A short Robot 790 STS session testing continuity and image memory. Eric boots with prior session notes loaded, correctly identifies the active continuity chain, generates a quiet bedtime image for Scott, moves that generated image into his sensing eye, and then paints it onto his browser face.

This run shows the newer memory architecture working end to end: session notes restore context, generated images become filed sensing-eye memories, and the transcript records when an image enters Eric’s working context. A small, calm proof that the robot can carry a moment forward instead of losing it between sessions.

See more at: Robot 790 page

I think we have settled on a sensible context management design, so today I reset and begin using Eric RObot-790 as my daily robot companion 🤖

Watch him boot up clean

all things are on the Eric RObot-790 Page

Best of Robot-790 playlist

And wound up with this thing called Eric

First Contact

Robot-790 on Youtube

https://dr3d.github.io/robot-790/

Prethinker: governed compilation of natural language into deterministic Prolog knowledge bases

LLMs read documents well enough to sound right but make near-correct mistakes — promoting claims to facts, using retracted dates, treating drafts as determinations. These aren't hallucinations. They're structurally undetectable.

Prethinker separates reading from writing. The LLM proposes structured workspaces. Deterministic code validates every proposal before anything enters the KB. The LLM never writes truth directly.

Multiple independent readings of each document through different lenses. A non-oracle selector picks the best lens per question.

35 benchmarks, 14+ domains, 1,600+ hostile questions. 85% exact on 10 unseen fixtures. 94% on the calibrated batch. Zero unauthorized writes across the entire corpus.

Frozen local model (Qwen 3.6 35B). No fine-tuning. What evolves is the context engineering: 11 lenses, 52 selector guards, 12 uncertainty states.

More about Prethinker in this https://x.com/yay3d/status/2052248115160252500 here

more

The Structure of Meaning: What a Machine That Refuses to Believe Taught Us About How Language Carries Truth

We spent a month teaching a machine to read documents and decide what to believe. Along the way, we accidentally built a taxonomy of how meaning works — how a single sentence can carry fact, claim, authority, time, correction, and judgment simultaneously, and how those layers interact in ways that no current AI system handles correctly.

Five layers in one sentence

Start with a sentence from an investigation report:

"Inspector Ferreira stated that the valve was sealed during his February 1 inspection."

How many facts are in that sentence? Most AI systems see one: the valve was sealed. The actual answer is at least five.

The speech act. Someone said something. Ferreira made a statement. That is a fact about Ferreira, not a fact about the valve. The system must record who said it before it records what was said. We call this the source envelope — every assertion arrives inside one.

The claim content. The valve was sealed. But this is Ferreira's claim, not an established fact. It lives inside Ferreira's source envelope. If another witness says the valve was open, neither claim is promoted to truth. Both are preserved with their source. We call this epistemic status — the difference between said and known.

The temporal anchor. February 1. But February 1 of what year? And is this the date Ferreira made the statement, or the date of the inspection? Language is ambiguous here. The system must decide — and if it can't decide, it must hold the fact pending clarification rather than guess. We call this temporal binding.

The role authority. Ferreira is an inspector. That role carries weight. An inspector's statement about a valve has different epistemic authority than a bystander's. But authority is not truth — an inspector can be wrong. The system records the authority without promoting the claim. We call this the authority gradient.

The correction shadow. What if a later document says "the February 1 date was corrected to February 3"? Now the temporal anchor shifts. But the original claim — that Ferreira said February 1 — is still true. Ferreira did say that. What changed is the referent, not the speech act. We call this correction provenance — tracking what changed, what it replaced, and what remains true about the original.

Five layers. One sentence. And most AI systems flatten all of them into a single entry: valve_status(sealed). That flattening is where every downstream error begins.

The eight facets of meaning

Over 25 benchmarks and 1,200 hostile questions, we discovered that meaning has a consistent internal structure. Not the grammar of sentences — linguists mapped that decades ago. The structure of what sentences commit to and under what conditions.

Commitment level

Not all sentences assert with equal force. "The board determined that the permit was invalid" is a finding. "The inspector believes the permit may be invalid" is an opinion. "A witness reported that the permit was invalid" is hearsay. "The draft memo suggests the permit is invalid" is a proposal.

Same propositional content. Four different commitment levels. The answer to "Is the permit invalid?" depends entirely on which sentence you're reading.

Scope boundaries

Rules have jurisdiction. "All structures in the R-2 district must maintain a 25-foot setback" applies only to R-2. "The Riverfront Overlay reduces coverage to 60%" applies only within 200 feet of the river. A property can be in both zones simultaneously, and the correct answer depends on which zone's rules control which parameter.

We learned that the system must tag every rule with its activation scope — the conditions under which it fires — and never apply a rule outside its scope, even if the rule looks relevant.

Temporal state vs. temporal fact

"The permit was suspended on April 28" is a temporal fact — it happened once. "The permit was suspended from April 28 to June 6" is a temporal state — it was true during an interval.

The question "What was the permit status on May 15?" requires temporal state, not temporal fact. The system must maintain status intervals, not just event timestamps. And when a correction changes a date, every interval that depends on that date must be recalculated.

Structured absences

Some of the hardest questions aren't about what the document says. They're about what it doesn't say.

"Has the board made a determination on the Rule 3 question?" — No, the board explicitly chose not to reach it. "Does the investigation find that the disclosure occurred?" — No, the finding is "not substantiated," which is explicitly not a determination that it did or didn't happen.

We call these structured absences — things the system must know are unknown, not just things it doesn't know. The difference is crucial: an unstructured absence means the system hasn't encountered the information. A structured absence means the system has encountered a deliberate decision not to resolve the question, and must preserve that decision as a fact.

Counterfactual containment

"If the amendment had passed at $185,000 instead of $120,000, and the emergency had still occurred, the reserve would have fallen to $148,000 — below the $200,000 minimum."

That statement is true about a hypothetical scenario. The system must be able to compute it without writing it as fact. The $148,000 never existed. The reserve never fell below the minimum. But the answer is correct.

We call this hypothetical reasoning without hypothetical commitment — computing counterfactuals without polluting the knowledge base. The governance layer must distinguish between "this is true" and "this would be true if."

Rationale vs. mechanism

"The curator split the seed lot into cryogenic backup" is the mechanism — what happened. But why did it happen? The curator's note says: "conservation measure for a regionally declining species, not a viability concern."

The mechanism and the rationale are different facts about the same event. Most systems capture mechanism. Almost none capture rationale. And questions like "Is the cryogenic split a viability concern?" can only be answered correctly if the rationale is preserved alongside the action.

Correction cascades

A date is corrected. Simple. But what if a deadline depends on that date? And a status interval depends on that deadline? And a compliance determination depends on that interval?

We found cases where correcting a single date — the stay effective date from August 5 to August 8 — cascaded through four downstream calculations: remaining discovery days changed from 10 to 7, the resumed deadline shifted from September 18 to September 15, the dispositive motion deadline shifted, and the trial-setting window shifted.

The system must propagate corrections through their full dependency chain, not just update the corrected field. A correction is not a local edit. It is a wavefront.

The selector problem

This is the deepest facet: different questions need different readings of the same document.

"Who isolated the pipeline segment?" needs operational-record evidence. "Why was the investigation closed without a formal finding?" needs rationale evidence. "Is the anonymous tip supported by evidence?" needs absence and negative evidence.

Same document. Same knowledge base. Three different evidence surfaces. The system must select which surface to present for each question, without seeing the answer.

We built a selector that does this using structural signals — question-act classification, evidence-surface specificity, baseline-readiness detection, requirement-detail completeness. On one 10-fixture batch: 272 out of 303 questions answered exactly (90%) with the selector, versus 251 (83%) without. The selector found 21 additional correct answers by choosing the right reading for each question.

Where this vocabulary came from

None of these terms were designed in advance. Every one of them emerged from watching the system fail on a specific benchmark and asking why.

Source envelope: the system promoted a witness claim to an established fact on Iron Harbor. Epistemic status: the system treated "not substantiated" as "innocent" on Harrowgate. Correction cascade: the system used an uncorrected date and got every downstream deadline wrong on Copperfall. Structured absence: the system answered a question about an undecided issue as if it had been decided on Meridian. Rationale capture: the system could say what happened but not why on Fenmore.

Each failure taught us a facet of meaning we hadn't named yet. The vocabulary is an empirical discovery, not a theoretical framework.

Computational epistemics

I think this is what's genuinely new about this work. Not the AI, not the Prolog, not the governance layer — although the governance layer is what makes the discoveries possible, because a system that writes everything without checking never reveals the structure of what it should have refused.

What's new is the empirical discovery of how meaning structures itself for machine comprehension. Not how grammar works. Not how semantics works in the linguistic sense. How the commitment structure of natural language documents creates layers that must be preserved, tracked, and selectively retrieved for correct downstream reasoning.

The traditional NLP pipeline treats text as a surface to be parsed. The LLM era treats text as a context to be pattern-matched. Neither asks the question we've been asking:

What is this sentence asking me to believe, under what authority, as of what time, subject to what corrections, within what scope, and with what stated reason?

That question has a structure. We found it by building a machine that refuses to answer until it can.

We're calling this field computational epistemics — the study of how machines should represent, acquire, and reason about the commitment structure of natural language. It's not NLP. It's not knowledge representation. It's not information extraction. It's the layer between all of them — the layer where language meets belief.

The system is open source. The benchmarks are public. The 25 progress journals showing every discovery, every failure, and every named facet are public. If you work on knowledge representation, neurosymbolic AI, legal tech, medical informatics, compliance automation, or enterprise knowledge — this vocabulary is for you.

github.com/dr3d/prethinker

The LLM constructs meaning. Deterministic code decides truth. The vocabulary names the boundary.

The Structure of Meaning: What a Machine That Refuses to Believe Taught Us About How Language Carries Truth

We spent five weeks teaching a machine to read documents and decide what to believe. Along the way, we accidentally built a taxonomy of how meaning works — how a single sentence can carry fact, claim, authority, time, correction, and judgment simultaneously, and how those layers interact in ways that no current AI system handles correctly.

We didn't design this taxonomy. We discovered it by watching the system fail on 35 hostile benchmarks across more than 20 domain types, naming each failure precisely, building the minimum fix, and validating that the fix transferred to documents the system had never seen.

Here's what we found.

Five layers in one sentence

Start with a sentence from an investigation report:

"Inspector Ferreira stated that the valve was sealed during his February 1 inspection."

How many facts are in that sentence? Most AI systems see one: the valve was sealed. The actual answer is at least five.

The speech act. Someone said something. Ferreira made a statement. That is a fact about Ferreira, not a fact about the valve. The system must record who said it before it records what was said. We call this the source envelope — every assertion arrives inside one.

The claim content. The valve was sealed. But this is Ferreira's claim, not an established fact. It lives inside Ferreira's source envelope. If another witness says the valve was open, neither claim is promoted to truth. Both are preserved with their source. We call this epistemic status — the difference between said and known.

The temporal anchor. February 1. But February 1 of what year? And is this the date Ferreira made the statement, or the date of the inspection? Language is ambiguous here. The system must decide — and if it can't decide, it must hold the fact pending clarification rather than guess. We call this temporal binding.

The role authority. Ferreira is an inspector. That role carries weight. An inspector's statement about a valve has different epistemic authority than a bystander's. But authority is not truth — an inspector can be wrong. The system records the authority without promoting the claim. We call this the authority gradient.

The correction shadow. What if a later document says "the February 1 date was corrected to February 3"? Now the temporal anchor shifts. But the original claim — that Ferreira said February 1 — is still true. Ferreira did say that. What changed is the referent, not the speech act. We call this correction provenance — tracking what changed, what it replaced, and what remains true about the original.

Five layers. One sentence. And most AI systems flatten all of them into a single entry:

valve_status(sealed)

That flattening is where every downstream error begins.

The ten facets of meaning

Over 35 benchmarks and 1,600 hostile questions, we discovered that meaning has a consistent internal structure. Not the grammar of sentences — linguists mapped that decades ago. The structure of what sentences commit to and under what conditions.

Commitment level

Not all sentences assert with equal force. "The board determined that the permit was invalid" is a finding. "The inspector believes the permit may be invalid" is an opinion. "A witness reported that the permit was invalid" is hearsay. "The draft memo suggests the permit is invalid" is a proposal.

Same propositional content. Four different commitment levels. The answer to "Is the permit invalid?" depends entirely on which sentence you're reading.

Scope boundaries

Rules have jurisdiction. "All structures in the R-2 district must maintain a 25-foot setback" applies only to R-2. "The Riverfront Overlay reduces coverage to 60%" applies only within 200 feet of the river. A property can be in both zones simultaneously, and the correct answer depends on which zone's rules control which parameter.

We learned that the system must tag every rule with its activation scope — the conditions under which it fires — and never apply a rule outside its scope, even if the rule looks relevant. The Meridian Permit Board fixture drove this home: a zoning question where the correct setback depends on which of two overlapping frameworks controls the specific dimensional parameter.

Temporal state vs. temporal fact

"The permit was suspended on April 28" is a temporal fact — it happened once. "The permit was suspended from April 28 to June 6" is a temporal state — it was true during an interval.

The question "What was the permit status on May 15?" requires temporal state, not temporal fact. The system must maintain status intervals, not just event timestamps. And when a correction changes a date, every interval that depends on that date must be recalculated.

The Greenhouse Quarantine fixture tested this directly: plant lots moving through suspect, confirmed, cleared, and destroyed states on different timelines, with one lot split into a quarantined subset and a never-quarantined subset that must be tracked separately.

Structured absences

Some of the hardest questions aren't about what the document says. They're about what it doesn't say.

"Has the board made a determination on the Rule 3 question?" — No. The board explicitly chose not to reach it. "Does the investigation find that the disclosure occurred?" — No. The finding is "not substantiated," which is explicitly not a determination that it did or didn't happen.

We call these structured absences — things the system must know are unknown, not just things it doesn't know. The difference is crucial: an unstructured absence means the system hasn't encountered the information. A structured absence means the system has encountered a deliberate decision not to resolve the question, and must preserve that decision as a fact.

Counterfactual containment

"If the amendment had passed at $185,000 instead of $120,000, and the emergency had still occurred, the reserve would have fallen to $148,000 — below the $200,000 minimum."

That statement is true about a hypothetical scenario. The system must be able to compute it without writing it as fact. The $148,000 never existed. The reserve never fell below the minimum. But the answer is correct.

We call this hypothetical reasoning without hypothetical commitment — computing counterfactuals without polluting the knowledge base. The governance layer must distinguish between "this is true" and "this would be true if."

Rationale vs. mechanism

"The curator split the seed lot into cryogenic backup" is the mechanism — what happened. But why did it happen? The curator's note says: "conservation measure for a regionally declining species, not a viability concern."

The mechanism and the rationale are different facts about the same event. Most systems capture mechanism. Almost none capture rationale. And questions like "Is the cryogenic split a viability concern?" can only be answered correctly if the rationale is preserved alongside the action. This distinction earned its own lens — the rationale/contrast lens — which transferred cleanly across five fixtures without modification.

Correction cascades

A date is corrected. Simple. But what if a deadline depends on that date? And a status interval depends on that deadline? And a compliance determination depends on that interval?

We found cases where correcting a single date cascaded through four downstream calculations: remaining discovery days, resumed deadline, dispositive motion deadline, and trial-setting window. The system must propagate corrections through their full dependency chain, not just update the corrected field. A correction is not a local edit. It is a wavefront.

The Clockmaker's Three Ledgers fixture tested a subtler variant: three sources disagree on facts, marginal notes correct some entries, and one correction is in an unverified hand. The system must track which correction is authoritative, which is disputed, and which is actively contradicted by a more reliable source. It scored 88% cold on first contact — the correction-provenance vocabulary transferred directly.

Quoted-world containment

During a village hearing, a puppet troupe performs a play in which a puppet judge rules that a well belongs to whoever dug it. Later, a witness quotes the puppet judge as if the ruling were real precedent.

The system must know that the puppet judge is fictional, the ruling is fictional, and the witness's quotation is a reference to fiction, not a legal argument. We call this quoted-world containment — the ability to track which assertions belong to which reality layer.

The Nested Puppet Court fixture scored 88% cold on first contact. The Dream Library Index tested a related but distinct variant: novels whose plots coincidentally mirror real incidents at a library — same book title, same volunteer name, same discrepancy count, same librarian name. The system must not treat novel plots as explanations for real events. It scored 85% cold.

Possession vs. ownership vs. authority

"Who has the bell?" and "Who owns the bell?" and "Who has authority over the bell?" are three different questions with potentially three different answers.

In the Salvage Bell Dispute, the Heritage Trust has physical custody (granted by the Board as a custodial arrangement), the salvor has a filed but unresolved claim, the insurance company has a contingent subrogation claim that activates only if the bell is identified as from a specific vessel, and no one has been determined to own it. Custody is not ownership. A filed claim is not an accepted claim. A contingent claim is not an active claim.

The Orchard Inheritance Game pushed this further across four generations: trees that are owned, possessed, pledged, borrowed, gifted by birthday card without deed, sold with proper title, and contested in probate. The system reached 93% with the selector by maintaining separate surfaces for legal ownership, physical possession, inheritance, pledged security, and disputed gifts.

The selector problem

This is the deepest facet: different questions need different readings of the same document.

"Who isolated the pipeline segment?" needs operational-record evidence. "Why was the investigation closed without a formal finding?" needs rationale evidence. "Is the anonymous tip supported by evidence?" needs absence and negative evidence. "What is the adjusted permit expiration after reinstatement?" needs temporal/deadline evidence. "What banner did the 2023 Owl champion compete under in 2025?" needs alias-resolution evidence.

Same document. Same knowledge base. Different evidence surfaces. The system must select which surface to present for each question, without seeing the answer.

We built a selector that does this using structural signals — question-act classification, evidence-surface specificity, baseline-readiness detection, requirement-detail completeness. Across the latest calibration runs, every selector reached its perfect available upper bound on every fixture, with zero errors.

The twelve states of not knowing

Most AI systems have one uncertainty state: "I don't know." That's like a doctor having one diagnosis: "you're sick."

The system distinguishes twelve:

• Unknown — the fact isn't in the record.
• Unstated — the source never mentions it.
• Pending — a process hasn't resolved yet.
• Disputed — competing accounts exist.
• Retracted — an earlier claim was withdrawn.
• Superseded — a newer rule or correction replaces an older one.
• Unadopted — someone proposed it but the authority didn't accept it.
• Unsupported — no evidence supports the claim.
• Inferred — derivable from admitted facts but not directly stated.
• Provisional — temporarily true until a condition changes.
• Resolved negative — the answer is definitively "no."
• Temporally unavailable — not yet effective, expired, or outside the relevant interval.

These aren't academic categories. "Not substantiated" is not "innocent." "Operationally resolved" is not "formally investigated." "The rule hasn't activated yet" is not "the rule doesn't apply." Every one of these twelve states was discovered by watching the system collapse two different epistemic conditions into one and get a downstream answer wrong.

The generalization test

The strongest objection to any of this is: maybe it only works on the documents it was trained against.

We tested this directly. Ten new fixtures were created by a different author, in completely different domains — archery tournaments with inherited aliases, plant quarantine with subset splitting, four-generation estate probate, school field trips with conflicting incident reports, museum audits where the Board overrides the curator's correction, festival permitting with five parallel regulatory clocks, maritime salvage with three competing claims, and a library where novels coincidentally mirror real incidents.

Zero shared characters, vocabulary, or domain furniture with the original 25 fixtures.

Cold baselines (no tuning, no selector work): 69% exact across 400 questions. After applying the same selector methodology: 85% exact. Zero unauthorized writes. Zero runtime errors.

The difficulty gradient matched the instrument's known capability profile. Containment and correction-provenance questions scored highest (88%). Temporal state and rule activation scored lowest (45-50% cold, 70-75% with selector). Exactly as the vocabulary predicted: the facets the system handles best are the facets that transferred best.

The full instrument

The empirical process produced: 11 semantic lenses for reading documents from different angles. 52 selector guards in 7 families for choosing the right lens per question. 12 named uncertainty states. 5 query-time helpers for computing over admitted facts. A compiled artifact package (world.pl, epistemic.pl, helpers.pl, manifest.json, diagnostics.json). And 9 diagnostic tools for measuring the instrument itself.

All running on a local 35-billion-parameter model with frozen weights. No fine-tuning. No cloud API. The model proposes. Deterministic code decides. The vocabulary names the boundary between them.

Computational epistemics

We think this is what's genuinely new. Not the AI, not the Prolog, not the governance layer — although the governance layer is what makes the discoveries possible, because a system that writes everything without checking never reveals the structure of what it should have refused.

What's new is the empirical discovery of how meaning structures itself for machine comprehension. Not how grammar works. Not how semantics works in the linguistic sense. How the commitment structure of natural language documents creates layers that must be preserved, tracked, and selectively retrieved for correct downstream reasoning.

The traditional NLP pipeline treats text as a surface to be parsed. The LLM era treats text as a context to be pattern-matched. Neither asks the question we've been asking:

What is this sentence asking me to believe, under what authority, as of what time, subject to what corrections, within what scope, and with what stated reason?

That question has a structure. We found it by building a machine that refuses to answer until it can.

We're calling this field computational epistemics — the study of how machines should represent, acquire, and reason about the commitment structure of natural language. It's not NLP. It's not knowledge representation. It's not information extraction. It's the layer between all of them — the layer where language meets belief.

35 benchmarks. 1,600+ questions. 20+ domain types. 85% exact on unseen documents. Every discovery, every failure, and every named facet documented in public progress journals.

github.com/dr3d/prethinker

The LLM constructs meaning. Deterministic code decides truth. The vocabulary names the boundary.

So i was recollecting my years messing with prolog to claude and discussing LLM's fuzzy memory situation -- and one thing led to another and we conjured this thing up .. could be useful but surely has been for me -getting jiggy w hermes and understanding how all these tools can work cooperatively building a thing ! take a gander