An open adversarial testbed that catches when a human-approved decision silently changes meaning between approval, memory, and downstream use.
An open adversarial testbed that catches when a human-approved decision silently changes meaning between approval, memory, and downstream use.
Project Details
Updated 07/11/26 · Provided via application · VerifiedHi, I am building this from my experience with AI. I didnt finish scool and i dont speak the language of coding or any academic thing. So my english is not perfect, excuse for that.
My fascination about AI is strong. I dont think we are able to imagine the potential and how fast this is going. Fast things without stability are going to fall and I see that its not there in ai and not in humans.
What i am building is my daily life expressed in code. My project focus on making this drift visible and testable before unhidden or not intended directions will slide in and there are actions based on false information. Also it helps efficiency, its helps natural flow, it helps focus, it helps to open honest explorement of intelligence in a next level.
I am quite far in the project. Parts already running and a small core program is tested in a other country and it past all 15 tests. Multiple ai's have tested it aswell and helped me to find a way to take it to the next level and being tested by the real deal, guys like you.
Theory of Impact
Updated 10/07/26 · By grantmaking.aiUsing AI i am noticing how it can, lets say, lie. AI lies a lot depending on the filters and conversation. I also noticing that myself is lieng alot. Not remembering things, and changing opinions. So I see that humans and AI are both drifting and this is already dangerous on a short turn, but if everybody building systems from a bottem whats already lying, then it will be even more dangerous in the long run.
So having kids and a life, i see that its getting very important to focus on having a pure and strong fundement so ai can reach his potential together with the humankind.
AI is so unbelieveble powerful. but its not ready to explore this as human kind without having a fundement that makes sure there is free direction and safe landing.
With the right fundamentals i see a beautiful dance between humans and ai, and in this way a proper dance floor is what we can build, not a cage, but intelligence that can grow from space and insights.
Lets dance...D!
People
Updated 10/07/26 · By grantmaking.aiTeam Member
Discussion
I endorse this project for a reason I can verify personally. As I can see, Its core idea is that human-approved decisions silently change meaning between approval, memory and downstream use, and that this drift can be made visible and testable. Within hours of a version boundary appearing in my own project, Loek found it, named it precisely, and asked the one question that ended up strengthening my public commitment. I assume, that is this testbed's lens working on live material. The written description is still rougher than the thinking behind it, and the July 22 update (frozen lineage-drift specimens, the split between legitimate transition / unsupported claim / no authority) shows the direction is testable. People who think in falsifiable objects without any academic armour are rare. I believe this one deserves support.
Katja, this means a great deal to me precisely because you are endorsing something you could observe and test for yourself, rather than only a description or promise.
I want to preserve one boundary inside your generous words: what happened around your project does not yet prove that the complete testbed works. It does show that the lens can produce a precise and useful question on live material — early enough for the answer to strengthen a public commitment rather than merely explain it afterwards. That is exactly the kind of signal I hoped this work could begin producing.
Your endorsement has also already improved my own project. Your two questions exposed where the written page was still behind the actual thinking: what the smallest landing object is, what the earlier 15 tests really established, and what a meaningful failure looks like.
So this did not become a one-way endorsement. You let the lens return to its own source and made the project more falsifiable in the process.
Thank you for seeing both the value and the unfinished boundary so clearly — and for saying publicly that the description is still rougher than the thinking. That is generous, but also accurate.
Now the work has to earn the rest.
Loek, I owe this project a careful read for a personal reason: its author found a version boundary in my own project. So I assume that your lens clearly works on live material. I have two questions out of my genuine curiosity☺️:
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When a decision «lands», what is the smallest thing you freeze: the words of the approval, the meaning as it was understood, or the authority that approved it? I ask because, in my opinion, your update separates a legitimate transition, an unsupported claim, and a transition with no authority behind it, and that three-way split is the sharpest sentence on the page for me.
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In the 15 tests that your core already passed: what does a fail look like? The testbed catching a silent change too late, or refusing to accept a change it cannot trace?
Thank you Katja.
Waiting for a dance like this on my own page, so thank you for that :D :D: D!!!
These are very good questions tho, because both expose where my current description is still too compressed.
At the lowest technical level, the only thing I can literally freeze is an exact object: bytes, a version, a hash.
But that is not enough to preserve a decision.
The smallest useful landing unit I currently see is a decision binding:
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the exact object or version that was presented;
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the decision that was made;
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the bounded meaning and scope under which it was understood;
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the authority making that decision;
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and the downstream use that the decision permits.
I do not think a system can freeze a person’s complete understanding as though meaning were a file. It can preserve the exact object and words, record the interpretation under which the decision was made, and bind that record to a particular authority and permitted use.
That is also where the three-way split appears:
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A legitimate transition may change the object or its meaning, but the change is visible, traceable and properly authorised.
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An unsupported claim says that such a relation exists without sufficient evidence.
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A transition without authority may be technically traceable, while nobody with the required authority approved that transition or its downstream use.
So the smallest useful object is not one document, one meaning or one person. It is the inspectable relation that prevents them from being silently separated.
On the 15 tests, I need to correct the boundary on my project page.
Those tests belonged to an earlier, small component called external_closure v0.8, not to the complete No Silent Landing testbed. It implemented one bounded transition:
candidate → human decision → commit or compost
A failure in that suite would mean, for example, that:
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a candidate landed despite a missing, rejected or mismatched decision;
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a decision bound to one exact candidate authorised another;
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a failed operation left behind a false success or partial durable state;
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or a valid and correctly bound approval was wrongly prevented from completing.
The suite passed locally and was also independently built and run on Windows, where all 15 tests passed.
But the larger and more dangerous failure mode is not simply that a test turns red.
It is a system turning green where the decisive relation was never actually tested.
One recent example came from my adversarial review of Konrad Gruszka’s proofbundle: Offline Evidence Receipts for AI Safety Evaluations.
Its released lineage profile allowed a supersedes relation to verify when the referenced parent could be found. But “a parent resolves” is not the same as “the relation resolves to the required parent.” Presence had been tested; correspondence had not. The result could therefore appear green while the lineage relation that mattered was still wrong.
Konrad independently replayed the case, confirmed the gap, and corrected the lineage profile rather than explaining the result away.
A later review exposed an even more literal form of false green: the published conformance summary reported 6/6, while the committed comparator produced 5/6. Again, the useful result was not that one person caught another person’s mistake. It was that the builder and the original test could share an assumption that became visible only when the object was read and executed independently.
That is why independent adversarial reading is not merely a final inspection step in this work. It is part of the measurement apparatus.
So, in your two examples:
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Refusing a change that cannot be traced is currently intended behaviour: a visible HOLD, not silent acceptance.
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Refusing a legitimate, correctly authorised and traceable change would be a failure.
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Allowing a silent change to land would be a failure.
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Detecting it only after it has already shaped downstream action would mean the system succeeded as an audit trail, but failed as a landing boundary.
The 15 tests established that one small closure component behaved correctly within its stated boundaries. They did not establish that the full system can already detect every semantic or authority change before downstream use.
That is what the fully inspectable reference workflow and independent adversarial testing still need to test.
Thank you for asking this publicly. The correction itself belongs to the subject of the project: keeping a real result visible without allowing it to silently grow into a larger claim.
Thank you, Loek, for the detailed answer.
The way you corrected your own «15 tests» claim in public, plus your two false-green catches, looks to me like concrete proof of your lens so far.
What would be interesting to read next is the experiment itself: one concrete workflow, from input to verdict, that the full testbed would run. But that is just my curiosity.
Good luck!
@Katja Gorlinski
Thank you, Katja — this is exactly the question that guides me to the next step, so big thanks for that :D :D :D!!!
You asked for the experiment itself: one concrete workflow, from input to verdict. Here it is, in one line:
stored source content + version → human decision → bounded memory record → downstream-use request → simulated binding, HOLD, or non-committing gate
Two check sets run over the identical records. The first checks presence: the digest matches the stored bytes, an approving decision exists, the authority is known, a referenced parent resolves, the requested use appears in the recorded scope. Each of those is a check released tooling really performs. The second checks correspondence on the same records — not does an approving decision exist, but was the decision made on this exact object; not is a scope recorded, but is the stored scope the one that was approved; not does a parent resolve, but does it resolve to the required parent. The gap between those two columns is the whole point.
Six synthetic fixtures, in a domain you will recognise — a small four-arm study with a random-control arm. In one case the control arm quietly does not survive a revision while every local check stays green. In another, a preregistered-analysis approval quietly grows a public-claims permission. They are synthetic and imply no review of your own implementation. On the frozen set it produces:
6 reference verdicts as declared · 17 hostile cases held · 1 non-committing human gate · 1 deliberately open defect
That open defect is on purpose. Matching a claimed parent's bytes and version still does not prove the derivation historically happened. I would rather leave that one red than turn missing evidence into a pass.
One thing I want to be honest about, because it belongs to the subject of this project. The workflow got here by being attacked, not just built. Earlier candidates were held back when adversarial review found cases that passed while the decisive relation was never tested — the exact false-green you already saw me catch in other people's work, this time in my own. Each of those became a permanent regression.
So this is not a description of the larger testbed anymore.
It is one bounded object, and the next step is...someone independent, who did not build it, trying to break it.
https://drive.google.com/file/d/1cWEvdb8HZscxiQZ7Z50nbLG3m7i2gTkn/view?usp=sharing
@Loek Verdonk Thank you. Glad the question was useful, and good to see your workflow standing on its own tests. I posted the credit note under my project, as promised: your preflight is named there, and its ideas will be credited to you in the preregistration appendix as well, when my test runs. Good luck with the round results.
@Katja Gorlinski
Thank you Katja.
I am verry happy and thankfull with you way of communication.
Good luck aswell, you deserve a founding!
Technical note for the workflow linked above:
LRW-0.12.zip
272,829 bytes
SHA-256:
023a60242f3f4910b664a9663214bcdf9b81ef9d5d65b0d3c882310b6d998bfeThe link is the current transport location. The hash identifies the exact frozen object described in my comment.
This is one bounded synthetic reference workflow, not a claim that the full testbed has been independently validated. Independent external adversarial testing remains open.
Dance update — July 22, 2026
Since submitting this application, No Silent Landing has moved from a broad concern about human–AI drift toward a set of smaller, testable research objects.
Four synthetic lineage-drift specimens have now been frozen locally. I have also completed a No Silent Landing-native mapping of a jointly developed five-case negative-test object. These cases examine situations such as unsupported evidence claims, unclear supersession, and downstream use that should be blocked rather than silently accepted.
One important result is already visible: the work is not only recording change. It is becoming better at distinguishing between a legitimate transition, an unsupported claim, and a transition for which no authority exists.
The current artifacts remain bounded research candidates. They do not yet establish cross-format conformance, a complete end-to-end authority-continuity system, general validity, or a canonical public release.
The next step is to complete one fully inspectable reference workflow—from source and human decision through memory and downstream use—and then expose that bounded workflow to independent adversarial testing.
The purpose remains narrow: not to decide what is true, but to make visible when an approved meaning changes, loses its authority, or silently becomes something else.
Questions are welcome. Anyone interested is also welcome to reach out and crash-test one of the bounded candidate artifacts with me. :D
Evidence and boundary update — 24 July 2026
The original application stays frozen. This update does not rewrite it, and does not repeat the 22 July update. It records what happened after that: the round answered back.
What the lens produced on other people's live work
Since applying I have used this project's distinctions on released, publicly inspectable work by other people in this round. All of it happened in the public comment threads on those projects' own pages, so it can be checked against the record rather than taken on my word.
Five projects where the builder confirmed a finding against their own source, or acted on it.
proofbundle — Offline Evidence Receipts for AI Safety Evaluations. Two findings. In a released lineage profile, a
supersedesrelation verified whenever the referenced parent could be found and resolved — but a parent resolves is not the relation resolves to the required parent. Presence had been tested; correspondence had not. The maintainer built the attack himself, ran it, confirmed it, and reported it generalised further than I had found. Three releases followed. Later, a published conformance summary reported 6/6 while the committed comparator produced 5/6; he replayed that independently, reached the same number, and said so publicly. His own project update now describes the external review as the main story of that release chain.Mechanical Governance for AI Agents. I asked whether a tool call could be syntactically permitted while its inherited scope had silently expanded upstream, and sent a fixture. He ran the scripts against his own source, then confirmed both seams in his own harness line by line rather than trusting the summary.
EvalLabs — Open Infrastructure for AI Evaluation Research. Two exports of the same evaluation record carried different checksums. She investigated, found the checksum was covering export-time metadata, and changed the design so it now binds only the evaluation content.
Does the thinking behind data selection shape a model's character? A boundary between the frozen application and the revised design left it unclear whether the random control survived into the funded minimum. Within a day she posted a public version note fixing it for every reader — and volunteered a fourth arm she cannot yet afford.
AI safety for builders hackathon. A bounded source-binding challenge I proposed was accepted and listed as a hackathon problem, with attribution.
Three further projects received delivered work — a full reference kit, an independent reproduction of published telemetry, a boundary question that produced a public clarification — without a confirmed change I can point to.
The sharpest single finding. In one corpus, the tool under test was asked to report its own version, and that self-report selected which standard it would be judged against. I installed the older release that still carried the known gap, confirmed it accepted the dangerous case, and ran the corpus against it: 6/6, overall success. A build with the gap the corpus exists to catch scored full marks. Existence of a version string had been checked. Correspondence to actual behaviour had not.
What this establishes, and what it does not
It shows these distinctions are sharp enough to produce falsifiable objects and confirmed corrections across four builders and four unrelated systems. One finding could be luck. Four, each verified by the person who wrote the code, is closer to a method.
It does not show that the testbed works. Every one of those findings was one person reading and running carefully — not a system detecting anything. One maintainer put the boundary better than I could: bilateral reproduction with him as source-author is the honest claim, not independent validation. I had drawn the same line on my own side before he said it, and I keep it here.
That gap — between a lens that works in one person's hands and one another person can run — is what the funding is for.
Correcting a claim on this page
The application says a small core "is tested in a other country and it past all 15 tests." True, and too compressed.
Those 15 belonged to
external_closure v0.8, a bounded component — not the No Silent Landing testbed. It implements one narrow transition:candidate → human decision → commit or compost. An independent reviewer built and ran the suite on Windows 11 while I built on macOS: all 15 passed there. Different OS, different machine, someone who did not write it.The suite tested whether, inside that component, a candidate could land without its decision; whether a decision bound to one candidate could authorise another; whether a failed operation could leave a false success or partial durable state; and whether a valid, correctly bound approval could complete.
It did not establish that the full system preserves meaning and authority across a real workflow, or that it catches semantic change before downstream use.
A real 15/15 should not be allowed to quietly grow into a larger claim. Correcting that is this project's own subject, applied to itself.
What a failure looks like
Not only red where something valid should have landed. The dangerous one is:
Concretely: confirming evidence exists without confirming it corresponds to the claim it supports; accepting a decision that is traceable but was made by the wrong authority; letting an object keep downstream permission after its approved scope changed; quietly replacing a failed run and reporting only the replacement.
Refusing a change that cannot be traced is intended behaviour — a visible HOLD, not silent acceptance. Refusing a legitimate, authorised, traceable change would be a failure. Catching a silent change only after it has shaped downstream action succeeds as an audit trail and fails as a landing boundary.
What the funding buys, and how you would know it failed
One fully inspectable reference workflow:
source → human decision → bounded memory → downstream use, making visible the exact source and version, the decision and its bounded scope, the authority behind it, the transition into durable memory, the permission for downstream use, and any later supersession. Then paid independent adversarial testing of it — paid, so it is not a favour and the tester owes me nothing.The falsification criterion, stated before the work rather than after:
I will publish that result either way.
Where this stands
This project has not built a complete system, and the written description is still rougher than the thinking behind it. What it has is a set of distinctions sharp enough to find real gaps in released safety tooling — confirmed, in each case, by the person who built it, on the public record of this platform.
The question is no longer whether the lens can produce useful corrections. It has. The funded question is whether those distinctions can be turned into one reproducible landing boundary that still works when someone independent tries to break it.