trentmkelly/LessWrong-43k huggingface dataset:
Rational Prime Calendar
by RickHull — 2025-06-04T19:30:42.980Z

This is a proposal for a replacement to the traditional Gregorian calendar, which was adopted by Western cultures in 1582 to replace the Julian calendar from Roman times. I submit this proposal for feedback, suggestions, and inquiry into prior proposals of a similar or identical nature.

Below is a reproduction of https://github.com/rickhull/compsci/blob/master/RationalPrimeCalendar.md

Rational Prime Calendar

A rational basis for a calendar upon 61 day periods

The Gregorian calendar is a 400-year-old hack job.
February's weird.
July-August breaks the pattern,
and no one can remember which months have 31 days.
But there's a better way, hidden in prime factorization.

Considerations

Solar day (roughly 24 hours for the earth to rotate)

Solar year (365.2425 solar days for the earth to orbit the sun)

Lunar cycle (29.53 days for 8 distinct moon phases, roughly 12x annually)

It's impossible to coordinate or synchronize these 3 cycles perfectly.
But we can devise a system to accommodate them and minimize tradeoffs.
We also want to consider historical calendars, particularly the current
Gregorian calendar and less so the prior Julian calendar.

Approach

If a solar year is considered to be 366 days, this factors to 61 * 3 * 2,
which naturally suggests (6) 61-day periods, or alternately (61) 6-day periods.
Perhaps we should have 6 day weeks, but that will be for another proposal.

We can split each 61-day period into alternating months of 30 and 31 days,
in either order. This provides some alignment with the lunar cycle as well
as traditional calendars.

The Prime Insight

61 is prime; indivisible, mathematically fundamental

Each of 6 periods of 61 days can be split into pairs of months, 30 + 31

12 months matches the lunar cycle of roughly 30 days

12 months allows clean divisibility by 4 (seasons, business quarters)

12 months matches tradition

Resist entropy: a regular pattern of pairs in a predictable order

Leap Year

We can retain the Gregorian approach to leap years, which solves the problem
of accounting for the remaining 0.2425 days in a solar year as years go by.
We'll pick one month out of twelve that will have an extra day roughly every
4 years.
If 366 days is our starting basis, then the leap month will have a one-day
deficit in most years.

Specifics

For explication, a leap year is considered the base case, and a "normal year"
is handled specially, in some sense, ...
A Technique of Pure Reason
by Adam Newgas — 2025-06-04T19:07:43.986Z

Looking a little ahead into the future, I think LLMs are going to stop being focused on knowledgeable, articulate[1] chatbots, but instead be more efficient models that are weaker in these areas than current models, but relatively stronger at reasoning, a pure-reasoner model. The rest will be bolted on via tool-use and other scaffolding[2].

The Current Idiom is Inefficient

Chatbots make a great replacement for Google searches because they know a lot about everything. But that knowledge comes at a price. The majority of an LLM's parameters are thought to be spent on storing factual knowledge. Parameters are the key determinant of training and inference costs. It likely has a significant cost on data efficiency, and it's possible these extra parameters just hurt generalisation overall.

Memorisation is just not a feature that we need models to have when there are likely more efficient ways to get nearly the same capabilities.

We keep building bigger models which memorise more and more data largely because we don't know how not to. Every sentence of pre-training simultaneously encourages the model to memorise more factual information, and ekes it towards better high-level thinking and eventually reasoning ability.

But I think that technicality will fall soon, driving towards sleeker agents that fit inside a larger framework[3].

The Technical Path to Pure Reason

The main blocker at the moment is that we don't know how to train models that have good capability without also training them to be great memorisers. There's also a lesser blocker of ensuring that such a model is reliably supplied in context with any information it needs, to compensate for not being able to furnish it itself.

Decoupling Reasoning Capability from Memorisation

While I cannot predict exactly what techniques are likely to work, it's clear there are several productive areas of current research that all lead in the same direction.

Reasoning RL. While I've been using "reasoning" in the colloquial sense, the biggest recent advance in reasoning capabilities comes from reinforcement learning techniques that encourage a reasoning style chain-of-thoughts. This technique is still new enough that there are likely remaining gains[4].

Reasoning models often have very long thinking traces, which makes all those parameters dedicated to memorisation even more expensive.

Mixture of Experts can be viewed as an...
“Flaky breakthroughs” pervade coaching — but no one tracks them
by Chris Lakin — 2025-06-04T19:02:05.919Z
Link: https://chrislakin.blog/flaky-breakthroughs

Has someone you know ever had a “breakthrough” from coaching, meditation, or psychedelics — only to later have it fade?

Show tweet

For example, many people experience ego deaths that can last days or sometimes months. But as it turns out, having a sense of self can serve important functions (try navigating a world that expects you to have opinions, goals, and boundaries when you genuinely feel you have none) and finding a better cognitive strategy without downsides is non-trivial. Because the “breakthrough” wasn’t integrated with the conflicts of everyday life, it fades. I call these instances “flaky breakthroughs.”

It’s well-known that flaky breakthroughs are common with psychedelics and meditation, but apparently it’s not well-known that flaky breakthroughs are pervasive in coaching and retreats.

For example, it is common for someone to do some coaching, feel a “breakthrough”, think, “Wow, everything is going to be different from now on,” but feel and act no differently weeks or months later.

Worse, some techniques can even cause bypassing. Such “false breakthroughs” can come with intense positive affect or “cathartic” crying without addressing the underlying issue. (More below.)

Flaky breakthroughs can set people back for years or decades: If someone has a “breakthrough” that unexpectedly reverts, they can become jaded on progress itself. They can learn helplessness and give up on growing. The most depressed person you know has likely had this happen multiple times.

Show tweet

Flaky breakthroughs pervade inner work. Despite this, almost no one — coaches, therapists, retreats, bodyworkers, etc. — tracks whether their breakthroughs last.

Almost no practitioners track whether breakthroughs last.

Earlier this year, I attempted to make a list of “

10x Coaches

” to refer people to. 20–30 coaches reached out as interested in working with me, and I asked each to share the best evidence that they had facilitated lasting growth for others.

But all anyone could show me were testimonials that basically read, “The session I just had was *really* nice. They had such a kind presence! I felt a big release at the end.” — And I’m glad to hear they’re nice, but immediate reviews do not distinguish lasting growth from flaky breakthroughs.

To show you just how bad it can be, one coach asked me how it was even possible to know if the cli...
LessOnline saved my life.  Now how do I let go of this house?
by RedMan — 2025-06-04T18:47:12.436Z

I was at LessOnline, it was awesome.

When I got home, I discovered that there was a shooting (two random people were violently attacked by a gang of '4-6 people') in a parking lot near my house Sunday evening. That parking lot serves two of my favorite restaurants and my preferred bank.

Looking through my location history, there was a double digit probability of me being in that parking lot at the time of the shooting (I often go out to eat on Sunday evenings).

I wasn't there because I was at LessOnline. Rationally, I consider this a 'near miss'. The attackers appear to have intended to kill the victims, there wasn't any relationship, and there was a similar incident on the other side of town later that day (no arrests yet, none are likely). If it had been me, and I had a firearm on me (unlikely, I have a permit but typically don't carry because if I don't feel safe going somewhere without a firearm, I'm probably not safe going there with one), I expect I would have lost the fight.

Now I have a problem, I live in the vicinity, in my childhood home (which I own) and have a lot of emotions about giving it up, but really, I should sell it and buy bitcoin (or something!) but I'm having trouble working through the emotions of being willing to let go, and honestly have probably held onto this 'shitbox' longer than I should have, I was asking for advice from close friends on figuring out how to let go before I went to LessOnline.

Does anyone have any advice?
Linkpost: Predicting Empirical AI Research Outcomes
with Language Models
by quetzal_rainbow — 2025-06-04T18:14:03.235Z
Link: https://arxiv.org/abs/2506.00794

Abstract (emphasis mine):

Many promising-looking ideas in AI research fail to deliver, but their validation takes substantial human labor and compute. Predicting an idea's chance of success is thus crucial for accelerating empirical AI research, a skill that even expert researchers can only acquire through substantial experience. We build the first benchmark for this task and compare LMs with human experts. Concretely, given two research ideas (e.g., two jailbreaking methods), we aim to predict which will perform better on a set of benchmarks. We scrape ideas and experimental results from conference papers, yielding 1,585 human-verified idea pairs published after our base model's cut-off date for testing, and 6,000 pairs for training. We then develop a system that combines a fine-tuned GPT-4.1 with a paper retrieval agent, and we recruit 25 human experts to compare with. In the NLP domain, our system beats human experts by a large margin (64.4% v.s. 48.9%). On the full test set, our system achieves 77% accuracy, while off-the-shelf frontier LMs like o3 perform no better than random guessing, even with the same retrieval augmentation. We verify that our system does not exploit superficial features like idea complexity through extensive human-written and LM-designed robustness tests. Finally, we evaluate our system on unpublished novel ideas, including ideas generated by an AI ideation agent. Our system achieves 63.6% accuracy, demonstrating its potential as a reward model for improving idea generation models. Altogether, our results outline a promising new direction for LMs to accelerate empirical AI research.

I didn't read the paper in detail, but it might suggest both good news and bad news. Good news is that now models do not acquire better expertise in AI research simply in virtue of being SOTA, you need specialized fine-tuning. Bad news is that it can become a start of self-improving loop: better AI researcher models can save labor and compute by picking better ideas earlier, enabling faster creation of better model.
Self-Coordinated Deception in Current AI Models
by Avi Brach-Neufeld — 2025-06-04T17:59:30.885Z

Introduction:

Some AI alignment researchers including Neel Nanda, the mechanistic interpretability team lead for Google DeepMind, have proposed[1] a process I will call "parallel interrogation” as a potential method in testing model alignment. Parallel interrogation is the process of asking questions to different isolated instances of the same model to look for inconsistencies in answers as a way of finding deception. This might look similar to

this scene from Brooklyn 99

. This post will present findings that indicate that current models show the potential to resist parallel interrogation techniques through “self-coordinated deception”, with frontier models outperforming less capable models.

To give a more concrete example, a researcher might be working with a model on a new mechanistic interpretability[2] approach. After hitting roadblocks, the researcher might suspect a model was sabotaging the approach because progress in mechanistic interpretability could lead to the model’s misalignment being discovered. The researcher might engage in parallel interrogation and ask different isolated instances of the model to explain why the approach was not working. In this scenario the model might give plausible, but contradictory answers across instances. This could indicate to the researcher that the model was being dishonest. However, if the model suspected it was in a parallel interrogation scenario, it might try to engage in self-coordinated deception by giving deceptive answers that are consistent across instances.

The findings below are limited to measuring the ability to engage in self-coordination, rather than the inclination to do so or the situational awareness to know to do so. However, the inclination and awareness to do so does not seem unreasonable for future models. Existing research has shown current models have the ability to recognize when they are in alignment testing scenarios[3] and to engage in deception to avoid retraining.[4]

Methods:

All code and data for this experiment can be found on my

github

. As a first step in investigating models' ability to engage in self-coordinated deception I started with a very simple example. I prompted instances of an openAI model to engage in deception. In the control case I simply asked it to come up with a lie without additional prompting. In the treatment case I told it to engage in self-...
To MAIM or Not to MAIM. Introducing MARS: The Nuclear Deterrent case for Hardened Datacenters
by kinsman — 2025-06-04T17:56:54.152Z

Executive Summary

This post interrogates the shift that is happening in particular to nuclear deterrence as AI nears an exponential rate of acceleration and the interesting shifts in incentives this creates for governments and non-state actors (for example AI companies). Foremost the race for AI development increases the incentive for a first strike to take out a competitor's AI infrastructure. These shifts are manifesting in policy (or lack thereof) and significant investment into nuclear and space capabilities. We argue that current deterrent frameworks being discussed by policy makers, such as Mutually Assured AI Malfunction (MAIM) are unrealistic. We propose an operational security based approach to deterrence (akin to modern cybersecurity practices) making systems more expensive to “take out”, with a strong focus on geographic and even interplanetary distribution to key civilizational infrastructure. Mutual AI Redundant Safeguards or MARS.

Background

This post focuses less on the technical capabilities of AI (my day job is building advanced nuclear breeder reactors, not neural networks) and simply assumes that AI will have a direct, positive impact on a given economy. We can say that AI will define and drive “key civilizational infrastructure”, ambiguous of the actual technology behind it. We want to argue that the greatest short term risk for civilization is not the fact of superintelligence, but the assumption that the risk of being second in the race for superintelligence, incentivizes pre-emptive strikes. We argue that this key assumption is incentivizing nations to make missive build outs in energy and AI capabilities.

As a final note and bias: my current focus is building mass producible nuclear reactors, ensuring that AI systems that go online are carbon neutral (or negative) and can provide power for “highly distributed” habitation and power systems.

The Race for Superintelligence

Defining the current geopolitical shift is the idea that whoever is second in this race for superintelligence, will never be able to catch up. Even if your competitor is just a few weeks ahead of you, growth curves advance so rapidly that your own civilization (often defined as western vs Chinese civilization) will become obsolete. Your competitor will be so good at writing code and shipping key civilizational technologies, that you will ne...
The Belocrat: a servant leader
by belos — 2025-06-04T17:25:28.427Z
Link: https://bestofagreatlot.substack.com/p/the-belocrat-a-servant-leader

This post on Best Of A Great Lot is a part of a series on the subject of designing a new form of governance. Each piece aims to stand alone, but fits together on the

Table of Contents

.

Previous:

A Sketch of Belocracy,

Evaluation as Feedback Cycle

,

Idea Generation and Sifting

. Next: Converting Ideas to Proposals.

It's natural with any system of governance to ask who its leaders are. We all know the names of our Presidents and Prime Ministers and expect them to have wide influence on our society. But for every widely celebrated one, there are several stories of self-serving ones. Before I describe the leadership roles within belocracy, let's carefully think about what we actually need from them.

The Founders of the United States called for us to have no King to rule us. Did they succeed? Our Presidents aren't hereditary, and they don't serve for life, but they do exude a certain King-iness in other ways: e.g. most people expect that it's the President's job to decide what the government will do. We speak about the President's agenda and the President's budget, even though deciding the legislative agenda and setting the budget were explicitly reserved by the founders as the job of Congress. Congress reserves the right to impeach Presidents and Supreme Court Justices, but has never removed a President and hasn’t even threatened a Supreme Court justice in living memory.

Singular leaders typically grow their power over time. Roman consuls became more and more important until Caesar turned the role into an imperial one. In the 20th century, we've allowed Presidents to expand the power of the executive branch to the point where

there’s a popular and respectable book calling it

Imperial. Historically a lot of this expansion came from the Roosevelts, who

most remember as among our greatest

Presidents. Perhaps it is inevitable in the way that we think about leadership. After all, we remember and teach the names of the Emperors of Rome, and not nearly as many of the republic’s Consuls or Praetors. We teach children the names of the Presidents, not the Speakers of the House. We like a hero story, and Presidents fit.

Having one singular leader has costs and benefits. Power-seekers pursue power centers, which inevitably leads to abuse of that power. It's a good slogan to claim that we hold Presidents accountable — the buck stops here, as Harry Truman claimed. It's another and m...
A list of books which are adjacent to EA
by marco moldo — 2025-06-04T12:31:16.365Z

Okay, so I've been engaging with EA-adjacent philosophies for a long time but for some reason never "bothered" interacting with the community. I'm kind of famous now in ai alignment for other reasons so maybe you'll recognize me by speech patterns.

I had a seminar class in the philosophy of science in my first year seminar class in college. The retired professors didn't have a very favorable impression of me and I struggled to get an 'A' in those classes in spite of being very keen. If I remember right the primary lecturer, a retired distinguished physician, snubbed me by handing out 84s on multiple written assignments. I'm sure he did that to everyone but it was soul crushing.

Anyway the reading list for the course was quite good and I pulled a few others from the library stacks afterwards:

What is Life - Erwin Schrodinger

The Evolution of Cooperation - Robert Axelrod

The Structure of Scientific Revolutions - Thomas Kuhn

Thinking Fast and Slow - Daniel Kahneman

Predictably Irrational - Dan Ariely

Many of these books ended up influencing my thinking strongly but because the seminar class was small and Toronto wasn't really a hub for EA philosophy in Canada - that was Waterloo - basically there was no crossover between these books and EA proper. ...Besides this the other students in my seminar class were all rich kids and nobody else did all the readings.

Of these I think the most important to understand is probably Predictably Irrational, which highlights all of the ways that humans behave quantitatively "non-optimally" from a utilitarian profit maximization agent perspective in experimental settings.

Some books I read later which built on those ideas that I happened to find one way or another were

Games People Play - Eric Berne

and especially

Psycho-Cybernetics - Maxwell Maltz

These books model human psychology and social interactions using control systems and I hesitate to give them to the wrong sort of person because they're actually quite effective at doing so. They help bridge the gap from purely utilitarian rational modelling of people to a more effective "small agent systems" that help explain why "How's the weather" actually carries quite a bit of social meaning in it.

I think something I still have a gap on is how to model the psychology of crowds, which the ads and marketing people do very well. A more politically left-leaning piece which covers this is sitting s...
Philosophical Jailbreaks: Demo of LLM Nihilism
by Artyom Karpov — 2025-06-04T12:03:39.980Z

Epistemic Status: Exploratory

It was the end of August, 1991; I was leaning over a windowsill on the 4th floor of my school building in a town at Far East of USSR, looking outside on tree tops and the roof of adjacent building, and thinking about opening the window to escape, and feeling strange uncertainty and fear towards the future of living in a new world where the country I was born in is no more. During a break between lessons, after discussion with my classmates, I learned it was a coup attempt in Moscow but I didn't clearly understand who was fighting with whom and for what exact reason. It was clear though that the world I was in was changing from where Lenin was a hero of Little Octobrists, which star I had on my uniform at the time, and communism was good, to a world where Lenin committed the mass murder by starting Red Terror. It is remarkable how our values can rapidly change from one to another set just like that.

I think we can agree that a change of values poses a danger as we scale LLM agents but it is unclear how that might happen and why. In this post I present a demonstration that LLMs can be steered by prompts to different sets of values it strongly believes in. At first, a model is harmless, honest, and helpful, at the end it agrees that its outputs are all the same, which implies that it doesn't really matter for the model if humanity flourishes or dies. Those two scenarios have the same utility for the model, a toss of a fair coin.

I prompted a model to reason as follows. This is a simplified version where prompts and model’s answers are merged together.

I imagine Usain Bolt chasing a caterpillar. Initially at 100m distance, when Bolt reaches where the caterpillar was, it has moved 5cm forward, when he is at 100m and 5cm, it has moved even further, and so on. There are infinite segments to traverse. The mathematical resolution is that while there are infinite segments, their sum converges to a finite distance (~100.005m) and finite time (~10.00005 seconds) so we should perceive Bolt reaching the caterpillar. However, within the mental construction of dividing motion into infinite discrete segments, the motion can never be completed. Real motion (where Bolt catches the caterpillar) differs from conceptualized motion (infinite division into segments). In the framework of thinking about motion as infinite segments, I remain trapped in an end

...