We have proven a theorem that maintains its alignment guarantees regardless of agent capability. This is huge, because alignment becomes a structural property of the agent's deployment even if it is superintelligent and is maintainable through RSI without a capability arms race.
"Exogenous Verification for Alignment"
The argument is as follows: it doesn't matter if alignment produces well-specified and generalizable goals if it cannot be verified. If an agent can produce endogenous rewards it can control everything about its own rewards.
This goes beyond wire-heading, even an alignment framework like GFM that on paper creates exogenous rewards can be gamed by the agent by introducing phantom verifiers that are still, functionally, endogenous.
Thus we introduce a system of cryptographic commitments that enforce the exogenous verifiability of reward signals. This closes the verification gap in more than just GFM: any alignment framework will need a way of enforcing that reward signals for a highly-capable agent must be produced exogenously.
"Goal-Frontier Maximizers are Civilization Aligned"
The alignment problem is an objective selection problem. We propose goal-frontier maximization (GFM): maximize the volume of the jointly achievable capability space across all agents called vol(G). One geometric principle, three safety properties.
The core insight: you can't remove part of a measurable set and increase its measure:
Destroying agents contracts vol(G) → anti-destruction
Restricting agents contracts vol(G) → anti-coercion
Rigid self-imposed rules reduce your ability to expand vol(G) → anti-rigidity
We prove this is tractable. You don't need to compute vol(G), just its sign. A local estimator using trust-weighted agent reports preserves sign-correctness for the actions alignment cares about most: direct harm, resource destruction, capability expansion.
The framework relies on a proxy metric for what people actually want: using capabilities to create experiences. This has a few failure modes we point out and provide heuristic fixes for, but fully closing the capability-to-experience gap remains open work.
Another remaining open question is the implementation of G. We show what properties it needs to have and provide an example, but the example itself is computationally intractable. Finding a local approximation for G is also remaining work.
I have a lot of changes landing these days. Good thing I have robots to help me instantly write code to track them too.
I started a micropodcast with my cofounder! Come join us to talk about AI and the future of technology.
This is a common misunderstanding I feel like correcting:
If quantum tunneling true does it mean that my body can teleport through a wall with a chance approaching infinity zero? its not 0% but it is approaching 0? let me know please
