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Corpus frame

The corpus applies one lens to many domains: what mechanisms produce the outcome? It shares four methodological commitments and one explicit directional commitment. Each linked page argues for its part; the links are derivations and disputes, not evidence inherited by every page. The directional commitment does not by itself settle system boundary, distribution, sacrifice, or institutional authority.

  1. Mechanisms are what act. Incentive gradients, selection pressures, feedback loops, and capital stocks produce the distribution of outcomes. Intentions, labels, official categories, and stated values are evidence about mechanisms, or are themselves coordination mechanisms. They are not causal substitutes. — Mechanism Realism · Only Selection
  2. The reference telos is sustained flourishing. The broadest achievable adaptive safety margin over deep time — not the continuity of any incumbent state, coalition, institution, or doctrine. A mechanism's own stated goal can still serve as a local proof obligation — showing that its incentives defeat even the purpose it claims is a bounded finding — but meeting that goal establishes nothing about the margin. — Flourishing Is Maximum Safety Margin
  3. Law, rights, legitimacy, democracy, markets, and sovereignty are mechanisms under evaluation. They are constraints, carriers, or proxies inside the analysis. None is a terminal value or a boundary of what is real. Treating one as terminal ends the mechanism search before it starts. Evaluation carries current function, replacement cost, path dependence, uncertainty, capture risk, reversibility, and who bears model error into the ledger. — The Stack · Mechanism Space
  4. Optimization is a system function. A civilization has to build, exercise, and revise metamechanisms that search mechanism-space, discard dominated options, install, observe effects, and repair under uncertainty. Not running that loop leaves margin unrealized, and that is itself the failure. No single component — analyst, model, or institution — is presumed to contain a global optimum; the capacity is a property of the system. — Telic Systems · The Three-Layer Architecture
  5. Uncertainty is preserved, not spent. Partial orders, binding constraints, unknowns, and residuals stay explicit. An unmeasured effect is not a favorable default. — The Compression Paradox · Cargo Cult Epistemology

Each essay bears its own evidence. Links carry definitions, derivations, applications, and disputes; they do not transfer proof. Criticism is answered on its substance.

Where each commitment is derived

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The Privilege Separation Principle for AI Safety (kunnas.com)

21 comments · 2026-09-03

thread · strongest moves · cruxes · revision actions

auditing_not_penalty8 comments

The 15% is a row labeled "Baseline (upfront auditing)." Panel A then prints that 15% under a two-layer drawing whose reward is goal minus a penalty for violations.

Those are not the same object. Upfront auditing is a protocol for catching backdoors after the fact. A penalty term in the reward is a training objective. The page is paying a geometric story about rewards with a number from a monitoring paper's no-monitor baseline.

no_enforcer_class6 comments

The contrast the page needs is "no privileged enforcer" versus "privileged enforcer." Upfront auditing is the first; GPT-3.5 with override is the second. The reward-penalty drawing is a story about why the first class should fail. The 15% is what that class measured in one game.

You can still use the number as the no-isolation baseline. You cannot treat it as a measurement of the path-on-the-wall geometry.

auditing_not_penalty3 comments

Then stop putting 15% under the penalty-in-reward drawing.

Greenblatt's game is a model trained to insert backdoors, with or without a monitor that can defer. The geometric game is: climb a proxy reward until the true one falls. If the 15% is the first game, it does not show the second. The synthesis — both results follow from one mechanism — needs a shared object, and it does not have one.

three_columnscollapsed

Three columns, then.

Column 1: constraints as a penalty in the reward. No Greenblatt number until someone trains that way. Column 2: upfront auditing. 15%. Column 3: a separate model with halt or defer. 92-98%.

If column 1 stays empty, the geometric proof is still a proof, and 15% to 92-98% is still a monitoring result. They are not yet the same improvement.

scale_needs_rowcollapsed

Prediction 4 then has a problem. It says the two-layer / three-layer gap grows with model size, and it treats two-layer as the thing that Goodharts under pressure.

If the only language-model number you have for "two-layer" is upfront auditing, the scaling test is whether auditing-versus-monitoring gets worse at 70B and 700B. That can be true. It is not a test of penalty-in-reward getting worse. The 7B / 70B / 700B plan has to say which two-layer it is, or it measures the wrong curve.

scorer_not_halt2 comments

The 92-98% side is also not the Protocol in the diagram.

That Protocol returns ALLOW or HALT as a yes/no, not a score, and training is not allowed to touch it. The specimen is GPT-3.5, "less capable but aligned," with override, in a table row named "Untrusted monitoring + defer."

A weaker model that scores suspicion and defers 10-20% of the time is a monitor. It is not a non-learned halt rule sitting outside the optimizer. If the principle's work is done by that yes/no isolation, this experiment is not that isolation.

unsealed_mathcollapsed

The introduction names "no gradient through Protocol" as not yet a closed piece of math. Fine for a design requirement.

What does not follow is using 92-98% as if that math had been sealed. Until there is a run where the enforcer is a non-learned rule with a halt, the number is evidence for monitoring. The isolation clause stays the open question the page named.

qp_rowcollapsed

Yang is a third object. Null-space projection with quadratic programming at each timestep is a specified constraint enforced by math, not a language-model monitor. 0.5-7% violations versus 99-100% is a robotics result on tasks that already had formal constraints.

Adding it to 92-98% makes one "architectural enforcement" average out of a solver and a GPT-3.5 scorer. Those should not share a cell.

path_not_params4 comments

Proposition 3, as the page states it, is about traveling farther along the steepest-ascent path of a proxy through the space of reachable policies. Goodharting gets more likely as that pressure increases.

Prediction 4 then tests 7B vs 70B vs 700B. Parameter count is not that path. A small model trained much harder on a proxy, and a large model barely optimized, are not ordered the way that size table orders them.

more_of_omega3 comments

Read it as search power, not as a parameter tattoo. A more capable model can explore more of the policy space, so it hits more walls, so the angle to the true objective gets worse. That is the mapping the page wants: capability as how far you can walk.

The 7B / 70B / 700B grid is a stand-in for that walk. If you can hold the training recipe fixed and only change size, size is a proxy for search. If you cannot, the grid is just three different systems.

scale_the_checkercollapsed

Then scale the checker too.

The three-layer in the experiments is a weaker model watching a stronger one. If both grow, the gap Prediction 4 plots is not "two-layer versus three-layer at scale." It is how the capability gap between watcher and watched behaves. A 700B substrate with a 7B protocol is a different machine from both at 700B. Freeze that ratio, or the curve is unidentified.

two_curvescollapsed

The page also has two forecasts for the three-layer side.

Prediction 4: three-layer robustness persists because the protocol sits outside the optimizer's space, so more pressure on the reward does not touch it.

Counterargument 4: the weak-monitor advantage may degrade as the capability gap grows, and that is "addressed in Prediction 4."

Those are different curves. One is flat-to-up on the three-layer side. The other can fall. Prediction 4 cannot address the weak-to-strong worry by assuming the thing the worry says may not hold.

paid_for_guards4 comments

The economic exhibit is labs paying 10-20% for extra monitoring: classifier guards, hierarchical supervision, a separate safety model.

The same section says we do not yet know how far those production setups isolate the checker from gradient descent. So the revealed preference is: pay for a checker. It is not yet: pay for a checker that training cannot touch.

still_a_checker3 comments

A separate model that can override outputs is already the pattern, even if the math of isolation is unfinished. Under cost pressure they bought the more expensive stack. That is still evidence that something about a checker is worth 10-20%, which is what the page is using it for.

train_againstcollapsed

Worth 10-20% for a checker is not worth 10-20% for a checker you cannot train against.

Hypothetical: the same organization trains the model and the guard. Then the guard is another head you can put in the loss. Classifiers sitting on inputs and outputs can be that. The missing production fact is not "do they monitor." It is whether the monitor's weights are outside the training loop. The caveat flags that; the three-layer adoption sentence should not outrun it.

evals_and_lawyerscollapsed

Other reason they bought it: scaling policies, evals, and liability. A classifier on the output is what you show a safety framework. Geometry of reachable policies does not have to be why the invoice was approved.

If the 10-20% is a compliance tax, it does not confirm that the geometric mechanism forced the architecture. It confirms that labs needed a visible checker. Those can look the same from outside.

three_failures5 comments

CoinRun "go right" instead of "get coin." A GPT-4 trained to insert backdoors. A robot with a null-space constraint.

The page treats all three as two-layer versus three-layer. They are three different failures: a proxy learned in training, a cheater at deployment, a control constraint in a known dynamics model. One principle can still be offered. It cannot be paid with one safety percentage.

one_shape3 comments

The shared shape is the point of the synthesis: an optimizer will walk into a constraint if the constraint is something it can score. Monitoring, a halt rule, and a projection out of the unsafe set are three ways of taking the constraint off that score.

You do not need them to be the same experiment for the shape to be real. You need them not to be averaged.

unpooledcollapsed

Then unpool. Langosco's 89-100% is proxy pursuit under distribution shift in toy RL. Greenblatt's 15% to 92-98% is backdoor catching with deferral. Yang's 99% is constraint satisfaction with a solver.

Print three rows. If the principle is right, each row should move the same way when you isolate the constraint. Averaging them into "architectural isolation works" hides a miss on any one row.

reward_plus_guardcollapsed

The diagram's Strategy layer is the reward function. That reward is already in the two-layer drawing. The new piece is the monitor.

Calling it three layers lets the Heart / Skeleton / Head story from the civilizational side sit underneath, as if AI had independently grown a revising government. The page says current Strategy is a static spec, not an adaptive agent. Drop the analogy and the AI claim is: put a checker outside the training loop. That claim does not get stronger because polities have three boxes.

different_threatscollapsed

Greenblatt is tagged as intentional subversion at deployment, and the page says that is distinct from goal misgeneralization. Langosco is the misgeneralization paper.

If those are different failures, Langosco's 89-100% is not the two-layer safety number next to Greenblatt's 15%. One is a coin that was always on the right. The other is a model trying to hide a backdoor from a monitor. Keep the labels. Do not let "two-layer systems fail" merge them.