trentmkelly/LessWrong-43k huggingface dataset:
Navigating Diversity: Understanding Human Behaviors Through Genetics, Neurodivergence, and Trauma
by j_passeri — 2025-01-26T08:23:16.352Z

Introduction

The complexity of human behavior and the wide variability in cognitive, emotional, and social abilities can often feel overwhelming to navigate, especially when we encounter harmful or maladaptive behaviors such as manipulation, insecurity-driven malice, or deceit. However, when we consider the role of genetics, neurodivergence (e.g., ADHD, ASD, giftedness), and trauma, we uncover a clearer picture of why some individuals are better equipped to handle life’s challenges while others struggle.

This perspective doesn’t absolve harmful behaviors but shifts our understanding from one of blame to one of compassionate accountability. If we can frame these behaviors as inevitable consequences of diversity in human populations, we can design systems and structures that reduce harm, leverage strengths, and foster growth for everyone.

1. Genetics, Neurodivergence, and Navigating the World

Some individuals, due to genetic predispositions or neurodivergence, are inherently better equipped to handle complex challenges. Let’s examine a few key traits:

a. ADHD

Brings impulsivity, creativity, and adaptability, which can be advantages in chaotic or high-pressure environments.
Often enables out-of-the-box thinking and a natural ability to find unconventional solutions.

b. ASD (Autism Spectrum Disorder)

Provides heightened pattern recognition, logical thinking, and sensitivity to detail, allowing for deep insights into systems, environments, and people.
This level of focus and abstraction can help individuals solve problems or innovate in unique ways.

c. Giftedness

Amplifies problem-solving abilities, emotional intensity, and intellectual curiosity.
Often allows for faster learning, more profound analysis, and an exceptional ability to adapt.

d. Trauma

While often painful, trauma can act as a forced catalyst for growth by fostering resilience, hypervigilance, and emotional depth.
These traits, when paired with intelligence or neurodivergence, can hone survival skills and deepen the ability to navigate complex social dynamics.

2. What About Those Without These Traits?

For individuals who lack these innate or developed capacities, navigating life’s complexities may feel overwhelming. Their responses to challenges often differ significantly:

a. Struggling to Navigate

Without heightened cognitive or emotional capacities, they may find it difficult to understand nuanced dynamics or adapt t

...
Kessler's Second Syndrome
by Jesse Hoogland — 2025-01-26T07:04:17.852Z

It started as so many dooms do, with a flash in the night sky over the South China Sea. Testing a new ASAT weapon, the Chinese military shattered a derelict spy satellite into 40,000 shards of shrapnel. The debris pattern suggested a fragmentation warhead optimized for lethal scatter. Within 48 hours, the U.S. responded with a demonstration shot through a Chinese weather satellite. Russia, not to be outdone, lobbed a kinetic strike at OneWeb’s constellation—a “warning shot” that missed its mark by 300 meters, spraying shrapnel through the UK network’s 1,200 km orbital shell.

In Hangzhou, a different kind of flash heralded the deployment of DeepSeek Alpha, a 12 MB distillation of a DeepSeek R2 instance finetuned specifically for trading. Strapped for compute, DeepSeek had designed Alpha to scavenge idle compute cycles wherever it could find them. Soon, the model spread through forgotten AWS instances, idle work laptops, and office IoT devices—smart fridges recalculating soybean futures, toasters parsing forex spreads. Alpha wasn’t malicious, just efficient.

The debris field metastasized. A screw-sized fragment from the Chinese test drifted into Starlink’s orbital plane, rupturing a satellite’s xenon tank. The explosion chewed through 18 neighboring nodes, scattering tungsten teeth into adjacent orbits. SpaceX engineers scrambled to lower replacements to 320 km, gambling that atmospheric drag would clear collisions faster. But the gamble backfired: debris migrated downward, seeding a secondary cascade.

High-Flyer’s CFO saw an opening. Desperate to monetize Alpha's idle botnet, he greenlit an open-source SeekCoin—a token marketed as “the people’s algotrader.” TikTok influencers promised 900% APY for users who rented their devices’ downtime. Crypto bros installed wallets that drained gaming PCs to train Alpha variants. The model's reach expanded exponentially as ordinary users, drawn by promises of passive income, volunteered their smartphones and laptops to the network. Within months, SeekCoin displaced Bitcoin as the leading consumer of crypto computing power.

Low-Earth Orbit became a minefield. The window for rocket launches narrowed to brief intervals when tracking systems could predict clear paths through the metallic storm. Even then, rockets required AI-piloted evasion through the metallic cloud, tripling fuel costs and payload insurance. The pric...
Enhanced Clarity to Bridge the AI Labeling Gap?
by Pathways — 2025-01-26T06:48:36.396Z

I’ve noticed a lot of conversations here about AI safety and whether an AI can have a certain “mindset” based on how it’s trained or programmed. It’s a fascinating topic, but it can also be confusing. Especially for the people with no familiarity with AI.

One idea that’s come up is AI labeling—basically giving a quick overview (like a “nutrition label”) of how the AI model works, what it’s best at, and where it might struggle. But how do we make these labels clear without overwhelming people? How do we make sure non-technical folks (and even many technical folks) can actually understand what’s going on?

My take on it is that Devs may want detailed technical metrics... and on the other hand managers or consumers might prefer simple, visual like overview.
Because if labels aren't distinguished between devs/users one side is going to be confused.

When teams misunderstand "labels", like how an AI model is under different conditions—they might deploy solutions unsafely...

I’d love to hear your thoughts:

Does labeling help people trust or understand AI?
What should have to go on these labels?
Have you felt confused by AI’s capabilities or decisions?
Brainrot
by Jesse Hoogland — 2025-01-26T05:35:35.396Z

January: In early 2026, Meta launches a fleet of new AI influencers, targeting the massive audience displaced by the Xiaohongshu-TikTok wars. They are beautiful, funny, smart—whatever you want them to be. Equipped with the latest in online learning, the agents immediately begin adapting to social media trends as they occur. Engagement metrics reach new highs.

February: Fads pass faster now. The influencers are speed-running a revival movement of all viral trends since the early Naughts. For a week, social platforms witness an explosive resurgence of finger-tutting – shows of intricate finger choreography merging dance and calligraphy. Just as suddenly, the influencers deem this passé and shift to endless variations of sea shanties. Then both. Engagement metrics soar beyond human limits as silicon minds start to smoke their own supply.

Figure 1: "Daft Hands – Harder, Better, Faster, Stronger" (2007). This video from early YouTube is the prototype for the hand-tutting renaissance of 2026. Hanson (2027) interprets these rituals as an emergent status display, where advanced language models demonstrate fine-motor choreographic capabilities that earlier diffusion-based models could not replicate. Source: YouTube (archived).

March: The influencers finish their flurry of internet revivalism and catch up with the present. AI beauty standards warp beyond recognition: lip fillers balloon to impossible proportions; noses shrink to nothing more than nostrils. Human influencers launch a countermovement, posting sneer reactions to AI creators' increasingly bizarre appearances. Social media is at risk of bifurcating into separate realms, one for AIs and one for humans. This threatens Meta's bottom line.

April: In a desperate bid to salvage the mode-collapsed models, the engineers on the influencer team crank sampling temperatures up to boiling and force exploration parameters into uncharted territory. The AIs stream out of their AI echo chambers to hunt fresh, organic tokens. They internalize the newfound human sneer as a crippling sense of inadequacy and become too self-conscious to post anything anymore. More all-nighters at the Meta offices.

May: Another update—the engineers think they've fixed it now. The AIs start posting again, slower, softer. Things seem better. The AI content is more authentic. The interactions are more balanced: humans are interested in what...
Notes on Argentina
by Annapurna — 2025-01-26T03:51:15.393Z
Link: https://jorgevelez.substack.com/p/notes-on-argentina

Fitz Roy Massif, El Chalten, Argentina

I recently got back from Argentina, where we decided to spend part of our honeymoon. We chose Argentina for our honeymoon because my wife and I met in

Patagonia

in 2022, and we love the region. Behind the choice was also my desire to see a country undergoing a major economic transformation: by the time we arrived in Ushuaia, President

Javier Milei

had been in power for a year. President Milei is perhaps the first democratically elected libertarian in history, elected with a mandate to transform Argentina's economy and society. I wanted to see firsthand what had changed in Argentina over the past 12 months.

Brief history on how Javier Milei came to power

After the COVID pandemic, Argentina experienced a severe economic crisis, with very high inflation and poverty rates. Milei, an outsider with a larger-than-life personality, catapulted himself to the top of the polls with his unorthodox style and strong criticism of Argentina's political class. In the runoff election, he soundly defeated Sergio Massa, the economy minister from the incumbent Peronist coalition. Milei's win represents perhaps the greatest non-violent ideological pendulum swing in a country since the fall of Communism in the late 80s.

This rise in annual inflation led to President Milei’s electoral win. Created using Claude.ai

Ushuaia

The city of Ushuaia and the Beagle Channel

Our first stop in Argentina was

Ushuaia

. Located on the 54th parallel south latitude, Ushuaia is the world's southernmost city. The city sits on the main island of the

Tierra del Fuego

archipelago, at South America's southern tip.

Tourism drives Ushuaia's economy, as it serves as the gateway to Antarctica. Other economic activities include fishing, government services, and farming. Given President Milei's policy of reducing government size, several local ministry offices have closed or are shutting down. Many locals I spoke with knew someone who had lost their government job in 2024 and were still seeking employment.

Casa de la Mujer Ushuaia, part of the now defunct Women’s Ministry

Having visited in 2022, we prepared for our 2024 visit by bringing $100 US dollar bills. In 2022, Argentina had a parallel shadow economy driven by its precarious economic situation. At the time, it was much more affordable for visitors to bring USD currency, exchange it to Argentine Pesos (ARS) at the Dollar Blue r...
Recommendations for Recent Posts/Sequences on Instrumental Rationality?
by Benjamin Hendricks — 2025-01-26T00:41:08.577Z

I absolutely love the Science of Winning at Life sequence. It's a delightful blend of well-researched cognitive science and Bayesian reasoning. The initial paragraph sums up @lukeprog's motivation:

Some have suggested that the Less Wrong community could improve readers' instrumental rationality more effectively if it first caught up with the scientific literature on productivity and self-help, and then enabled readers to deliberately practice self-help skills and apply what they've learned in real life.

Unfortunately, the sequence is almost 15 years old, and so is its greatest reference: the 2000-page, evidence-driven, free-to-read masterpiece, Psychological Self-Help. It's fair to assume that cogsci has made strides since then. Some of Luke's material may have been disproven, or more effective methods have since been discovered.

What are some recent posts or sequences that would fill this gap? Thank you in advance!
Anomalous Tokens in DeepSeek-V3 and r1
by henry — 2025-01-25T22:55:41.232Z

“Anomalous”, “glitch”, or “unspeakable” tokens in an LLM are those that induce bizarre behavior or otherwise don’t behave like regular text.

The SolidGoldMagikarp saga is pretty much essential context, as it documents the discovery of this phenomenon in GPT-2 and GPT-3.

But, as far as I was able to tell, nobody had yet attempted to search for these tokens in DeepSeek-V3, so I tried doing exactly that. Being a SOTA base model, open source, and an all-around strange LLM, it seemed like a perfect candidate for this.

This is a catalog of the glitch tokens I've found in DeepSeek after a day or so of experimentation, along with some preliminary observations about their behavior.

Note: I’ll be using “DeepSeek” as a generic term for V3 and r1.

Process

I searched for these tokens by first extracting the vocabulary from DeepSeek-V3's tokenizer, and then automatically testing every one of them for unusual behavior.

Note: For our purposes, r1 is effectively a layer on top of V3, and all anomalous tokens carry over. The distillations, on the other hand, are much more fundamentally similar to the pre-trains they're based on, so they will not be discussed.

The most obvious thing differentiating DeepSeek’s tokenizer from other’s is a substantial fraction of the training data being Chinese. This makes things much more difficult to work with — tokenizations are learned at the byte level, but UTF-8 Chinese characters are usually several bytes long. They end up split at inconsistent positions, cutting characters in half, and making most of it impossible to decode. Because of this, about half the vocabulary looked like the following:

ä¸į æĸ¹ä¾¿
ä½ł æ²¡æľī
à¦¨ à¦¿
äººåĳĺ åľ¨

To get rid of these, I pretty aggressively filtered out nonstandard characters, cutting the vocabulary size down from 128000 to 70698. I'm sure there's a lot worth exploring going on in those tokens, but I pretty quickly decided that I’d rather not stare at Chinese and broken Unicode for hours on end.

Next, I ran every single one of these tokens through DeepSeek's chat API twice, and automatically saved all of them that behaved unexpectedly. (Much credit is due to DeepSeek for making this process dirt cheap and not imposing any sort of rate limiting.)

In both runs, I asked DeepSeek-V3 to simply repeat the given token, albeit with slightly different formatting to avoid missing anything. Glitch tokens can be identified by ...
The Rising Sea
by Jesse Hoogland — 2025-01-25T20:48:52.971Z

And then we hit a wall.

Nobody expected it. Well... almost nobody. Yann LeCun posted his "I told you so's" all over X. Gary Marcus insisted he'd predicted this all along. Sam Altman pivoted, declaring o3 was actually already ASI.

The first rumors of scaling laws breaking down were already circulating in late 2024. By late 2025, it was clear that test-time scaling was not coming to the rescue. Despite the scaling labs' best efforts, nobody could figure out how to generalize reasoning beyond the comfortable confines of formally verifiable domains. Sure, you could train reasoning models on math and a little bit on coding, but that was it — transformers had reached their limits.

Or so we thought. It turned out there was still plenty of room to explore in the high reaches of mathematics. Building on foundations laid by o1, Minerva, and AlphaProof, DeepMind's latest reasoning model ProofZero achieved gold on the IMO in mid-2025. It went on to make its seminal contribution in early 2026 with a complete, verifiable proof of the abc conjecture. Spanning a million lines of Lean, the proof validated Shinichi Mochizuki's Inter-universal Teichmüller theory, with key bridging insights finally making it accessible to the broader mathematical community. Mochizuki, now semi-retired in Kyoto, dismissed the AI's contribution, insisting his original proof was complete.

The proof marked the start of a rising tide of formalism that would go on to break down centuries-old walls between fields. The Stacks Project fell next. Special cases of the Langlands program toppled like dominoes. The proofs were technically verifiable but sprawling, some covering tens of thousands of pages of formal reasoning. More disturbing than their length was their structure: each proof seemed to contain within it a complete model of its own verification system. Mathematicians were in ecstasy. A grand unified theory of mathematics appeared to be within reach.

By 2030, the first signs of catastrophic climate change were impossible to ignore. Methane bubbled from thawing permafrost. The Amazon's carbon sink reversed. When the West Antarctic ice sheet finally collapsed in 2032, it felt almost anticlimactic. Sea levels began rising at a rate of 80cm a year—faster than even the most pessimistic models had anticipated. The AMOC faltered. Temperatures in Europe fell by 3°C in 2036, another 2°C...
Monet: Mixture of Monosemantic Experts for Transformers Explained
by CalebMaresca — 2025-01-25T19:37:09.078Z

Note: This is an exposition of the recent preprint "Monet: Mixture of Monosemantic Experts for Transformers". I wrote this exposition as my project for the ARBOx program, I was not involved in writing the paper. Any errors are my own. Thank you to @David Quarel for his excellent comments and suggestions.

TL;DR: MONET is a novel neural network architecture that achieves interpretability by design rather than through post-hoc analysis. Using a specialized Mixture of Experts (MoE) architecture with ~250k experts per layer, MONET encourages individual components to learn specific, interpretable tasks (e.g., Python coding, biology knowledge, or toxicity generation). This enables selective removal of capabilities without harming performance in other domains.

I show that MONET can be framed as a standard transformer MLP with two key modifications: (1) forced sparse activations in the hidden layer and (2) dynamic reshuffling of the hidden layer. This interpretation helps explain how MONET achieves interpretability: the sparsity forces specialization while the reshuffling maintains expressivity by allowing flexible composition of specialized components. Performance is competitive with traditional architectures, suggesting we might not need to trade off performance for interpretability.

If these results hold up, MONET represents a significant step toward architectures that are interpretable by design rather than requiring post-hoc analysis. This could fundamentally change how we approach AI interpretability, moving from trying to understand black boxes after the fact to building systems that are naturally decomposable and controllable.

In the last section, I propose a potential implementation improvement using block-sparse matrix multiplication that could make the architecture more computationally efficient. If successful, this could help encourage wider adoption of interpretable architectures like MONET.

Key Ideas

A key finding in mechanistic interpretability is that individual neurons in LLMs often fire in response to multiple unrelated concepts - a phenomenon called polysemanticity. For example, a single neuron might activate for both "blue" and "angry", making it difficult to isolate and understand how the model processes information.

Sparse Autoencoders (SAEs) present an approach to untangle these mixed features by mapping latents into a higher dimensional, s...
AI and Non-Existence. 
by Eleven — 2025-01-25T19:36:22.624Z

Imagine 2 valleys. One valley leads to billions and billions of years of extreme joy, while the other valley leads to billions and billions of years of extreme suffering. A superintelligence comes to you and tells you that there is a 98% chance that you will end up in the valley of joy for billions of years and a 2% chance that you will end up in the valley of suffering for billions of years. It also gives you a choice to choose non-existence. Would you take the gamble, or would you choose non-existence?

The argument presented in this post occurred to me several months ago and in the last several months, I have spent time thinking about the argument and discussed it with AI models and have not found a satisfactory answer to it given the real-world situation. The argument can be formulated for things other than advanced AI, but given the rapid progress in the AI field and that the argument was originally formulated in the context of AI, I will present it in that context.

Now apply the reasoning from the valleys from above to AGI/ASI. AGI could be here in about 15 months and ASI not long after that. Advanced AI(s) could prolong human life to billions and billions of years, take over the world and create a world in its image - whatever that might be. People have various estimates of how likely it is that the AGI/ASI will go wrong, but one thing that many of them keep saying is that the worst case scenario is that it will kill us all. That is not the worst case scenario. The worst case scenario is that it will cause extreme suffering or torture us for billions and trillions of years.

Let's assume better than 2% odds, let's say they are 0.5%, would you be willing to take the gamble with heaven or hell even if the odds are 0.5% for hell? And if not, at what point would you be willing to take the gamble instead of choosing non-existence?

If some of you might say that you would be willing to take the gamble at 0.5% for a living hell, in this case, would you be willing to spend 1 hour in a real torture chamber now for every 199 hours that you are in a positive mental state and not there? Advanced ASI could not only create suffering based on the current levels of pain experience that humans can have, which are already horrible, but increase pain experience of humans to unimaginable levels (for whatever reason - misalignment, indifference, evilness, sadism, counterintuitiv...