Imagine being captain of a ship and using the same AI with different profiles as background. E.g. what's your opinion on data based on a geologist profile, vs. a profile based on some other profession...
"K-lines: A Theory of Memory" (1980) -- Cognitive Science. Names as activation vectors that reactivate entire constellations of knowledge.
https://www.sciencedirect.com/science/article/abs/pii/S03640...
https://en.wikipedia.org/wiki/K-line_(artificial_intelligenc...
"Society of Mind" (1986) -- The full theory. Intelligence as emergent property of many simple agents.
https://www.simonandschuster.com/books/Society-Of-Mind/Marvi...
https://archive.org/details/societyofmind0000marv/mode/2up
"The Emotion Machine" (2006) -- Extends Society of Mind to emotions as "ways of thinking."
https://en.wikipedia.org/wiki/The_Emotion_Machine
"The question is not whether intelligent machines can have any emotions, but whether machines can be intelligent without any emotions." -- Marvin Minsky
https://philopedia.org/thinkers/marvin-lee-minsky/
For LLM multi-agent work:
"Generative Agents: Interactive Simulacra of Human Behavior" (Park et al., Stanford, 2023)
https://arxiv.org/abs/2304.03442
The connection keyle is making -- same LLM, different professional profiles -- is exactly Minsky's insight. The profile IS a K-line: "geologist" activates {rock strata, deep time, fieldwork, skepticism...}
I wrote up how this applies to LLMs, including The Sims zodiac signs (1997: zero code, perceived as "too influential"):
Sims Astrology: https://github.com/SimHacker/moollm/blob/main/designs/sims-a...
Society of Mind: https://github.com/SimHacker/moollm/blob/main/skills/society...
K-Lines https://github.com/SimHacker/moollm/blob/main/skills/k-lines...
Adversarial Comittee: https://github.com/SimHacker/moollm/blob/main/skills/adversa...
The punchline: one voice is the wrong number of voices.
When you ask a single LLM "Should I take this client?", you get the statistical center of all possible viewpoints -- hedged, cautious, anodyne. The centroid of the cloud, not the shape of the cloud.
An adversarial comittee with Maya would say: "Trap. Their scope creep is a red flag." Frankie would say: "The opportunity! The growth!" Vic would say: "Show me the financials."
A single voice smooths all these into one bland answer. Adversarial committees force the LLM to explore the actual distribution of perspectives.
MOOLLM Manifesto: https://github.com/SimHacker/moollm/blob/main/designs/MOOLLM...
MOOLLM Eval Incarnate Framework: https://github.com/SimHacker/moollm/blob/main/designs/MOOLLM...
"Don't be curmudgeonly. Thoughtful criticism is fine, but please don't be rigidly or generically negative."
Timothy Leary used his own Interpersonal Circumplex to break out of jail!
https://github.com/SimHacker/moollm/tree/main/skills/mind-mi...
In other words, the completeness or scientific rigor of the original categorization and naming is irrelevant, compared to its consistency and presence in the training data.
In some cases, the most straightforward approach might be to name a particularly popular character by name.
1. enumerate the actions (policies) your agent takes, collected from prior runs
2. infer the states that correspond to each of these policies, make a state atlas (similar to the zodiac here)
3. infer the maximally discriminative features that can identify the state from current context
4. label a few examples and train a small policy model that predicts your action from those state features
I think LLMs should be used more often like this - as feature extractors for toy models, which can be used like tools. This way you can encode arbitrary logic in a small tool model that does not depend on the biases of the base model. For example this setup could power a "skill" to reliably implement your policy.
The trick here is that you carefully identify states that predict policy reliably, and features that distinguish between states, instead of using embeddings or pure LLM reasoning. You can decouple the logic from the feature extraction, and have it calibrated to your goals.
All 4 steps can be done by a coding agent with your supervision and zero coding. It's LLM as generic feature extractor with small models sitting on top.
There was someone a while ago who made a funny post about the type of Hacker News commenters. So I have 5 of them that will review things, and ended up being way more effective than I ever imagined they'd be.
│ contrarian-provocateur-roaster │ Challenge premises, explore alternatives │ "Have you considered..."
│ enthusiastic-newcomer-roaster │ Accessibility, onboarding friction │ "Wait, how do I even..."
│ pragmatic-builder-roaster │ Operational reality, production concerns │ "This won't survive 3AM pages" │
│ skeptical-senior-roaster │ Long-term maintenance, sustainability │ "Who maintains this in 2 years?" │
│ well-actually-pedant-roaster │ Terminology precision, verifiability │ "Technically, that's not..." │
https://gist.github.com/notque/e57cb975a3df7780824ce4085a59a...
In my other comment here, I described The Sims' zodiac from 1997: Will Wright computed signs from personality via Euclidean distance to archetypal vectors, displayed them cosmetically, and wrote zero behavioral code. The zodiac affected nothing. Yet testers reported bugs: "The zodiac influence is too strong! Tune it down!"
Your "mode-collapse with stochastic noise" is the same phenomenon measured from the other direction. In The Sims: zero computed difference, perceived personality. In this LLM experiment: minimal computed difference, perceived personality. Same gap.
Will called it the Simulator Effect: players imagine more than you simulate. I would argue mode-collapse IS the Simulator Effect measured from the output side.
But here is where it becomes actionable: one voice is the wrong number of voices.
ChatGPT gives you the statistical center -- mode-collapse to the bland mean. The single answer that offends no one and inspires no one. You can not fix this with better prompting because it is the inevitable result of single-agent inference.
Timothy Leary built MIND MIRROR in 1985 -- psychology software visualizing personality as a circumplex, based on his 1950 PhD dissertation on the Interpersonal Circumplex. The Sims inherited this (neat, outgoing, active, playful, nice). But a personality profile is not an answer. It is a lens.
The wild part: in 1970, Leary took his own test during prison intake, gamed it to get minimum security classification (outdoor work detail), and escaped by climbing a telephone wire over the fence. The system's own tools became instruments of liberation.
https://github.com/SimHacker/moollm/tree/main/skills/mind-mi...
MOOLLM's response: simulate an adversarial committee within the same call. Multiple personas with opposing propensities -- a paranoid realist, an idealist, an evidence prosecutor -- debating via Robert's Rules. Stories that survive cross-examination are more robust than the statistical center.
https://github.com/SimHacker/moollm/tree/main/skills/adversa...
I wrote this up with links into the project:
https://github.com/SimHacker/moollm/blob/main/designs/sims-a...
The bigger project is MOOLLM -- treating the LLM as eval() for a microworld OS. K-lines, prototype-based instantiation, many-voiced deliberation. The question I keep wrestling with: mode-collapse as limitation vs feature. The Sims exploited it. MOOLLM routes around it.
Would value your take on the information-theoretic framing -- particularly whether multi-agent simulation actually increases effective entropy or just redistributes it.
https://github.com/SimHacker/moollm
The MOOLLM Eval Incarnate Framework: Skills are programs. The LLM is eval(). Empathy is the interface. Code. Graphics. Data. One interpreter. Many languages. The Axis of Eval.
https://github.com/SimHacker/moollm/blob/main/designs/MOOLLM...
https://news.ycombinator.com/item?id=14997725
https://www.donhopkins.com/home/images/Sims/
https://news.ycombinator.com/item?id=15002840
DonHopkins on Aug 13, 2017 | parent | context | favorite | on: Motive.c: The Soul of the Sims (1997)
The trick of optimizing games is to off-load as much as the simulation from the computer into the user's brain, which is MUCH more powerful and creative. Implication is more efficient (and richer) than simulation.
During development, when we first added Astrological signs to the characters, there was a discussion about whether we should invent our own original "Sim Zodiac" signs, or use the traditional ones, which have a lot of baggage and history (which some of the designers thought might be a problem).
Will Wright argued that we actually wanted to leverage the baggage and history of the traditional Astrological signs of the Zodiac, so we should just use those and not invent our own.
The way it works is that Will came up with twelve archetypal vectors of personality traits corresponding to each of the twelve Astrological signs, so when you set their personality traits, it looks up the sign with the nearest euclidian distance to the character's personality, and displays that as their sign. But there was absolutely no actual effect on their behavior.
That decision paid off almost instantly and measurably in testing, after we implemented the user interface for showing the Astrological sign in the character creation screen, without writing any code to make their sign affect their behavior: The testers immediately started reporting bugs that their character's sign had too much of an effect on their personality, and claimed that the non-existent effect of astrological signs on behavior needed to be tuned down. But that effect was totally coming from their imagination!
They should call them Astrillogical Signs!
DonHopkins on Aug 13, 2017 [–]
The create-a-sim user interface hid the corresponding astrological sign for the initial all-zero personality you first see before you've spent any points, because that would be insulting to 1/12th of the players (implying [your sign] has zero personality)!
fcpguru•3w ago