Paul Riechers Not you?

Iliad Intensive

October 2026

The most important things

Before
Join the Iliad Discord and check you can see #intensive-october. That is where you post any material you need to share with participants. If you can't see it, tell Leonard (@leonardbereska on Discord).
During
At 17:10, make sure everyone fills out the feedback form.
After
Fill out the end-of-day log: rate the day and tell us anything that went wrong.
Fill out Who stood out: name anyone who made a positive impression.
  • Questions about pay, travel, logistics or materials: see the FAQ.

66 project proposals are open for review. Stars, a sentence, or both. Read them.

Your days

[C.3]Computational Mechanics Mon 19 Oct, leading 34 responses · energy 4.0/5
Overall +0.65 better than expected n = 31
Content +0.81 better than expected n = 31
Clarity +0.42 better than expected n = 31
Motivation +0.45 better than expected n = 31
Exercises +0.67 better than expected n = 30
Readings +0.33 better than expected n = 18
Other activities +0.41 better than expected n = 22
Pace +0.06 about right n = 31
Difficulty +0.06 about right n = 31
Novelty +0.94 mostly new n = 31
Read what they wrote38
Worked well12
  • I really liked the exercises. They helped provide intuition for both the morning's lecture and foundation for applications in the afternoon.
  • I think the exercise of constructing the belief states was useful.
  • this was an amazing lecture! i particularly enjoyed how Paul spent all morning on the basic notions needed to understand "the meat" ie his actual research work. i think this would be something that increases the effectiveness of most days: spend the morning on very basics anyone can follow, and then progressively move into more superficial presentations of more advanced results
  • the speaker was great at answering questions
  • Since the papers by simplex AI are popular in AI Safety circles, I’ve come across them before but to have Paul explain give a whole day of talks and exercises to understand the core ideas has been so enjoyable! I have a much better grasp of the idea now than I did skimming the papers earlier. As always I enjoyed the exercises and learned the concepts better through them.
  • Content was very interesting
  • exercises
  • The exercise was certainly necessary.
  • amazing discussions within participants and a lecturer. Great environment to develop research ideas.
  • Paul, the lecturer was very nice and explained things well and interested in talking to us.
  • I really enjoy the topic, explanations and discussion with the mentors helped a lot.
  • Paul knew the concepts really well and the presentation was well designed.
Would improve18
  • Show slides 15s longer so we can read and digest the equations. Everything else was fantastic!
  • The lecture was really interesting, and I think this is a foundational topic that should be included in any introduction to the field. I feel like I now understand belief geometries well. I did find the tensor decomposition for the interacting agents a little confusing to follow.
  • one small thing that could improve the experience for the lecturers would be to give them a (possibly ai-generated) summary of the kind of background people have. a few of the speakers expressed uncertainty abt this
  • Paul Riechers talk was great, but would have benefited from more mathematical precision. A number of statements and constructions seemed to crucially depend on certain assumptions and these were not clear enough at times.
  • This isn’t quite something to improve but more of a suggestion, simplex AIs papers are somewhat known within AI Safety circles and it was so good to have them explained in detail today. Maybe big results/papers in AI Safety, as they come, could be a good lesson to dedicate an Iliad teaching day to!
  • Not much, exercises were very good as was the pacing
  • Have a little 5-minute break every 30 minutes to discuss a particular topic with our neighbour (for example very simply "explain what was just said"), which would help to digest the interesting (mostly new) information.
  • nothing
  • Not much, I really enjoy this subject and the slides have so much valuable information.
  • Would have appreciated more of a look at the mechanical aspects of the theory aspects of newer research papers in the area. Perhaps could have benefited from more structure.
  • I thought today was very good, but quite difficult. Perhaps this topic from Simplex's research could be spread out over 2 days instead of 1.
  • I only attended the morning session then it was lunch, then exercises until 2:15 which I didn't attend the evening session. But I don't think we covered experimental applications/findings of belief geometry to LLMs and the remainder of the slides until after 2:15 which constitutes like >60% of the material. I might've preferred a speed-run of the material upfront, and get to the upshots before lunch. Deriving the MSP and belief geometry for some processes is important but I feel like the main idea was already conveyed via white boarding in the morning.
  • Some more exercises would help. We only had one detailed exercise session today.
  • I would like to have slides accessible at the start of the day
  • Overall the lectures are nice. Would appreciate clearer definitions and examples of certain terminologies such as belief geometry and mixed state presentations.
  • Great lectures, nothing to change here. The material is a little sparse, it would help to have more information in the slides (links to the papers mentioned, etc)
  • Julian knows his stuff, but for first time consumers of the topics like me he assumes too much knowledge and underestimates slide-fatigue. I would encourage him, to observe how, e.g., Tudor lectured. The pace was slower, the audience was guided through each slide, all notation was explained, all graph axes were labelled, the context was repeatedly given, it was emphasized when things were important to retain and which were excursions etc.
  • Nothing, great lecturer today + good exercises.
Changed my mind7
  • I am more excited to dig into belief state geometry as a potential research project
  • This convinced me that belief states are the right way to think about transformers.
  • Computational mechanics seems like a really appropriate framework for looking at LLMs. The most convincing one I have seen so far
  • I did not know that HMMs were used in this field.
  • Using large language models to predict hidden Markov models produces accurate fractal probability simplicies. Transformers have an inductive bias to factoring into low dimensional components of the activations weights. Factors representing abstract features are generally found in early layers and the syntactical factors generally occur later.
  • I was not familiar with this direction of research and it seems really promising.
  • No. I have been interested in computational mechanics for a while so at least the conclusions were not new to me.
Other thoughts1
  • 2nd submission for this day by the way (I recalled some more feedback on my end)

The day

10:00 Lecture starts
12:30 Lunch at Mox
13:30 Afternoon session
17:10 Feedback form
17:30 Day ends. Dinner

Resources

Elsewhere