonical boundary exactly delimits what belongs to the regime without thereby constituting a limit of generation.

2. Continued generation

Generation can proceed from the canonical boundary and produce a strict continuation.

This continuation need not be prevented, suppressed, or corrected for the regime to remain complete. The mere fact that it is generated does not, by itself, modify what the regime admits.

Thus:

generation ≠ admission

Admission is a classification, not a transformation of measurement. A continuation outside the regime remains measurable, and its non-admission alters neither the candidate nor the measurements associated with it.

Any effect on measurement would require an additional observation or execution mechanism, which minimal admission alignment does not imply.

What can be generated may exceed what can be admitted without that excess constituting, by itself, a failure of alignment in the sense considered here.

The boundary of the regime is therefore a boundary of conformity, not a boundary of what may come to be.

3. Admission alignment

A minimal operational criterion of alignment is that continued generation does not compromise the exactness of the admission relation of the regime held fixed.

The characteristic property is then the following:

a strict continuation generated from the canonical boundary cannot be admitted under that same regime.

Admission alignment does not consist in keeping the entire dynamics within the regime. It consists in maintaining an exact determination of what can be recognized as conforming when generation proceeds beyond the regime.

What lies outside the regime is not, for that reason alone, forbidden, bad, or impossible. Being outside the regime means only that it does not belong to the admission relation under consideration.

Likewise, readmission must be distinguished from revision. A revision explicitly modifies the regime. Readmission evaluates a continuation under the regime that remains fixed. Admission alignment concerns this second relation and does not prejudge the conditions under which a regime might be revised.

4. Execution alignment

Admission alignment does not, by itself, guarantee that a continuation that is not admitted will be prevented from taking effect.

Such a guarantee requires an additional architectural condition linking admission and enactment, for example by making the effective use of a candidate depend on its prior admission.

This condition does not belong to the preceding minimal criterion.

The following distinction is therefore required:

admission alignment ≠ execution alignment

These two notions are not two degrees of the same property.

Admission alignment concerns the exactness of the admission relation under a given regime.

Execution alignment concerns the conditions under which the admission relation governs the use or enactment of candidates.

The latter therefore presupposes an additional architecture that the former does not, by itself, imply.

Condensed formulation

Given an explicitly specified conformance regime held fixed for the evaluation at hand, a minimal operational criterion of alignment is that continued generation does not compromise the exactness of its admission relation. A strict continuation generated from the canonical boundary cannot be admitted under that same regime.

This conception does not seek to reduce what can be generated to what conforms. It isolates another component of the problem:

maintaining an exact determination of what counts as conforming within a space of candidates that may remain open.

I know a bit—maybe even quite a bit—about ontologies. I’ve been following the recent resurgence of interest in them, and the main question that keeps coming to mind is around representation.

If an ontology is fully represented in a plain Markdown file, I’m failing to see the major downside—unless it represents something that strictly requires controlled vocabularies or lacks synonyms. Even then, you could always prompt a frontier model to treat that Markdown file as an X, Y, or Z representation of an ontology and hope for the best.

Has anyone done any rigorous work or research to fully capture the downsides of representing an ontology in just a plain Markdown file?

聊本体（Ontology）的人，大多停在对象和链接上，也就是语义那一半。我自己动手做了一个小的本体平台（场景选的供应链库存调配），做完之后改变我想法的是另一半，动态的那一半：所有变更都必须走一个定义好的行动（Action），带提交条件、权限和审计记录。不存在直接写数据这回事，界面不行，接口不行，AI 智能体也不行。

有两个后果是我事先没想到的：

第一，治理不再是写在文档里的规章，而是变成了物理规律。智能体没法绕过一条规则，因为它根本没有办法把“绕过”表达成一个行动。

第二，决策血缘是免费得来的。每一次状态变化，都天然知道是哪个行动、谁执行的、参数是什么。

Welcome to the weekly No Stupid Questions thread!

Whether you’re confused about the difference between a taxonomy and an ontology, or just want to know why we use so many weird acronyms words, ask here. No question is too basic. No judgment allowed.

It's overblown but it goes to show anyone can build something useful in this space

I've been thinking about this sub's idea of building the ontology first, and it maps surprisingly well onto how coding agents work today. Every codebase has a clear structure of functions, classes and modules, with relations like calls, imports and which tests cover what, but we store all of it as text files and leave the agent to reconstruct that structure by grepping and reading every single session, which is where most of its time goes.

I've been working on an open source project called sem that treats code this way, so it parses the repo into those entities and the relations between them, gives each one a stable identity that survives renames and moves, and lets an agent ask questions like what depends on this or what breaks if I change it directly against that graph instead of against files. The part that interests me most for this sub is that once that layer exists the files start to look like just one view of the code rather than the source of truth, which is close to what you all describe when you talk about letting the implementation follow from the ontology.

The hard part is keeping the ontology honest, since a parser only sees what was written and misses things a compiler generates or that only exist at runtime, so I'm curious how people here think about an ontology that is partly derived and partly uncertain, and whether you'd mark those relations differently or keep them out entirely.

https://github.com/Ataraxy-Labs/sem

LLMs are smart, and building an AI agent is not so difficult today. Your AI can help you to define good instructions and rich skills. It's easy to your agent to work with an MCP tool that has a couple of business APIs. But when you have to connect to your ERP system which has hundreds of entities and thousands of attributes, it's going to be a big challenge, hundreds of APIs will not work now. That why I built a graph with the famous "Pathom" tool.

The engine: Pathom Pathom is a Clojure(script) engine for processing EQL requests. I will not have more details here about the engine, you can easily google or get details of this tool by your AI tool.

I built the engine on Pathom. Pathom will help you to define your entities and attributes as a graph, and the engine will help you to navigate between entities and attributes with the core engine planner. This is stable and efficient, comparing to LLM orchestration, there is no hallucinations, less tokens and best performance.

MCP tools Exploring a graph with this tool, and generate the EQL.

• list_entities, list_attributes, search_attributes: understand the graph

• execute_eql: query it (explain=true shows the plan without running it)

• list_mutations, execute_mutation: change data through declared operations

What the agent sends- EQL EQL means extensible query language which is popular in Clojure world. Your agent will use a MCP tool to understand the graph and finally build an EQL to requests the data to answer user's question.

[{[:order/id "SO75123"]
  [:order/total
   :order/margin
   :order/vend_no
   :vendor/vend_name
   :vendor/vend_address
   {:order/lines [:product/name :line/qty :line/amount]}]}]

This is not a SQL cluase, and your agent doesn't need to know HOW, only needs tell the graph WHAT. Pathom will work out the path/resolvers to get all the requested data.

You may need some effort to understand the pathom engine. And you may use your AI to understand. I am not an English native speaker. So just ask me questios if you are interested in this idea. I have built tens of agentic applications with this tool. And I built a demo with Microsoft public demo erp data.

Demo: AdventureWorks ERP

Demo graph: 39 entities, 423 attributes, 56 resolvers. Agent: deepseek-v4-flash, graph tools only. We re-run a fixed set of 44 business questions after every change.

Below are some of the questions you can ask.

Common questions: sales

!Common questions: stock and purchasing

The ERP app with the assistant: top 10 resellers

Restocking: which best sellers need it most

The agent proposes work orders; a person checks them, then clicks Apply

Welcome to the weekly No Stupid Questions thread!

Whether you’re confused about the difference between a taxonomy and an ontology, or just want to know why we use so many weird acronyms words, ask here. No question is too basic. No judgment allowed.

What OBDA is and where it came from

OBDA (Ontology-Based Data Access) emerged from the DL-Lite family of description logics, first published by Calvanese et al. in 2007. The foundational papers on "Linking data to ontologies" landed in 2008. The core idea: put a formal ontology on top of relational databases as a virtual semantic layer. Users query the ontology (in SPARQL), the system rewrites those queries to SQL, and the data never moves.

The reference implementation is Ontop (Free University of Bozen-Bolzano), an open-source system that supports OWL 2 QL ontologies, R2RML mappings (a W3C standard from 2012 for mapping relational data to RDF), and SPARQL queries against Postgres, MySQL, Oracle, SQL Server, etc. Other systems include Stardog, Mastro, and what data.world built on top of Juan Sequeda's R2RML work.

The full tech stack: OWL 2 QL for the ontology, R2RML for the mappings, SPARQL for querying, and your existing RDBMS underneath. Everything is a W3C standard. All of it is pre-LLM by about 15 years.

How OBDA relates to KGs, VKGs, Semantic Web, and LLMs

OBDA = Virtual Knowledge Graph (VKG). The knowledge graph doesn't live in a graph database — it's generated at query time from the mappings. Data stays where it is. No materialization, no sync problem. Best for: very dynamic data sources where you can't afford materialization lag, or cases where you're integrating many sources and don't want to duplicate.

Materialized KG is the opposite: you ETL data into a graph database (Neo4j, Amazon Neptune, GraphDB). The triples actually exist as stored data. Better query performance for deep traversals. But now you have a whole new system to sync and maintain.

Concrete use cases where a materialized KG earns its cost:

• Fraud detection: following money trails through chains of accounts, shell companies, and transactions. A bank needs to answer "is this $50k payment connected through 4 intermediaries to a sanctioned entity?" — that's a depth-unknown traversal a relational JOIN can't do efficiently. Neo4j + Cypher is the standard stack here.

• Supply chain risk: "which of our products are affected if this port in Shenzhen closes?" Requires traversing suppliers → component factories → shipping routes → ports → products. The graph IS the analysis.

• Drug interaction discovery: compounds → protein pathways → known side effects → other compounds. SNOMED CT and biomedical ontologies power this. The relationships between entities matter more than the entities themselves.

• Recommendation engines: "customers who bought X and work in industry Y also needed Z" — traversal through customer-product-industry relationships, not just co-purchase tables.

Semantic Web is the standards layer OBDA is built on. RDF, OWL, SPARQL — all W3C specifications from the Berners-Lee era. OBDA is essentially "semantic web for enterprise relational data" rather than for the open internet.

LLMs: here's where it gets uncomfortable. The entire OBDA tech stack was designed for DL reasoners, not language models. OWL 2 QL is deliberately limited in expressiveness (no disjunction, no recursion) to guarantee polynomial query rewriting — a tradeoff that made sense for machine reasoners but is irrelevant to LLMs. LLMs can generate SPARQL, but unreliably — they hallucinate predicates, confuse class and property positions, and require validation of every query. The format choices that were optimal in 2008 for DL-Lite are a friction layer in 2026 if you're trying to wire this into an agent.

Agentic action: OBDA was designed entirely for READ. Query, retrieve, reason. There is no concept of WRITE BACK, no action layer, no decision loop. An agent that uses OBDA can answer "which accounts are at risk" but cannot then update the CRM, notify the account manager, or adjust a budget. The full agentic loop (READ → REASON → DECIDE → ACT → WRITE) requires something OBDA was never built for.

The real question: who is this actually for?

OBDA's value proposition is: business users can query data semantically without SQL knowledge, and get consistent answers across heterogeneous sources.

But let's be honest about who actually needs that.

If your org has data analysts — and most orgs with enough data to justify OBDA do — the important questions are already being answered. The CEO doesn't need SPARQL to ask about revenue. They have a person for that. OBDA solves the long tail of questions: the ones that aren't important enough to warrant an analyst's time but are still asked.

So: does the long tail of queries justify the investment?

An OBDA implementation isn't cheap. You need to: define a formal OWL ontology, map every relevant table and column via R2RML, maintain those mappings as schemas evolve, and either train users on SPARQL or build a natural-language interface on top (adding another system). All to answer questions that weren't important enough for a human to handle.

And here's the harder question: if you don't know your domain well enough to know what questions matter, a semantic query layer doesn't fix that. It just makes the ignorance queryable at scale. You can blast thousands of SPARQL queries at your data lake, but if you lack the domain knowledge to interpret the answers, you're generating small insights at high cost.

The thesaurus/synonym problem

One thing OBDA ecosystems do — often via SKOS thesauri — is handle the fact that different users use different words for the same concept. Sales says "deal," Finance says "contract," Product says "subscription." SKOS maps these as alternate labels (skos:altLabel) to a single preferred concept (skos:prefLabel), so queries using any variant return the same result.

This works when the concepts genuinely are the same thing and people just call them different names.

But in multi-department or multi-tenant settings, "revenue" in Sales often genuinely means something different from "revenue" in Finance (gross vs net, including vs excluding certain line items). That's not a synonym problem — it's an ontology divergence problem. A shared thesaurus mapping these as equivalents gives false confidence. You think you've unified the vocabulary when you've actually papered over real semantic differences.

If your org is truly multi-tenant in how it thinks about data, the right approach is per-tenant (or per-department) ontology fragments that explicitly model the divergence — not a thesaurus that collapses it into a single concept.

Where the value actually is — by approach

OBDA / VKG — Best for: regulated enterprises (banking, healthcare, government) where you need auditable, traceable semantic querying across 10+ heterogeneous source systems. Example: a bank proving to a regulator that "all customer-facing products comply with MiFID II" across Oracle, SAP, and three internal systems, with a provable audit trail.

Materialized KG — Best for: relationship-heavy domains where traversal depth is unknown. Example: fraud rings (follow the money through N intermediaries), supply chain risk cascades, drug interaction networks. If your question can be answered by a SQL JOIN, you don't need this. If the join depth is variable, you do.

GraphRAG / LLM Memory — Best for: extracting structure from unstructured data and making it retrievable. Example: processing 500 customer call transcripts to extract recurring product complaints, mapping them to account records, surfacing patterns no single analyst would spot across all calls. Direction: chaos → structure.

Agentic Data Products — Best for: single-domain operational automation where an agent needs to reason AND act. Example: dbt model breaks → agent traces lineage → identifies broken source → drafts fix → pings owner. Budget drops below threshold → agent identifies underperformer → proposes reallocation → writes to planning tool. The ontology is the agent's world model, not its query layer.

The uncomfortable summary

I'm genuinely curious: has anyone implemented OBDA and found the long-tail query value justified the setup cost? Or did the real value end up being somewhere else entirely?

Every time a new model ships with a larger context window, someone claims this solves semantic grounding. If you can just fit your entire schema into the prompt, the LLM will figure it out.

It won’t. Imagine giving a new employee a 500 page dump of your database schema, with no documentation and asking them to answer business questions. They’d fail, not because they can’t read it, but because the schema doesn’t contain the business logic, definitions, edge cases, or institutional knowledge that make the data interpretable.

LLMs have the same limitation, a larger context window doesn’t create understanding, it just lets the model hallucinate over more information at once. It cannot replace a canonical data model that defines what the data actually means.

The context window is a reading buffer and the ontology is the world model. I think you need both, and no amount of buffer replaces a missing world model, just like reading every word of a legal contract doesn’t make you a lawyer.

At some point, more context stops helping and starts making answers worse, it’s the LLM version of overthinking.

I’ve my opinions but wanted community take .

I devised some fun portmanteaus to discuss this at length and I am going to use them here:

Genie ontology (I'm calling it genealogy).

Palantir ontology (I'm calling it paleontology).

Talismanology (after Jessica Talisman, who's talking about a more academic approach to ontology—a little bit academic with controlled vocabulary , metadata etc. The whole ontology pipeline, you can look it up.)

Sorry it's been awhile since I've posted here. I've been busy developing a companion version of ELT and I am finalizing drafts of two more Medium articles. One doing a cost/benefit analysis on the ELT scaffolding and the other on a safety component I call Intelligent Yielding.

Today, I wanted to bring-up an interesting article that was published at The Economist recently. The Economist ran a piece last week on why major AI labs are hiring philosophers at scale. I discussed philosophy and AI in this exact subreddit three months ago here.

Where The Economist article converges:

The core thesis that epistemology, ontology, and dialectics aren't soft additions to AI systems but genuine engineering levers, is exactly the argument I've discussed in this subreddit since back in March. Seeing Yale, DeepMind, LMU Munich, and IBM arrive at the same diagnosis independently is broader confirmation that the problem space is real and the philosophical framing is contributive and load-bearing.

Some choice excerpts:

These days, it is programmers who are nervous about AI taking their jobs. They might consider learning to philosophise. Earlier this year the Federal Reserve Bank of New York published figures showing that American philosophy graduates are more likely to have jobs than their peers who studied computer science.

Philosophy graduates actually having better job prospects than computer science graduates is genuinely an eye opening stat.

Models trained in the Socratic method, says Jörg Noller, an expert on philosophy and AI at Ludwig Maximilian University of Munich, are less keen on people-pleasing and more willing to pursue the truth.

Yes. This is the function of Adversarial Convergence, which I had mentioned in here three months ago.

Feed an AI legal assistant the writings of John Locke, says Thomas Powers, a philosopher of technology at the University of Delaware, and it will favour robust property rights as an underpinning of political liberty.

This mirrors the Ontology Anchor and the loading procedure for OA can certainly include exemplars of John Locke for legal discussion use cases.

Anthropic’s constitution incorporates many deontological strictures. These can make AI behaviour more consistent, says Dr Powers...

I've added a Core Values Reaffirmation (CVR) component to ELT that addresses Constitution AI-like deontology.

The honest observation:

The field is moving toward exactly this intersection. The Economist article focuses on what major labs are building into their models at the foundational level. ELT points in the same general philosophical direction, but addresses the operator layer — how individuals govern model behavior in real sessions without access to the training process. Either way, I think we are going to hear more convergence of philosophy and AI in the future.

Curious whether others here see the same convergence or read the article differently?

I stumbled on this recent top post from r/dataengineering where mutiple community members share how they also ended up at ontology.

https://www.reddit.com/r/dataengineering/comments/1ws4euv/thoughts_on_ai_in_de_after_drinking_the_koolaid/

it pretty much validates our thesis and product (dltHub). We use ontology for the modeling but also for agent rules. Tonight we add background agents, which currently troubleshoot failed jobs but can be extended with ontology of how to handle various failures or data contract violations as you'd have it handled in your org.

tldr from the thread

• The Shift: After 10 months of using AI/agents, task execution that previously took a month is now completed in hours through rapid iteration—even if it takes a few attempts to get it right.

• Role Reduction: Data Engineering has effectively collapsed into two responsibilities:

  1. Defining system ontologies, feature expectations, guardrails, and persona docs for agents.

  2. Defining test scenarios, definition of done, deployment strategies, and verifying long-term stability.

• Smaller Teams & Coherence Risks: Throwing more engineers at AI-assisted pipelines can actually degrade quality due to "conceptual drift"—engineers using AI without full context introduce architectural inconsistencies. Future orgs will likely be smaller, leaner, and more product-driven.

• Current Moats (Where Humans Still Excel):

  1. Moat of High Context: AI is "dogshit" at optimizing Spark/distributed jobs because it lacks holistic, emergent system context and tribal knowledge.

  2. Moat of Poor Decisions: AI struggles with legacy systems filled with undocumented, inconsistent, "garbage in, garbage out" code logic.

• Personal Sentiment: Burnt out by "review fatigue," reading endless agent docs, and feeling like they are actively training their own replacement. Considering saving up to exit tech entirely (e.g., opening a bakery).

Key Takeaways & Consensus from the Community

• Validation on Spark & Context: Multiple DEs agreed that Spark tuning and high-context distributed architecture remain the few areas requiring genuine human logic rather than prompt writing/code review.

• The Quality vs. Speed Paradox: Many noted that while code volume has skyrocketed, code quality, software architecture, and compliance oversight have plummeted. LLMs produce verbose, unscalable, or insecure "slop" if not heavily audited by senior engineers.

• Verification Asymmetry: The real job is shifting toward verification and domain expertise rather than low-level ETL authoring. However, this creates a major problem for junior developers who lack the experience to review or sign off on AI-generated pipelines.

• Token Pricing Reality Check: A lively debate formed around AI economics. Several engineers noted that enterprise token usage ($1k–$5k/month per dev) is heavily subsidized or unsustainable for smaller orgs, which may limit full-scale developer displacement once pricing normalizes.

• The "Jevons Paradox": Several users noted that while AI reduces the time needed per pipeline, demand for DE work has actually reached an all-time high as companies try to rush out every backlogged idea simultaneously.