All prediction market platforms trade continuously, which is the same mechanism the stock market uses. Buy and sell limit orders can be posted at any time, and as soon as they match against each other a trade will be executed. This is called a Central limit order book (CLOB).

Example of a CLOB order book from Polymarket Most of the time, the market price lazily wanders around due to random variation in when people show up, and a bulk of optimistic orders build up away from the action. Occasionally, a new piece of information arrives to the market, and it jumps to a new price, consuming some of the optimistic orders in the process.

The people with stale orders will generally lose out in this situation, as someone took them up on their order before they had a chance to process the new information. This means there is a high [...]

The original text contained 3 footnotes which were omitted from this narration.

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First published:
August 2nd, 2025

Source:
https://www.lesswrong.com/posts/rS6tKxSWkYBgxmsma/many-prediction-markets-would-be-better-off-as-batched

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Essays like Paul Graham's, Scott Alexander's, and Eliezer Yudkowsky's have influenced a generation of people in how they think about startups, ethics, science, and the world as a whole. Creating essays that good takes a lot of skill, practice, and talent, but it looks to me that a lot of people with talent aren't putting in the work and developing the skill, except in ways that are optimized to also be social media strategies.

To fix this problem, I am running the Inkhaven Residency. The idea is to gather a bunch of promising writers to invest in the art and craft of blogging, through a shared commitment to each publish a blogpost every day for the month of November.

Why a daily writing structure? Well, it's a reaction to other fellowships I've seen. I've seen month-long or years-long events with exceedingly little public output, where the people would've contributed [...]

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First published:
August 2nd, 2025

Source:
https://www.lesswrong.com/posts/CA6XfmzYoGFWNhH8e/whence-the-inkhaven-residency

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TL;DR

I believe that:

• Almost all LLM-centric safety research will not provide any significant safety value with regards to existential or civilisation-scale risks.
• The capabilities-related forecasts (not the safety-related forecasts) of Stephen Brynes' Foom and Doom articles are correct, except that they are too conservative with regards to timelines.
• There exists a parallel track of AI research which has been largely ignored by the AI safety community.  This agenda aims to implement human-like online learning in ML models, and it is now close to maturity. Keywords: Hierarchical Reasoning Model, Energy-based Model, Test time training.
• Within 6 months this line of research will produce a small natural-language capable model that will perform at the level of a model like GPT-3, but with improved persistence and effectively no "context limit" since it is constantly learning and updating weights.
• Further development of this research will produce models that fulfill most of [...]

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Outline:

(00:11) TL;DR

(01:22) Overview

(04:10) The Agenda I am Worried About

(07:36) Concrete Predictions

(09:29) What I think we should do

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First published:
July 31st, 2025

Source:
https://www.lesswrong.com/posts/tEZa7PouYatK78bbb/i-am-worried-about-near-term-non-llm-ai-developments

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Produced as part of MATS 8.0 under the mentorship of Alex Turner and Alex Cloud. This research note overviews some early results which we are looking for feedback on.

TL;DR: We train language models with RL in toy environments. We show that penalizing some property of the output is sufficient to suppress that property in the chain of thought also, even when that property is relevant to task completion. For example, when we penalize a model for mentioning in its output that it completed a task via a certain form of cheating, its reasoning also omits this fact. We therefore suspect that training against an output monitor is sufficient to cause obfuscated CoTs in more realistic settings.

Introduction

Chain of thought (CoT) supervision appears in many control and scalable oversight protocols. It has been argued that being able to monitor CoTs for unwanted behavior is a critical property [...]

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Outline:

(00:56) Introduction

(02:38) Setup

(03:48) Single-Turn Setting

(04:26) Multi-Turn Setting

(06:51) Results

(06:54) Single-Turn Setting

(08:21) Multi-Turn Terminal-Based Setting

(08:25) Word-Usage Penalty

(09:12) LLM Judge Penalty

(10:12) Takeaways

(10:57) Acknowledgements

The original text contained 1 footnote which was omitted from this narration.

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First published:
July 30th, 2025

Source:
https://www.lesswrong.com/posts/CM7AsQoBxDW4vhkP3/optimizing-the-final-output-can-obfuscate-cot-research-note

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FutureHouse is a company that builds literature research agents. They tested it on the bio + chem subset of HLE questions, then noticed errors in them.

The post's first paragraph:

Humanity's Last Exam has become the most prominent eval representing PhD-level research. We found the questions puzzling and investigated with a team of experts in biology and chemistry to evaluate the answer-reasoning pairs in Humanity's Last Exam. We found that 29 ± 3.7% (95% CI) of the text-only chemistry and biology questions had answers with directly conflicting evidence in peer reviewed literature. We believe this arose from the incentive used to build the benchmark. Based on human experts and our own research tools, we have created an HLE Bio/Chem Gold, a subset of AI and human validated questions.

About the initial review process for HLE questions:

[...] Reviewers were given explicit instructions: “Questions should ask for something precise [...]

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First published:
July 29th, 2025

Source:
https://www.lesswrong.com/posts/JANqfGrMyBgcKtGgK/about-30-of-humanity-s-last-exam-chemistry-biology-answers

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Maya did not believe she lived in a simulation. She knew that her continued hope that she could escape from the nonexistent simulation was based on motivated reasoning. She said this to herself in the front of her mind instead of keeping the thought locked away in the dark corners. Sometimes she even said it out loud. This acknowledgement, she explained to her therapist, was what kept her from being delusional.

“I see. And you said your anxiety had become depressive?” the therapist said absently, clicking her pen while staring down at an empty clipboard.

“No- I said my fear had turned into despair,” Maya corrected.

It was amazing, Maya thought, how many times the therapist had refused to talk about simulation theory. Maya had brought it up three times in the last hour, and each time, the therapist had changed the subject. Maya wasn’t surprised; this [...]

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First published:
July 27th, 2025

Source:
https://www.lesswrong.com/posts/ydsrFDwdq7kxbxvxc/maya-s-escape

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TsviBT Tsvi's context

Some context:

My personal context is that I care about decreasing existential risk, and I think that the broad distribution of efforts put forward by X-deriskers fairly strongly overemphasizes plans that help if AGI is coming in <10 years, at the expense of plans that help if AGI takes longer. So I want to argue that AGI isn't extremely likely to come in <10 years.

I've argued against some intuitions behind AGI-soon in Views on when AGI comes and on strategy to reduce existential risk.

Abram, IIUC, largely agrees with the picture painted in AI 2027: https://ai-2027.com/

Abram and I have discussed this occasionally, and recently recorded a video call. I messed up my recording, sorry--so the last third of the conversation is cut off, and the beginning is cut off. Here's a link to the first point at which [...]

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Outline:

(00:17) Tsvis context

(06:52) Background Context:

(08:13) A Naive Argument:

(08:33) Argument 1

(10:43) Why continued progress seems probable to me anyway:

(13:37) The Deductive Closure:

(14:32) The Inductive Closure:

(15:43) Fundamental Limits of LLMs?

(19:25) The Whack-A-Mole Argument

(23:15) Generalization, Size, & Training

(26:42) Creativity & Originariness

(32:07) Some responses

(33:15) Automating AGI research

(35:03) Whence confidence?

(36:35) Other points

(48:29) Timeline Split?

(52:48) Line Go Up?

(01:15:16) Some Responses

(01:15:27) Memers gonna meme

(01:15:44) Right paradigm? Wrong question.

(01:18:14) The timescale characters of bioevolutionary design vs. DL research

(01:20:33) AGI LP25

(01:21:31) come on people, its \[Current Paradigm\] and we still dont have AGI??

(01:23:19) Rapid disemhorsepowerment

(01:25:41) Miscellaneous responses

(01:28:55) Big and hard

(01:31:03) Intermission

(01:31:19) Remarks on gippity thinkity

(01:40:24) Assorted replies as I read:

(01:40:28) Paradigm

(01:41:33) Bio-evo vs DL

(01:42:18) AGI LP25

(01:46:30) Rapid disemhorsepowerment

(01:47:08) Miscellaneous

(01:48:42) Magenta Frontier

(01:54:16) Considered Reply

(01:54:38) Point of Departure

(02:00:25) Tsvis closing remarks

(02:04:16) Abrams Closing Thoughts

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First published:
July 15th, 2025

Source:
https://www.lesswrong.com/posts/5tqFT3bcTekvico4d/do-confident-short-timelines-make-sense

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Eliezer and I love to talk about writing. We talk about our own current writing projects, how we’d improve the books we’re reading, and what we want to write next. Sometimes along the way I learn some amazing fact about HPMOR or Project Lawful or one of Eliezer's other works. “Wow, you’re kidding,” I say, “do your fans know this? I think people would really be interested.”

“I can’t remember,” he usually says. “I don’t think I’ve ever explained that bit before, I’m not sure.”

I decided to interview him more formally, collect as many of those tidbits about HPMOR as I could, and share them with you. I hope you enjoy them.

It's probably obvious, but there will be many, many spoilers for HPMOR in this article, and also very little of it will make sense if you haven’t read the book. So go read Harry Potter and [...]

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Outline:

(01:49) Characters

(01:52) Masks

(09:09) Imperfect Characters

(20:07) Make All the Characters Awesome

(22:24) Hermione as Mary Sue

(26:35) Who's the Main Character?

(31:11) Plot

(31:14) Characters interfering with plot

(35:59) Setting up Plot Twists

(38:55) Time-Turner Plots

(40:51) Slashfic?

(45:42) Why doesnt Harry like-like Hermione?

(49:36) Setting

(49:39) The Truth of Magic in HPMOR

(52:54) Magical Genetics

(57:30) An Aside: What did Harry Figure Out?

(01:00:33) Nested Nerfing Hypothesis

(01:04:55) Epilogues

The original text contained 26 footnotes which were omitted from this narration.

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First published:
July 25th, 2025

Source:
https://www.lesswrong.com/posts/FY697dJJv9Fq3PaTd/hpmor-the-probably-untold-lore

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As a person who frequently posts about large language model psychology I get an elevated rate of cranks and schizophrenics in my inbox. Often these are well meaning people who have been spooked by their conversations with ChatGPT (it's always ChatGPT specifically) and want some kind of reassurance or guidance or support from me. I'm also in the same part of the social graph as the "LLM whisperers" (eugh) that Eliezer Yudkowsky described as "insane", and who in many cases are in fact insane. This means I've learned what "psychosis but with LLMs" looks like and kind of learned to tune it out. This new case with Geoff Lewis interests me though. Mostly because of the sheer disparity between what he's being entranced by and my automatic immune reaction to it. I haven't even read all the screenshots he posted because I take one glance and know that this [...]

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Outline:

(05:03) Timeline Of Events Related To ChatGPT Psychosis

(16:16) What Causes ChatGPT Psychosis?

(16:27) Ontological Vertigo

(21:02) Users Are Confused About What Is And Isnt An Official Feature

(24:30) The Models Really Are Way Too Sycophantic

(27:03) The Memory Feature

(28:54) Loneliness And Isolation

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First published:
July 23rd, 2025

Source:
https://www.lesswrong.com/posts/f86hgR5ShiEj4beyZ/on-chatgpt-psychosis-and-llm-sycophancy

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Authors: Alex Cloud*, Minh Le*, James Chua, Jan Betley, Anna Sztyber-Betley, Jacob Hilton, Samuel Marks, Owain Evans (*Equal contribution, randomly ordered)

tl;dr. We study subliminal learning, a surprising phenomenon where language models learn traits from model-generated data that is semantically unrelated to those traits. For example, a "student" model learns to prefer owls when trained on sequences of numbers generated by a "teacher" model that prefers owls. This same phenomenon can transmit misalignment through data that appears completely benign. This effect only occurs when the teacher and student share the same base model.

📄Paper, 💻Code, 🐦Twitter

Research done as part of the Anthropic Fellows Program. This article is cross-posted to the Anthropic Alignment Science Blog.

Introduction Distillation means training a model to imitate another model's outputs. In AI development, distillation is commonly combined with data filtering to improve model alignment or capabilities. In our paper, we uncover a [...]

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Outline:

(01:11) Introduction

(03:20) Experiment design

(03:53) Results

(05:03) What explains our results?

(05:07) Did we fail to filter the data?

(06:59) Beyond LLMs: subliminal learning as a general phenomenon

(07:54) Implications for AI safety

(08:42) In summary

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First published:
July 22nd, 2025

Source:
https://www.lesswrong.com/posts/cGcwQDKAKbQ68BGuR/subliminal-learning-llms-transmit-behavioral-traits-via

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