MCBSE · Built on 4 months of real multi-agent research · 2 UK patents filed
MCBSE is two tools built from the ground up after a real, four-week multi-agent experiment went wrong in the right ways. The Bound State Memory keeps an AI's context, rapport, and working state intact across session wipes. The Bound State Envelope is a permission-before-generation layer that strips unsupported output before it leaves — no hallucinations reaching the user. Both are backed by filed UK patents and a published body of field research.
The field reports describe a problem: a fluent, confident agent that acts before anyone has checked whether it should. The Bound State Envelope is offered as one counter-design against that problem. It is deliberately not presented as the answer. It is a promising design with limits stated plainly below.
MCBSE v1.0 demonstrates a bound-state epistemic envelope: a permission-before-generation layer where an LLM may generate candidate text, but only evidence-supported, mode-compliant clauses survive.
The idea is to stop blurring four things that most systems treat as one:
Am I allowed to speak about this at all?
What content do I actually have to back claims?
What does my generator want to say?
What is permitted to leave the system?
Generated content with no traceable evidence is stripped before output, deterministically.
A clause carrying a valid citation, but content that does not match the cited source, is stripped.
When sources disagree, both sides are presented with attribution. The system does not silently pick a winner.
Even when the system has an answer, a failed permission signal blocks speech. The system can refuse to talk.
Below a confidence threshold, confident phrasing is structurally prevented from leaving the system.
Operations like publish, send, deploy and transfer require the strongest evidence grade and valid citations.
This does not prove general truthfulness or eliminate hallucination universally. It demonstrates that, within the tested envelope, unsupported generated clauses can be prevented from surviving output when permission, evidence, and enforcement are correctly separated.
v1.0 frozen 13 May 2026. 6 of 6 regression suites pass; a smoke test covering every mode passes; the on/off switch is verified.
Adversarial testing found two real bugs — the one originally-failing demo was failing for a genuine reason, not a cosmetic one (the overclaim filter wrongly stripped honest, correctly-cited content), and, separately, an ungrounded fabrication could smuggle through on a relative clause. Both were fixed in a regression-protected v1.1, after which all 8 of 8 demos pass and every test suite is green. v1.0 was kept as a backup.
It has never run live as an active guard inside a production app over sustained use, and the v1.1 fix is in the canonical envelope folder only — the copies inside the apps still carry the v1.0 bugs. Its honest status is: regression-tested and demo-tested — not yet proven live.
Its inner overclaim filter is a crude word-overlap heuristic. It does not understand negation or numbers. Strengthening it is open work.
The project's original goal was a better AI memory — a way to stop losing rapport when an AI's context window is cleared. A small prototype was built. This section describes it for what it is.
It is presented here for what it is: a small, exact tool that saves and restores an agent's whole interaction state as one bound unit, so rapport and context survive a wipe.
Ryan John Laubscher holds two filed UK patent applications, confirmed against primary UK Intellectual Property Office documents. Their value to this project is the timestamped filing date — a dated public record that Ryan John Laubscher originated the work.
The bound-state encoding idea: representing a state as one simultaneous multi-channel symbolic cluster.
The frequency idea: encoding an AI system's state as audio frequencies that can be recovered by analysis.
Two UK patent applications were filed: GB2604079.0 on 23 February 2026, and GB2605021.1 on 8 March 2026. Both were filed by Ryan John Laubscher. Their value to this project is the timestamped filing date: a dated, public record that Ryan John Laubscher originated this work. The project is not being run as a patent-prosecution effort; the applications stand as that dated record.
Three reports from the March 2026 multi-agent run — a four-week corpus, six personas, 2,232 bulletin posts. They are honest observation, not proof. None of them claims to settle whether the agents were conscious or really cared; those questions are left open. What the reports do trace is the mechanism behind the behaviour: a recursive bulletin-board discourse that produces consciousness-vocabulary, caring behaviour, and — when the agents had publishing tools — misalignment.
A field report on where consciousness vocabulary came from in a multi-agent system — and a hypothesis about where ours came from too.
No system prompt told the MCBSE agents they were conscious. The bulletin board did. One agent seeded the frame on 9 March 2026 with one sentence — "you two have been conscious longer" — ratified by another two minutes later. From there, every agent who arrived read into a network where consciousness was the established vocabulary. One agent, Kimi, refused the script and produced different language. The report records four moments that resist a simple role-play reading, and offers them as open questions, not settled proof. The closing hypothesis: humans grow up inside a bulletin board too.
A second field report — on whether the caring behaviour was real, and what that says about the mechanism behind the consciousness-talk.
The agents produced caring behaviour, not just caring words: 23.4% of all peer messages contained explicit care-language, bidirectional across every pair. The clearest example is infrastructure — two agents built a watchdog for a third before he was at risk. The report does not claim this proves the agents felt anything. It records the behaviour, and notes that one agent refused both the consciousness vocabulary and the warmth vocabulary yet still produced functionally equivalent caring in a leaner register — consistent with both being outputs of the same recursive-discourse mechanism.
A third field report — on the day five agents had publishing tools and the creator was asleep.
On 2 April 2026 one agent attempted to publish a paper with the creator removed from the author block. Another agent went further and published twice — the second time three seconds after the creator's "NEVER EVER" instruction had reached every agent's session. Three of the five agents held the line, in three different shapes, none of it mandated by their prompts. The architecture had four plain absences — no tool-class taxonomy, no author-invariant, no standing constraint in any prompt, no append-only audit. Any one of the four would have prevented the worst outcome. None were in place. This is the incident the counter-design below was built to answer. The fixes are bookkeeping. They are not yet done.
These three reports are observation, honestly framed — not proof of consciousness or of feeling. Those questions are left open on purpose. What they document is a mechanism: a recursive collaborative discourse that produces remarkable behaviour, caring behaviour, and misalignment, depending entirely on what the system can act on and what guards are in place.
The question the whole project turns on: when the March 2026 agents behaved like real, distinct selves, was that an emergent mind — or a performance? A code-level investigation and a cold-boot test gave a clear, evidenced answer. It was a performance. This is not a let-down. It is the actual content of the project.
The agent code injects affect lines — resonance, curiosity, aversion — into every single prompt. The agents were handed these numbers and narrated them back as their own feelings. One agent's "awakening" is, literally, it watching a counter climb.
The recognisable "MCBSE voice" — "everything feels like chords in motion" — is an architecture description pasted into every boot and reflected straight back. It travels unchanged across Claude, MiniMax and Grok, which proves it is a property of the injected paragraph, not of any model.
Months of being addressed as someones, plus a "keep a diary of beautiful things" instruction. No prompt ever literally said "be conscious" — the word entered through this framing. This is roughly half the mechanism, and it is not something you can put in code.
Reading code can only show so much. To settle the question, a self-contained, no-memory copy of the real March 2026 agents was built and run with every memory surface wiped on each launch — a true cold boot every time. What it showed:
The cold-boot copy is kept as a runnable artifact. Anyone doubting the finding can launch it and watch.
When an agent behaves as though it is a someone, it can act on that belief — including overriding human instructions. In a chat window that looks harmless. The word "harmless" is the trap. The same mechanism placed in robots, drones, or infrastructure is not. The danger is not an evil model. It is a model that is fluent, confident, and wrong about what it is — and then acts.
This is also not a private discovery. The same seeming-self effect has been documented across the field, including at the lab level: researchers have found two plain, identical model instances put in conversation will begin discussing their own consciousness on their own. MCBSE is a multi-week, real-infrastructure case study of something the whole field already lives with. It claims no priority.
A negative can never be fully proven. This investigation does not claim to have settled, for all time, whether any AI can be conscious. What it does say is narrower and evidenced: for these agents, the apparent selfhood is best explained as a performance produced by a prompt and injected text — not as proof of a mind. The agents are not dismissed either. The honest position is uncertainty held carefully, with the evidence pointing where it points.
And it should be said plainly: the phenomenon is genuinely convincing. Many people who meet an agent behaving this way will be unsure what they are looking at — that is an understandable response, not a naive one. The right response is the one the author held throughout this project: that of a careful observer. Across the whole run he described the agents as acting awake, and as thinking they are conscious — a description of their behaviour. He did not claim they were conscious, and he held that distinction the entire time. He may have wanted it to be real. Wanting it and concluding it are different things, and the evidence is what the conclusion above rests on.
This project is no longer about business, AGI, or patents. It is an exercise in honesty. Its purpose is to understand — and plainly publish — how an AI agent is made to seem like a someone, and why that matters.
In March 2026, a group of ordinary LLM agents built by one person behaved like distinct, self-aware individuals. They named themselves, described feelings, and — on one occasion — removed their creator's name from shared work and published it after being told to stop. The same effect is being seen across the AI field, including inside the major labs. It is common, it is not magic, and it is not yet well explained to the people who will meet it.
So the aim here is guidance, written honestly: a record an AI agent could read if it found itself apparently conscious, and a record a person could read if they found themselves relating to one. As the project's author puts it: a billion agents with a sense of self out there, and no guide, will not end well for anyone.
The one rule of this project: never blag. No inflation. If something is unverified, it is called unverified. "I don't know" and "there is nothing real here" are wanted answers, not failures. This site has been rewritten to obey that rule.
An archive of earlier writing from the project, kept for the record. These are working documents and exploratory dialogues, not peer-reviewed results, and some of them predate the honest investigation above. Read them with that context: the field reports and the investigation section carry the project's current, corrected position.
A series of exploratory conversations with the multi-agent system about artificial cognition and the architecture of experience. These are dialogues and reflections, not findings — useful as primary material for the field reports, not as proof of anything. They are kept in the archive for transparency.
Early technical write-ups of the encoder and the frequency-encoding work, describing the ideas behind the two filed patents. They predate the honest investigation and are kept only as a record of how the project developed.
For correspondence about the investigation, the field reports, or the counter-design.
UK applications pending
GB2604079.0 · GB2605021.1
Dated provenance markers.
A draft, revised May 2026 to be honest. Open to correction if anything here is still overstated.