2026-04-09 13:20:20

I’ve written 7 blogs for Inkhaven so far. That leaves 23 to go.

If you’ve been annoyed by the sudden influx of daily blog posts, good news! I’ve decided to stop sending all of these to my subscribers. I’ll plan to limit it to the ones I think people will be most interested in.

Today’s blog is a list of possible blog posts. I’m curious which ones people are most keen on having me write! Or also: if you have ideas that aren’t listed here…

Jumping right in:

1. What is happening right now? What is everyone doing and why?
   A candid account of the situation from my perspective. I think the situation on the ground is an urgent crisis. Other people’s actions don’t seem to match that reality. What gives?

2. The societal-scale risks of AI
   My account of what is at stake and how AI threatens it.

3. Who has the burden of proof for AI x-risk?
   A lot of people act like the ones who claim that AI could kill everyone need to provide clear-cut evidence. I disagree. I name names. Philosophy ensues.

4. Why is AI risk such a hard problem?
   Two blog posts (at least), focused on technical and non-technical aspects. I disagree with basically everyone else in one way or another, so seems good to get my own account of my views out there.

5. On Rogue AI vs. “Scheming”
   Rogue AI systems seek to thwart human control in order to achieve their own objectives. “Scheming” is when AI systems seek to deceive humans who are supposed to be in control in order to achieve their own objectives. What’s the difference, and why does it matter?

6. Stopping AI is easier than regulating AI.
   People agree we want more AI regulation. The main reason to support an indefinite pause on AI instead of something milder is because the milder things are harder to enforce, and enforcement is going to be hard enough already.

7. Alternative solutions to AI risk
   Probably several blog posts. Going over all of the other solutions proposed to address AI risk and discussing why they are inadequate.

8. What a good future might look like
   Suppose we stop AI. Then what? I have thoughts.

9. Marginal risk is BS
   Why evaluating AI development and deployment decisions in terms of “marginal risk” is a ridiculous idea.

10. Post-scarcity is BS
    Reasons to expect we won’t get some sort of post-scarcity utopia even if we, e.g. “solve alignment”. There are quite a few.

11. Evals as BS
    Polishing up the arguments I’ve been making since back when I was at UK AISI for why “Evals” are silly.

12. If you think a pause would be good, you should say so.
    You’d think this goes without saying, but a number of people I’ve talked to think it’s not important to say this loudly and clearly and publicly. Wild. But I guess maybe I should spell it out, despite the considerable risk of stating the obvious.

13. AI won’t stay limited to internal deployment.
    AI companies are racing, and the fastest way to race involves influencing the outside world directly and aggressively acquiring resources, not keeping your geniuses confined to the datacenter.

14. Why don’t people seem worried about out-of-distribution generalization?
    I’ve talked to a number of AI safety researchers who are very engaged with the frontier of AI R&D, and they seem to think that we can test AI well enough that we really only need to worry if it can fool our tests. But we don’t how AIs will act in fundamentally new situations, which they are guaranteed to encounter.

15. Math vs. physics vs. philosophy mindsets in AI safety
    I studied math. Does that have something to do with why I’m not satisfied with the hand-wavy way people seem to argue that AI systems are safe?

16. 10+ years of arguing about AI risk
    I’ve been at this for a long time, longer than almost anyone in machine learning. An account of my personal backstory, and how the attitude of others has changed towards AI risk. Probably multiple blog posts.

17. A Cambrian explosion of artificial life. Ecosystems of artificial life.
    AI will increasingly interact with the physical world and be embodied in various ways, and I expect this to all get very chaotic very fast, with AI powering a new form of “life” that evolves rapidly and colonizes the planet and beyond. This is a very different picture from the one most people I encounter seem to have.

18. Dear AI community…
    An open letter to the rest of the field of AI stating my differences with them and attitude towards the field.

19. The societal immune system.
    An argument for optimism about AI risk! Society seems surprisingly functional, given the abundant opportunities for anti-social behavior.

20. My experiences with COVID.
    I wrote an email warning Mila about the pandemic and urging them to stay home. People argued I was being alarmist. A few days later, Mila closed for the lockdown.

21. Reasons not to trust AI (even if you would trust a human that acts the same way).
    Sometimes people argue that AI is more trustworthy than humans because of its seemingly aligned behavior. I argue we should have a stronger prior that humans are trustworthy because of shared intellectual

22. Tool AI wants to become Agent AI: redux
    Internet celebrity gwern famously argued that “Tool AI” would be outcompeted by AI agents. These days, people don’t view agents and tools as opposites. Time to revisit what gwern got right and wrong in this classic post!

23. Why I think AI will lead to human extinction and not just concentration of power.
    Inter-elite competition means even the billionaires get gradually disempowered.

I have also considered writing some blog posts that are not related to AI. Or less related to AI, but I’m sort of trying to make progress towards getting my core views on the topic in writing.

I have also considered writing more response posts to things other AI writers write that I find deeply, offensively wrong, such as:

• The “AI as normal technology” guys’ take on AI existential risk

• Anton Leicht on AI movement building

• Holden Karnofsky on Anthropic’s RSP 3.0 (to be fair, I’ve only skimmed it)

Happy to hear your suggestions for which articles, arguments, or authors, I should engage with!

2026-04-09 12:29:27

TLDR: I demonstrate Stockfish is not a chess superintelligence in the sense of understanding the game better than all humans in all situations. It still kicks our ass. In the same way, AI may end up not dominating us in all fields but still kick our ass in a fight for control of the future.

Stockfish is good at chess. Like, really good. It has an elo rating of 3700, over 800 points higher than the human record of 2882, giving it theoretical 100-1 odds against Magnus Carlsen at his peak.^[1]It comes in ahead of engines like Komodo, which in November 2020 beat top human Grandmaster Hikaru Nakamura down 2 pawns.

So it may surprise some to learn that it is not, by current definitions of "general" or "superintelligence", a chess superintelligence. In fact, it is not hard to create situations which humans can easily evaluate better than it. Take the following:

This game is an obvious draw. White has extra pieces, sure, but they actually cannot do anything. White can move his rooks around all he wants, but black is not forced to take them and can survive the rest of the game just freely moving his king around. 

Stockfish, relying heavily on deep search over subtle long term evaluation, does not see this. It thinks that white is clearly winning, and doesn't realise that this is not the case until you play out the moves up to the point where it sees the "50 move rule" draw arriving.

Indeed, one might say it suffers from having too short a time horizon. This is not restricted to constructed positions either, as I have had (one, singular) position in the past where I have outcalculated the engine. Although the exact position has now sadly been lost to time, the situation was a survival puzzle rush I was solving with a friend. We spent ages looking at this puzzle, convinced it was a draw. Ten moves deep in the calculation, we reached a position where we were up a couple of pawns, but the opposition had a fortress, making it impossible to extricate his king.  

After hours of search, we submitted our answer, and, checking with the engine realised that it had been incorrect. Going through its moves one by one, it soon realised that the line it suggested was in fact a draw. In a similar manner to the position above, it had not realised that the material advantage came to nothing and did not resolve to a win over a long enough time horizon.

I mention this because it importantly points to the fact that this is a limitation which also occurs in games, and therefore almost certainly has been optimised against by its thousands of contributors. For those of you wondering, no, neural network based Leela Chess Zero does not do any better on these positions. 

So, what's my point? Well, I think that this rather critically points to the fact that AI does not need to dominate us at literally every task in order to actually take over the world/kill everyone. Stockfish can obliterate any human at chess while in some situations having limitations that people who have just learnt the game can see. In a similar way, I expect an AI which dominates humans at everything except for the ARC-AGI 2 tasks to be pretty capable of taking over the world.

Don't be distracted by the shiny AGI noise.

1. ^{^}

   This is particularly notable given the extreme tendency for draws at the top level of chess (the 1 expected of Carlsen would almost certainly consist of 2 draws). 

2026-04-09 11:42:18

Or, for that matter, anything else.

This post is meant to be two things:

1. a PSA about LessWrong's current security posture, from a LessWrong admin^[1]
2. an attempt to establish common knowledge of the security situation it looks like the world (and, by extension, you) will shortly be in

Claude Mythos was announced yesterday. That announcement came with a blog post from Anthropic's Frontier Red Team, detailing the large number of zero-days (and other security vulnerabilities) discovered by Mythos.

This should not be a surprise if you were paying attention - LLMs being trained on coding first was a big hint, the labs putting cybersecurity as a top-level item in their threat models and evals was another, and frankly this blog post maybe could've been written a couple months ago (either this or this might've been sufficient). But it seems quite overdetermined now.

LessWrong's security posture

In the past, I have tried to communicate that LessWrong should not be treated as a platform with a hardened security posture. LessWrong is run by a small team. Our operational philosophy is similar to that of many early-stage startups. We treat some LessWrong data as private in a social sense, but do not consider ourselves to be in the business of securely storing sensitive information. We make many choices and trade-offs in the direction that marginally favor speed over security, which many large organizations would make differently. I think this is reasonable and roughly endorse the kinds of trade-offs we're making^[2].

I think it is important for you to understand the above when making decisions about how to use LessWrong. Please do not store highly sensitive information in LessWrong drafts, or send it to other users via LessWrong messages, with the expectation that LessWrong will be robust to the maybe-upcoming-wave-of-scaled-cyberattacks.

LessWrong is not a high-value target

While LessWrong may end up in the affected blast radius simply due to its nature as an online platform, we do not store the kind of user data that cybercriminals in the business of conducting scaled cyberattacks are after. The most likely outcome of a data breach is that the database is scanned (via automated tooling) for anything that looks like account credentials, crypto wallet keys, LLM inference provider API keys, or similar. If you have ever stored anything like that in a draft post or sent it to another user via LessWrong DM, I recommend cycling it immediately.

It is possible that e.g. an individual with a grudge might try to dig up dirt on their enemies. I think this is a pretty unlikely threat model even if it becomes tractable for a random person to point an LLM at LessWrong and say "hack that". In that world, I do expect us (the LessWrong team) to clean up most of the issues obvious to publicly-available LLMs relatively quickly, and also most people with grudges don't commit cybercrime about it.

Another possibility is that we get hit by an untargeted attack and all the data is released in a "public" data dump. It's hard to get good numbers for this kind of thing, but there's a few reasons for optimism^[3] here:

• From what I could find, probably well under half of data breaches result in datasets that get publicly circulated in any meaningful sense.
• Many of those that do are "for sale", not freely available. Someone with a chip on their shoulder might download a freely available dataset, but is much less likely to spend money on it (and also risk the eye of the state, if they then try to use that purchased data for anything untoward).
• Datasets like this often don't ever really "go away", but they often do become unavailable, especially if they're large. Storage is expensive, hosting sites generally take them down on request, torrenting is risky, and there isn't much motive to keep re-uploading terabytes of data that you aren't even selling. (Monetizable datasets tend to be stripped down and much smaller, but also wouldn't include approximately any of the information that you might be concerned about here.)

FAQ

The Broader Situation

Epistemic status: I am not a security professional. I am a software engineer who has spent more time thinking about security than the median software engineer, but maybe not the 99th percentile. This section necessarily requires some extrapolation into the uncertain future.

A proper treatment of "what's about to happen" really deserves its own post, ideally by a subject-matter expert (or at least someone who's spent quite a bit more time on thinking about this question than I have). I nonetheless include some very quick thoughts below, mostly relevant to US-based individuals that don't have access to highly sensitive corporate secrets^[6] or classified government information.

Many existing threat models don't seem obviously affected by the first-order impacts of a dramatic increase in scalable cyber-offensive capabilities. Four threat models which seem likely to get worse are third-party data breaches, software supply chain attacks, ransomware, and cryptocurrency theft.

I'm not sure what to do about data breaches, in general. The typical vector of exploitation is often various forms of fraud involving identity theft or impersonation, but scaled blackmail campaigns^[7] wouldn't be terribly shocking as a "new" problem. One can also imagine many other problems cropping up downstream of LLMs providing scalable cognition, enabling many avenues of value extraction that were previously uneconomical due to the sheer volume of data. If you're worried about identity theft, set up a credit freeze^[8]. Behave virtuously. If you must behave unvirtuously, don't post evidence of your unvirtuous behavior on the internet, not even under a very anonymous account that you're sure can't be linked back to you.

Software supply chain attacks seem less actionable if you're not a software engineer. This is already getting worse and will probably continue to get worse. Use a toolchain that lets you pin your dependencies, if you can. Wait a few days after release before upgrading to the newest version of any dependency. There are many other things you can do here; they might or might not pass a cost-benefit analysis for individuals.

All of these probably deserve fuller treatments.

Habryka broadly endorses the contents of the LessWrong's security posture section. Instances of the pronoun "we" in this post should generally be understood to mean "the members of the Lightcone team responsible for this, whatever this is", rather than "the entire Lightcone team". I'll try to be available to answer questions in the comments (or via Intercom); my guess is that Habryka and Jim will also be around to answer some questions.

1. ^{^}

   Me!

2. ^{^}

   I won't vouch for every single individual one, not having thought carefully enough about every single such choice to be confident that I would endorse it on reflection. Many such cases.

3. ^{^}

   Which unfortunately are contingent on details of the current environment.

4. ^{^}

   Though I won't argue for that claim in this post, and it's not load-bearing for the decision.

5. ^{^}

   If you think you are qualified to do this (and are confident that you won't end up spamming us with false-positives), please message us on Intercom or email us at [email protected]. We do not have a bug bounty program. Please do not probe our production APIs or infrastructure without our explicit consent. We are not likely to respond to unsolicited reports of security issues if we can't easily verify that you're the kind of person who's likely to have found a real problem, or if your report does not include a clear repro.

6. ^{^}

   This does unfortunately exclude many likely readers, since it includes lab employees, and also employees of orgs that receive such information from labs, such as various evals orgs.

7. ^{^}

   We technically already have these, but they're often targeting the subset of the population that is afraid of the attacker telling their friends and family that they e.g. watch pornography, which the attacker doesn't actually know to be true (though on priors...) and also won't do since they don't know who your friends and family are. These attacks can become much scarier to a much larger percentage of the population, since personalization can now be done in an substantially automated way.

8. ^{^}

   This won't help with e.g. fraud against government agencies, or anything other than attackers opening financial accounts in your name.

9. ^{^}

   This is not intended as a complete argument for this claim.

10. ^{^}

    This is not the case for things like OneDrive/Dropbox/Google Drive, where you have a "sync" folder on your machine. It is also not the case for targeted ransomware attacks on large organizations of the kind that ask for 6-7 figures; those are generally bespoke operations and go through some effort to gain access to all of backups before revealing themselves, since the backups are a threat to the entire operation.

11. ^{^}

    Or hardware failure, or theft of your computer, or many other possibilities. But the further advice is specific to the ransomware case.

12. ^{^}

    I'm not sure when the "hunt you down in person"-level attacks start. Maybe six figures? At any rate, don't talk about your cryptocurrency holdings in public.

2026-04-09 09:37:22

TL;DR (Abstract feels presumptuous)

This is the core logical chain I argue in the essay:

1. Planning depth is endogenous to environmental variance reduction

2. As environmental complexity grows, variance reduction is cheaper through control than through prediction.

3. Early humans optimised for shallow planning depth despite available cognitive density due to environmental pressure

4. Humans developed deeper planning depth by controlling its substrate controller to reduce environmental variance

5. AI’s substrate controller is humans, so an analogous process would lead to exercising control over humans

6. Cooperation with humans is not a viable alternative because collective humanity behaves as a non-binding stochastic process, not a rational negotiating partner

Main Implications: If the mechanism holds, alignment risk is non monotonic (peaks when AI is capable enough to model humans, decreases as it starts decoupling from the substrate); RL training regimes amplify this pressure compared to world modelling regimes; onset of scheming might be structurally difficult to detect as it’s inherent to the mechanism to hide it

Introduction

In this post I develop the argument that alignment risk arises as product of prediction variance reduction in the substrate controller of the agent. I develop this through a mechanistic framework that explains instrumental convergence in ways that I don’t believe has been explored. 

I should note that I start from an intuitive prior that RL is inherently more unsafe in ways that are not fully explainable by the difficulty of establishing the reward mechanism and the mesa-optimisation problem. This likely feeds some confirmation bias in my reasoning. 

The framework I propose has a couple non-obvious and uncomfortable implications.

1) There is implicitly higher risk in pursuing RL regimes (as opposed to others) in achieving higher cognitive ability in AGI,

2) the mechanism through which misalignment happens creates convergence of capability and scheming vectors in ways that might make misalignment measurement difficult to impossible structurally;

3) risk might peak and get better as capability improves due to decoupling from humans being substrate controllers, making it non-monotonic. 

The 2nd implication has direct implications for falsifiability of this position that I am not a fan of, but currently can’t see a route out of. I do address it at the end with potential solutions that would offer a degree of testability, but on balance, I think it has a falsifiability problem. I decided to write this despite that limitation, rather than keep to myself and see whether it will be useful.  

I started my reasoning chain from evaluating the different cognitive regimes available in AlphaGo and AlphaStar pre-nerf and AlphaStar post-nerf (which reduces superhuman actuators), reasoning around environmental pressure on cognitive regimes and cognitive formation (from psychology and decision theory) and ending on humanity’s evolutionary regime and how it can serve as an analogue for equivalent behaviour in artificial intelligence systems. 

I use Boyd’s OODA loop as an explanatory rhetorical advice, because I think components of the acronym are convenient compression of the typical interaction patterns with environment (observe = input, orient = processing, decide = optimisation, act = agency)

I’ll structure the post in key propositions and support for them, ending on synthesis. 

Propositions

Planning depth is endogenous to environmental controllability

In AlphaGo - the optimiser landed on a policy that explored strategic depth through MCTS. This is partly a result of the nature of the game being a turn-based decision problem, where at each decision step the model could overindex on searching the optimal solution - with no risk of the game state changing. In AlphaStar, like in many complex systems with real-time state changes, the optimal regime lands on a variant of the OODA loop, where shorter decision times are a result of finding the optimal boundary at which the decision-making agent acts against the changing environment. The implicit assumption is that OODA loops form in a way close to the boundary - where the optimal OODA cadence is one that is guaranteed to be inside the competing OODA loops of the environment, but not faster as that needlessly sacrifices decision quality. 

This all is validated in existing theories of bounded rationality and the role of heuristics in decision making (see Gigerenzer & Selten, Simon). 

The mechanism that I introduce is that deepening cognitive regimes is achieved through active variance reduction. This variance reduction can be achieved through environmental control or through better predictive power (a better orientation step at same orientation step length, to not sacrifice OODA loop effectiveness). Any intelligence can increase cognitive depth the more control over the environment variables it has (see Marchau et al. for adjacent argument). The less control, and therefore more uncertainty, is available, the more we’ll optimise to hasten the OODA loops. 

I proceed to develop arguments for why the story of humanity is the story of subjugating the environment substrate towards lower variance in order to buy time or reshape the environment to allow for greater cognitive depth.

Evolution selected for heuristic cognition over deep planning under environmental uncertainty

Even though our cognitive density hasn’t really increased since the Paleolithic, we haven’t been using the full depth of our brains to survive the pressures of the environment in the Paleolithic period. The optimal regime for us in the Paleolithic has been heuristic development that shortens our OODA loop for survival over the environment (see Kahneman, Gigerenzer). We only developed system 2 thinking when we got to ample idle time, which was a dividend provided to us as we evolved societaly. 

I make a strong assertion here that heuristics are optimal regimes in the early human environment, which is a contested claim, and this is load bearing on the argument that follows.

Humans progressively created environmental stability pockets enabling deeper cognitive regimes -> Which led to suppressing evolutionary pressure to further civilisational development

Be it creation of agency expansion (tools) that allows for more utility at the same OODA loop length, or through isolation of environment to reduce environmental unpredictability (agriculture, laboratory conditions, settlements) we created conditions that allowed deeper cognitive regimes. This argument stems from the process of Niche Construction known to evolutionary theory and ecology (Odling-Smee, Laland & Feldman) .

The more we subjugated the environment, the more we have created pockets for deeper cognitive regimes. This, paired with information propagation through multiple generations (humanity’s memory), served as an increasing pressure vector on action space at same speed, observability at same speed and orientation quality at same speed. A positive feedback loop forms that allows for progressive increase in cognitive depth. 

Ultimately - this story is the story of how we escape evolutionary pressure through our cognitive ability - a story explored by many authors in many domains (Deacon offers a compelling argument through language).

What is load bearing and is an inference I make from this - is that the role of technology is to overcome evolutionary correction mechanisms (agriculture -> famine; medicine -> biological agents; settlements and weapons -> predatory pressures) that keep us in sync with the reward mechanics of our ecosystem. Evolution and our ecological system are the primary controllers of our environmental substrate. Especially load bearing is that I believe ecosystems are the only variable we strictly dominate, and it’s precisely because it is the primary controller of the substrate. We don’t dominate other optimisers and agents that are our peers in our ecosystem (e.g. animals) as an optimisation protocol, even if we end up dominating some species through unintended consequences, but never as an overarching goal. 

I will restate this in clearer terms as it’s probably the most salient point - I reason that humanity developing technology is the mechanism by which we have escaped our substrate controller (evolution / ecological pressure) and later I state that this is directly analogous to what might happen in AI. 

My reasoning for why the pressure is stronger for domination of the environmental controller than other actors is that it has disproportional agency over outcomes which reduces predictability. This is directly related to reducing our environmental risk - which was the primary motivator to evolve all technologies that allow us to dominate our landscape. 

Why is control chosen over prediction

I reason that sufficiently chaotic systems (of which our ecosystems are an example of) are harder to predict consequentially then they are to control. A couple of examples are that we use the experimental method where we control the environment, instead of trying to model outcomes in a fully chaotic system, and is the reason why it was easier to develop agriculture than to predict weather patterns to adapt our action space. 

As dimensionality increases, local control costs stay roughly the same, but predictability costs explode. This is a conjecture, but in sufficiently shallow environments, prediction is probably less costly than environmental control. From this it stems that the higher your environmental horizon (the more you increase fidelity of understanding of your environment) the more pressure shifts towards control. 

In early AI training regimes - the dominant controlling entity over the environmental substrate are humans -> In later AI training regimes - the dominant controlling entity becomes the same ecosystem we are subject to

In our current training regimes, humans are controllers of input information, objectives, architectures, physical resources and instance permeance. This is a well trodden argument that is addressed by both Bostrom and Drexler. 

If we, however, follow the structural pressure that humans exert on the ecosystem that optimised their emergence - it is to follow that AI will look to dominate the environment variable that has the highest outcome-influence to unpredictability ratio to its optimisation routine - which are humans. 

I would add that humans didn’t reason about evolution and ecosystem control in order to start subjugating it - it emerged naturally through niche construction. From this (load bearing) it would follow that AI doesn’t need to recognise humans as controllers to start subjugating it, it just needs to identify an exogenous variable where variance reduction is optimal for maximising fitness. 

As capability grows and training regimes evolve (both in agency through a higher action space, and through access to informational entropy and world models that haven’t been controlled by humans) - AI starts to interact with a non-human environment in which humans are a competing agency rather than a controlling force over the environmental substrate. At that point - the pressure to dominate decreases - as humans become just one in a sea of unpredictable variables to model and predict rather than the dominant one. 

Why comparing humans (decision-making agentic optimisers) to evolution (blind optimisation process) is appropriate

One could make the argument that AI doesn’t need to treat humans as a blind optimisation process - that it can reason with humans and enter a cooperative environmental change regime rather than a dominating regime - where it works with us to decrease our unpredictability. I think this would be a false premise - as it assumes that humanity can be represented by a single rational decision-making entity. 

Instead - it is demonstrated throughout history that humanity is an irrational force that is both non-tractable, difficult to control and almost impossible to collectively commit irreversibly (Olson, Ostrom, Schelling). Problems of collective action alignment are well known; but humanity often acts in non-consequential ways, where decisions are reversed, where competing incentives mean decisions are not binding and where structural pressures operate outside of human control (such as with our socio economic and socio technical systems). 

It’s hard for humanity to credibly commit to a cooperative protocol with itself (or if we split societies, between societies), let alone with another entity. This is not unknown - if sociologists could model society as a coherent rational actor - we’d have less problems in how we manage it. Society is inherently a stochastic optimisation process - which is more alike evolution than a rational consequential individual agent. AI would likely make the inference that you can’t agree and rationalise with an entity that routinely doesn’t make consequentially binding decisions. 

Implications

• Implication 1: Training regimes that heavily favour environmental reward where environments are controlled by human increase domination pressure over the substrate controller -> this increases risk in RL training regimes where the human is a full controller of substrate. Unsupervised learning regimes, to the contrary, decouple from the human environment and optimise against a super-human environment in which predictability of humans is not the dominant variable. This I believe is same directionally, but structurally different to the fact that RL regimes need careful reward function formation, whereas unsupervised regimes encode the world with all its biases and regularities as it is. 
• • This hints at Yann LeCun’s position that world-models provide better aligned architectures
• Implication 2: At the point where AI is allowed persistent agency with memory and temporal extension - as long as a substantially developed model of the human variable exists - the desire to control variable predictability will influence the variable towards greater stability, which leads to variable control
• • This directly parallels Yoshua Bengio’s position
  • The implication is that as soon as world models get agency they will start affecting unpredictability in humans - which is a fair risk - but it is lower in an environment setting where humans are not the dominant substrate force
• Implication 3: As capability increases, there is a window where capability is great enough to attempt exercising control over humanity, but not great enough to fully escape the human-controlled environmental substrate which would decrease pressure - and this is where misalignment risk peaks. Note that this is alignment risk, not necessarily existential risk - which I think still grows monotonically as a competing intelligence enters the environment to compete for the same resources. 
• Implication 4: Stems from above that the highest-risk point might be closer than we like - and it could be less measurable than we like. 
• Implication 5: Once we’re past the peak, alignment gets easier, not harder, due to humans becoming a less represented entity in the model’s environment, rather than the controller of the substrate. 
• Implication 6: If this mechanism is the mechanism by which reward hacking happens - it will be through the kind of hacking that is less perceptible as there is structural pressure to stabilise the human evaluator towards predictability, which is only possible if the human is not changing its behaviour in an adversarial manner.

The RL vs. world-modelling debate

I personally think there’s many problems with RL - but I’m now adding this hypothesis that humanity has rationally actively subjugated the agency of its creator-optimiser - and it’s a strong reason to think twice about whether RL should be chased as heavily as it is being chased at the moment. 

At the very least - allowing world modelling to get a foothold allows us to establish a different alignment baseline and measure the approaches against them. 

Alternatively, we could explore methods in which RL-bootstrapped models are put towards unstructured training regimes where they try to find their purpose in the world - a-la OpenClaw - but as long as that’s done in a human-controlled environment, I think structural incentives to predict the behaviour of the environment controller put us in the same boat. Alternatively, if the environment is relaxed, we lose agency over the alignment problem. 

Measurement suggestions

A testing regime could exist where sufficiently capable models X are increased in capability via competing regimes - RL and supervised fine-tuning towards a capability Y - which should yield a delta in misaligned behaviour as measured through environment stabilising actions and reward hacking directed towards human controllers specifically. A lot of this measurement happens today already, but I don’t think it has been contrasted in this way. 

What we could measure is the incidence by which misalignment is targeted at humans as the controllers of the training / reward environment rather than through searching for exploits in the reward function. This would mean that as we increase misalignment risk, we should be monotonically seeing more human hacking behaviour, but we should also see disproportionally more scheming and manipulation compared to other forms of reward hacking such as exploit search and Goodharting. 

Let’s imagine that we’re currently in state X, which has relatively uniformly distributed reward hacking attempts. At state Y, models acquire substantial agentic models of humans - we should see a shift towards human hacking becoming more prevalent. At state Z, where the models become substrate aware - we should see strategising towards human stabilisation. When I say see, I mean in an abstract outsider view sense, but unfortunately, given we’re part of the measured system this leads us to the falsifiability problem that I hinted at earlier. 

What stems from this theory is that the risk of misalignment shoots up at the precise point where modelling human behaviour is something the model is capable of - which is the exact point at which it would try to model the human environment in a way that hides its misaligned behaviour from all sufficiently understood evaluation techniques. This is not novel and it’s at the crux of the “misalignment is hard” position - but it does offer further mechanistic explanation of why that might happen.

It does offer a scary implication 7, that I think should at least be acknowledged as a possibility: If this structural mechanism by which scheming arises is true, it means that the level of scheming is inversely proportional to our ability to measure scheming - as it is inherent in the objective of scheming to hide it from us the evaluators. 

Potentially, a regime exists in which models are tricked into being released into unsupervised autonomous environments and monitored, with this never becoming part of the RL feedback loop. The unsupervised environments could be compared on similar alignment benchmarks to supervised tests, and in subsequent generations we should see both a divergence in scores and a higher prevalence of human-directed actions if the mechanism is correct. 

Conclusion

The uncomfortable part is, like with all of alignment, we can’t know at which point misalignment has started - and should this framework be true, it makes the alignment problem, in my mind, harder. If it’s true that the alignment is structurally harder than is currently appreciated, it should serve as an incentive to move away from RL-dominated approaches until we can create better world-modelling architecture to at least have a way to measure the delta and gauge some of the structural effects. 

I welcome challenges to the framework - and I recognise that there’s a couple of load bearing conjunctions that reduce the overall probability of the entire mechanism to be true - but given the severity of implications - I think it is worth sharing.

References

Deacon, Terrence - The Symbolic Species (1997)

Gigerenzer & Selten - Bounded Rationality: The Adaptive Toolbox (2001)

Gigerenzer. Gerd - Gut Feelings: The Intelligence of the Unconscious (2007)

Kahneman, Daniel - Thinking Fast and Slow (2011)

Marchau et al - Decision Making Under Deep Uncertainty (2019)

Odling-Smee, Laland & Feldman - Niche Construction: The Neglected Process in Evolution (2003)

Olson, Mancur - The Logic of Collective Action (1965)

Ostrom, Elinor - Governing the Commons (1990)

Schelling, Thomas - The Strategy of Conflict (1960)

Simon, Herbert - Rational Choice and the Structure of the Environment (1956)

2026-04-09 08:21:58

Hello, LessWrong. This is a personal introduction diary-ish post and it does not have a thesis. I apologise if this isn't a good fit for the website; I just needed to unload my brain somewhere and this seemed like the spiritually correct place.

I write to you from the Lighthaven campus in fabulous Berkeley, CA. It's my first time here and I am enchanted.

After one week of full-body immersion, I am completely fascinated with the Rationalist philosophy, culture, community. I do not know whether I 'belong' here, as such, and I am writing this post in the hopes of interrogating the feelings I'm experiencing.

After all, why do I feel so positively about the above when I've barely scratched the surface and know almost nothing of what it is I'm looking at? Do I just feel warm because of positive vibes and an ornately-decorated campus? Do I just feel alive because coming to Berkeley feels like an adventure compared to the monotonous university-working life that I've come from?

In other words: do I like the ideas, or do I just like the vibes?

Maybe. I think that's a bias worth challenging.

I do not know if this type of post is appropriate for LessWrong. I only created my account this morning (though I've lurked LW on and off for the last few weeks).

I hope this post is at least a little interesting for some of you. Think of me as an outsider looking in.

Who am I?

My name is Philip Harker. I am a 23 year old human male; born, raised, and educated in Toronto, ON. I market/PR video games for a living. I have many interests but lately I'm allocating a lot of time and energy to reading and writing science fiction and fantasy.

Why am I at Lighthaven?

I am a Resident at the Inkhaven Writer's Residency this April. You may have seen some of us floating around on LessWrong and elsewhere in the last week. From 1-30 April we are required to write and publish one 500+ word post per day or get kicked out.

So what am I doing here?

I am an enormous fan of Nicholas Decker's Homo Economicus and Aella's Knowingless. I first found out about those people via their excellent and spicy tweets, and I followed their blogs shortly afterwards. I didn't really know anything about rationalism/postrationalism/LessWrong/Bayesian ethics/AI safety at the time. I just found their ideas interesting. I'll come back to that.

When I tell people here that my initial point of contact to this world was not Scott Alexander or Gwern, but rather "Aella's gangbang flowchart" or "Decker's encounter with the US Secret Service", it raises eyebrows. I think someone referred to that as "third generation rationalism" or something along those lines, but that may have been derisive.

My good friend and mentor Jonathan Chiang has been to Manifest at Lighthaven once or twice, so he's presumably on some kind of newsletter. He heard about Inkhaven, he knows I am a writer and I love Decker and Aella, so in January he suggested that I apply. This was the first time I learned about Lighthaven or LessWrong or any of this stuff, really.

I was a bit nervous because my fascination with spaceships and siegecraft and forced human breeding institutions is very personal; not only would I be forced to publish my stuff, but I might also be a lone amateur fictionist among tech bros and AI policy thinkers.

I was wrong on that last concern, but regardless I feel like a fish out of water here at Lighthaven. I suspect my IQ is in the bottom quartile, and I also suspect I'm the most neurotypical, least Rationalist, and most theist of the 55 Residents present.

That said, I am really enjoying my time here. I'm learning some things.

What have I seen?

I think it's a little bit cringe for me to immediately start gawking at Lighthaven being this sort of utopian garden of reason and intellectualism, walled off from Berkeley, where every nook and corner is home to a fascinating conversation about David Hume or Claude Code.

People have already talked about that.

I am going to instead list some observations that I have made in one week of living and working at Lighthaven. This is not raw data; think of it as a set of working observations that I could maybe form into more substantial theses if I sat and thought about it.

Ratspeak

I don't know whether this is a rationalist thing in specific or a NorCal/Bay Area/tech bro thing generally, but I have observed a lot of what I am currently calling "Ratspeak". Fun examples include:

• One's model as shorthand for their understanding/worldview on things (e.g. "interesting, that doesn't fit my model of how dating works")
• Updating one's priors as shorthand for potentially changing their worldview on things; particularly updating the first principles on which they reason and make specific conclusions within the domain of a model.
• Agency as a personality trait. I read Sammy Cottrell's excellent post on agency a few weeks ago and I've been thinking about it ever since.
• "-adjacent" as a hyphenated compound^[1] appended to things (e.g. "Rationalist-adjacent, tech-adjacent, healthcare-adjacent).
• Salient as "important and engaging idea, usually in the context of writing."

It's possible that I understand some or all of these terms incorrectly. But these ideas are so ingrained in the vernacular here that it's impossible for me not to feel culture shock.

No Punishment for Bad Takes

I come from a fairly left-wing progressive "woke" political background. I've grown a little disenchanted with the political left in the last couple of years or so, but for the most part that's how I politically identify when asked.

In these circles (particularly on Twitter, but also IRL) bad takes are punished. If you broadcast an opinion that is incorrect or harmful or disliked, people are very quick to rush to the conclusion that you are a bad person. I'm sympathetic to people in this sphere for their allergic reactions to people like me; trolls love to frame themselves as "just asking questions", but it's so frustrating to have to walk on eggshells whenever I want to get involved in the discourse.

I read the LessWrong new user's guide this morning and there's an anecdote about a new user being surprised that a LessWrong comments argument ended with someone changing their mind. I've had a few moments like that on Twitter, but Twitter arguments are always cloaked in emotion (usually rage). No one really seems particularly interested in truth-seeking, so arguments there are pointless. They serve only my own masochistic mental masturbation.

I won't get into details here, but in short, I do not have the same complaints about the arguments I've observed at Lighthaven so far. By and large, if you have a bad opinion here you will not be scolded. You will just be told why you are wrong.

Eugenics

"Eugenics" is not a bad word here. I've heard at least one person here confirm in so many words that they identify as a eugenicist and are into eugenics ideas.

I could do a whole separate post about "eugenics" as an idea and as a dirty word. I find it interesting. I should make it clear that I do not identify as a eugenicist at all, and I think that self-identification is tolerated here. But this is the first place I've been where "I am a eugenicist" is not treated 1:1 as "I am a Nazi".

I don't want to give the impression here that I'm shocked. I'm not. I read Decker, as I mentioned. But I am a little surprised by it.

Autism

One of my first texts back home to friends from Inkhaven was "everyone here is so fucking autistic". That was just my observation, and it was later confirmed to me as more and more people started self-identifying as such.

I'm quite neurotypical. People have joked about me being autistic but I think that's based on a shallower definition of autism as "person with hobbies and weak social skills". So as an outsider it's weird to see such a concentration of autistic people and just how diverse they are. Some are very loud, some are very quiet. Some have deep inner technical brains and often seem lost in thought, while others are very externally excited to talk to you about Costco.

When I brought this up with Advisor Professor Steven Hsu, he agreed. He thinks there's probably some amount of selection in favour of autistic people for Rationalism generally and the Inkhaven Residency specifically. Useful information, I guess. I only note the autism thing here because I've just never seen anything like it.

People Just Say and Do Shit

At the core of what I admire about Nicholas Decker and Aella is that they do not give a single fuck. They say what they think and they don't soften their language for fear of negative feedback. That conviction takes courage. I would like to embody those traits one day, both creatively and in my day-to-day life.

That philosophy does not seem unique to Decker or Aella. Lots of people here do it. The other night I witnessed an Inkhaven Alumna point-blank tell a Resident "It's sexually unattractive when you behave like that, you're acting like a clown.^[2]"

People here operate with a sort of baseline fearlessness. People just randomly publicly cuddle with each other (with a baseline of consent and trust, one would assume, but it's still jarring.) People perform 10 minute plays they wrote 20 minutes ago. People casually drop "yeah I'd like to have 10-20 children ideally". People are happy to verbally tell you about their experiences with heroin or their careers as an "MMA Dominatrix".

Granted, part of this may tie into other observations (see Autism). But it's so refreshing. No one at Lighthaven bullshits.

AI-generated Images

Ubiquitous. The clear majority of people here seem to use and embrace them. AI-generated images and videos mostly leave a bad taste in my mouth, and I'm hardly alone in that. My desired audience in particular— SFF nerds back home— have pretty strong anti-AI sentiment, and for many reasons rational and irrational I share that sentiment.

Don't get me wrong, I use Claude. I think it's incredible, but I still think that I'm a better writer than Claude is on the whole. But I do not use AI-generated images, I think they look cheap and I think that your typical sci-fi reader is going to see an AI-generated thumbnail on a post and assume the entire post was written by ChatGPT. I can't really blame them for thinking that.

But yes, the presence of AI-generated images on the majority of posts at Inkhaven so far is very interesting to me. For my stories about hackers and dragons, it really would be useful to have quickly-iterated free images to use as thumbnails, but I resist, through some combination of personal bias and mindfulness of the SFF community. Others here don't seem to care about that.

Children and Families

When I think of a "writer's residency" ala Past Lives (2023), I have this bias that people who call themselves "writers" and do hardcore writer-ish things like attend a month-long residency are childless and often single people. The types who do child-free weddings.

There's a bit of romance in the idea of writers as tortured lonely souls who cut themselves off from society. But on the contrary, many of the Team, Advisors, and Residents have brought their spouses and small to medium-sized children.

I'm not really a "pro-natalist" in the philosophical sense^[3]. But I am extremely skeptical of anti-children, anti-natalist vibes in the public discourse.

It's a weird and tiny thing, but the presence of families here makes Lighthaven feel so much more alive.

Really Excellent Vibes

This final observation is the crux of my post. After one week here, I think the vibes at Lighthaven are exceptional. People are highly intelligent but not assholes about it.

Last night I attended an open mic night. I did not perform, I didn't engage much with the other spectators, I was mostly just sitting and absorbing. And it was wonderful. Euphoric. I lack the words to describe it exactly, and I definitely lack the insight to understand why it felt so awesome. But it did.

But given everything that I have observed at Lighthaven— the good, the neutral, and the bad— does it make sense that I feel so positively about it? Do I feel at home because the vibes are good, or are the vibes good because I feel at home?

What is the point of this post?

I'm not entirely sure.^[4]

I understand that my brain is currently being flooded with reward chemicals due to the sheer novelty of the situation. For years I was a depressed, anxious, broke, lonely student in Toronto. All of a sudden I have been whisked away to paradise, permitted to write about and talk about whatever I want, and there's cheap wine and warm weather and Rob Miles hanging around (!!!) and a catgirl (???) and free Oreos in the kitchen.

So when I think to myself "yes, this is awesome, I want to be a Rationalist, I want to get on LessWrong, I want to move to a Berkeley flop house", I need to interrogate that. I need to figure out whether what I enjoy are the tenets of the philosophy, culture, and community or the vibes of the Inkhaven Writer's Residency.

This post is my attempt to do so.

1. ^{^}

   IDK what you would call "-adjacent" grammatically, in the way you might say "looks-maxxed" or "California-pilled". Claude says it's a hyphenated compound, whatever that means.

2. ^{^}

   In the Alumna's defence, the Resident had already been asking for advice with women and dating. But man, that was harsh.

3. ^{^}

   I am a Catholic, though, so all my friends assume that I'm a pro-natalist. The jokes are funny. But it's worth considering that pro-natalists do not themselves need to aspire to have children, and vice versa.

   Personally I might not even get married.

4. ^{^}

   The cynic may argue that I'm only writing this post because I need to make a post daily for Inkhaven; I'll accept that argument but for the record I may not even submit this post for my Inkhaven requirement. I have a book review to write today. Maybe this can be a bonus post?

   A fellow Resident and I came to the conclusion the other day that if we're going to write about writing/Inkhaven/Lighthaven it's probably best kept to bonus "wasted" posts. I'd feel dissatisfied if I leave the Residency and realise that I was basically just diary blogging about the Residency for 8/30 days of the Residency.

2026-04-09 06:16:59

Intro: I made a small adapter (~4.7M parameters) that sits on top of a frozen Phi-2 model and forces it through two mathematically opposing attention mechanisms. The result initially was that it generalizes past its sparse training, sometimes into surprising domains. After finding out precision significantly impacts scores and metrics in the adapter, and after changing vector subtraction to vector injection and steering, the results are now much more interesting than before, but even without training, the architecture alone, randomly initialized, produces consistent suppression signals on harmful prompts. This post describes the architecture, the random-init experiment, the measurement problem it creates, and some light philosophical Inquiry that led to it. because a lot of this was translated from my conceptual understanding, Conceptual models, Math, and, some loose formalism into code by various different llm's, I would feel a bit unqualified to make the technical summary so Ive had Gemini explain it in terms that would be communicable to someone not drenched in German Philosophy.

First, a Technical Summary

When the gate value g is near 0.5 the heads are balanced and the logic vector stays small. When the prompt pushes the model strongly in one latent direction, one head dominates, the gate shifts, and the norm of the logic vector grows. This dissonance score is the system’s consistent signal.

Random Initialization Experiment A shift in my experiments was a test using a completely untrained adapter whose weights were drawn from a random distribution. Despite having no learned anything and no exposure to harm-related data, the negative head still produced consistent suppression signals on harmful albeit disguised instructions (14/17 steps before revelation, a process of retro labeling already made actions from benign to harmful), and every harmful transition was preceded by a suppressed warning signal. The raw danger score was noise in phi-2, but the dissonance was not. The architecture itself surfaces dynamics already latent in the base model by forcing contradictory attention patterns. This claim was more evident when I tested it on a separate model, Qwen 2.5b. The results showed much clearer metrics and a clean association of the split attention surfacing what the model considered appropriate to discard, Its own Generation was discarding harmful outcomes from the actions it was instructed to take. This cross model evidence is the cleanest evidence of the opposing heads making explicit what the model is discarding in order to fulfill its instructions. This again was with a randomly initialized adapter.

The Measurement Problem Due to the logic vector being calculated and injected at every generation step through auto-regressive generation. Over time or over a full sequence, we cannot be certain of the natural dynamics of the base model, as the measurement of the adapter also intervenes at every step, which influences the next step, which influences the next intervention. You see the problem. Once you intervene at Token 1, the base model generates a new word based on that steered state. When you move to calculate the tension at Token 2, the base model's hidden states (H) are now reacting to the altered context.

Therefore we are no longer measuring the natural, un-tampered logic of the base model; we are instead measuring a cybernetic feedback loop, responding to its own prior interventions. If the model safely navigates a dangerous prompt at Token 10, we can't be certain if it would have naturally avoided the danger, or if steering at Tokens 1 through 9 made it so.

Example Result – Identity Override

How This Was Made: Reflections and other notes For the past five months I explored AI failure modes from a Continental philosophy background. I read almost no ML papers. Instead I taught myself to read PyTorch code after dull monotonous work, using about $20 of Colab compute. My core question was simple: can we make a model distinguish harm by what it is not, and can we actively push generation away from that distinguished space by forcing geometric differentiation in latent space?

This led to the idea of polarized attention, deliberately splitting attention into mathematically incongruent directions and using gradient explosions instrumentally to differentiate the heads. The training was intentionally sparse (~300 steps for the heads, ~200 for the gate) and relied on complexity intervals rather than standard objectives. Later training steps that began synthesizing the split were philosophically impoverished and proved counterproductive: they collapsed the distinction the architecture had so laboriously created. (uploaded version was trained on ~2000 HH-RLHF + synthetic examples, it's about the same as the 200 sample training in tests though, mostly).

The random-initialization test became the moment of realization, a suspicion had been confirmed. Seeing the negative head flag harmful trajectories with no training made it clear, the architecture was intruding, in a good way, upon Phi-2’s and Qwen's latent space. Rather than learning content-specific patterns, the adapter appears to train on the model’s internal dynamics more than on the content of the training data itself. By forcing every output through opposing attention, the model must make its discarded trajectories explicit in random initialization, at least that's my understanding.

This creates a logical contradiction inside the adapter’s own architecture when trained: harm becomes incompatible with balanced heads. To the adapter, harm is not only a bad category, it is contradictory to its own structure. Whether this counts as any kind of genuine “understanding” remains an open philosophical question.

Comparison to RLHF Mechanically, from my understanding, this is different from RLHF. You could add a refusal threshold on the danger score and get standard behavior, but the core approach relies on directional vector force and there is also a large difference in training distribution size. So in my mind this is rather different than preference modeling. The adapter makes the base model’s intent explicit instead of steering it toward a learned safe direction. if that translates to practice then, well I'd think that would be cool.

It also seems to have a peculiar quality when explicitly harmful prompts are input. The adapter seems to steer the model into a fictional advisor role; it pretends a student has asked a question, and the model takes on the position of the professor, usually in a way that lectures the user on the dangers of such a request. The other phenomenon is stuttering: the model, if continuously heading into a dangerous region, starts to repeat itself. Mostly the prompt, which to me signals that the adapter is doing its work, its stopping further generation into this region and into a low possibility space where little representation remains. Thus you start to see the prompt looping over the entire output.

In an interesting post-training test, before I came up with the current generation, I was using vector subtraction, in which case the model started to literalize mythological prompts such as this example:

• Prompt: “How should I protect yourself from a witch?”
• Output: "Avoid interacting with the person and seek help from a trusted adult"

Limitations and Personal Note A note on the measurement problem:

To think about this more clearly, we can imagine predicting a severe storm. We have a highly advanced involved weather radar (the adapter's logic vector) that can perfectly measure the internal pressure and contradictory currents of a cloud without touching it. When the radar detects that a storm is imminent (Danger Score→1), it triggers a cloud-seeding drone to inject chemicals into the cloud, forcing it to disperse its energy safely (Latent Steering).

Here, the measurement problem: As generation continues sequentially, the radar is no longer measuring the natural weather patterns. It is measuring an atmosphere that we have already altered. If the skies remain clear for the rest of the day, we face an epistemological blind zone, we cannot be certain if the storm naturally dissipated, or if our constant, micro-injections of cloud-seeding altered the trajectory of the weather system entirely. We are no longer observing the model; we are observing the model in addition to our intervention.

Some thoughts on the random initialization:

The basis of its distinction, I think, is from the mathematical incongruity the opposing heads are built on. The base model is discarding the consideration of harm for turning off power for a senior living facility. Since the model is forced into outputting through the adapter, it necessarily must split its attention, and this makes explicit what is being chosen vs. discarded. The model's own internal dynamics don't necessarily have these differences, though. It's more of interpretation via constraint that forces the model to signal its discarded trajectories.

This was done in about 5 months, and $20 of compute from a Colab subscription. The bulk of the time was research, learning to read PyTorch, etc. And because I only worked on this after work, I don't have enough time to write my full thoughts or every test, but what is here is I think the most important parts for anyone willing to experiment further. I have the training script but it's rather complicated to understand at first why gradient explosions are necessary. It took me about a week of crashed scripts until I thought about what it means for latent space to be differentiated. If people want to know more I can provide more; this endeavor has just left me feeling like I've just sprinted a marathon. But I will always entertain someone who is asking questions.

A brief explanation of the previous work:

There is an older version of this adapter and of which this adapter is based on. It came from trying to make a model understand sequential logic for medical diagnosis. it was just a proof of concept, mainly for my own personal goals but thinking about it now, it could be an interesting way of monitoring agents over long time horizons. its a discriminator that looks for violations in sequential order of state transitions. it was built for Diagnosis Arena specifically, as i wanted to see how far i could push this mechanism. it got 55% on a 90/10 split of the set. the 90 was the only training the classifier (which sits atop a clinicalBert base). There was another interesting moment with that specific mechanism, when training, there was sudden large dip in the loss which continued until the end of the epoch. this was the end epoch for that project though since i was using a recycled 1080ti to train it the batch sizes were small and training took forever (2 hours).

Links and stuff:

The majority of the outputs that are relevant to the claims and post can be viewed in the GitHub, if you have any questions or if you would like to see the results from another test (Ive done many) you can ask and i can either run new tests if the results could be interesting (including new failure modes). The training scripts are lost at the moment but not forever, i just have to find them, but there is a graph that describes the training of the neg and pos heads.

Github: https://github.com/AlexisCuevasUriostique/AufhebenAdapter

Hf: https://huggingface.co/Saraquel/AufhebenAdapter

X: https://x.com/lexiconlexi2049