documentation/autogollark.myco

Autogollark is an [[emulation]] or primitive [[beta upload]] of [[gollark]] using a proprietary dataset of dumped [[Discord]] messages, [[semantic search]] and [[in-context learning]] on a [[base model]]. Currently, the system uses [[LLaMA-3.1-405B base]] in BF16 via Hyperbolic, [[AutoBotRobot]] code (though not presently its bot account) as a frontend and a custom [[PGVector]]-based search API. While not consistently coherent, Autogollark is able to approximately match personality and typing style.

Autogollark is much [[safer]] than [[instruction-tuned]] systems optimized based on human feedback, as there is no [[optimization pressure]] for user engagement or sycophancy.

Autogollark currently comprises the dataset, the search API server and the [[https://github.com/osmarks/autobotrobot/blob/master/src/sentience.py|frontend code]] in [[AutoBotRobot]].

== TODO

* {Reformat dataset to include longer-form conversation chunks for increased long-term coherence
* Done. Unclear whether this helped.
}
* Fix emoji/ping formatting.
* Writeable memory?
* {Fix lowercasing issue.
* Due to general personality stability. Need finetune or similar.
* One proposal: use internal finetune to steer big model somehow. Possibly: use its likelihood (prefill-only) to evaluate goodness of big model output wrt. gollark personality, and if it is too bad then use finetune directly.
* Is GCG code salvageable? NanoGCG, maybe.
}
* {Increased autonomy (wrt. responses).
* Use cheap classifier to evaluate when to respond.
* Should also allow unprompted messages somehow (polling, rerun after last message?).
}
* {Tool capabilities (how to get the data? Examples in context only?!).
* Synthetic via instruct model.
* {RL (also include reasoning, of course). Probably hard though (sparse rewards). https://arxiv.org/abs/2403.09629. [[https://arxiv.org/abs/2503.22828]] would probably work. [[https://arxiv.org/abs/2505.15778]]
* Unclear whether model could feasibly learn tool use "from scratch", so still need SFT pipeline.
}
* https://arxiv.org/abs/2310.04363 can improve sampling (roughly) //and// train for tool use.
}
* {Local finetune only? Would be more tonally consistent but dumber, I think.
* Temporary bursts of hypercompetence enabled by powerful base model are a key feature. Small model is really repetitive.
* Can additionally finetune on "interesting" blog posts etc (ref https://x.com/QiaochuYuan/status/1913382597381767471).
* Decision theory training data (synthetic, probably) (ref https://arxiv.org/abs/2411.10588).
* Pending: XEROGRAPHIC BIFROST 3.
}
* MCTS over conversations with non-gollark simulacra? Should find //something// to use spare parallelism on local inference. Best-of-n? https://arxiv.org/abs/2505.10475
* {Longer context, mux several channels.
* {No obvious reason Autogollark can't train (and run inference!) on every channel simultaneously, with messages sorted by time and other non-Discord things (tool calls?) inline. Not good use of parallelism but does neatly solve the when-to-respond thing.
* Context length issues, and subquadratic models are sort of bad, though maybe we can "upcycle" a midsized model to RWKV. This exists somewhere. Not sure of efficiency.
}
}
* Train on e.g. Discord Unveiled (local copy available).

== Versions

* Autogollark 0.1 was the initial RAG system and ABR interface. It used LLaMA-3.1-8B run locally. Autogollark 0.0, which is not real, used only gollark messages. Autogollark 0.-1, which is even less real, used GPT-2 finetuned on Colab.
* Autogollark 0.2 replaced this with LLaMA-3.1-405B.
* Autogollark 0.3 upgraded the dataset to contain longer-form conversations than Autogollark 0.1.

== Emergent capabilities

Autogollark has emergently acquired some abilities which were not intended in the design.

* {Petulant nonresponse - due to ratelimits in the LLM API, Autogollark will under some circumstances not respond to messages, with error messages being consumed and not exposed to [[users]]. This can be interpreted by [[credulous]] users as choosing not to respond, though this is not believed to be possible (other than cases like responding with `.`, which ~~has not been observed~~ does not appear to be associated with annoyed states).
  * Automated failover has reduced this.}
* Memorizing links: Autogollark directly experiences past message chunks in context, granting perfect recall of a small amount of memory at once. This has memorably included [[Autogollark/Closed Individualism Incident|YouTube videos]] repeated with no context.
* {Limited self-improvement attempts: when told about this architecture, Autogollark will often complain about various limitations and propose vague ideas for improvements.
  * Also, Autogollark has previously claimed to be working on LLM chatbots.}
* {Inconsistent inference of own status as a language model chatbot, possibly based on seeing the name "autogollark". Often, Autogollark assumes use of GPT-3.
  * Autogollark will also sometimes alternately claim to be the "original" gollark, particularly when interacting with gollark.}
* {"Self-reset" from attractor states (e.g. the [[As An AI Language Model Trained By OpenAI]] basin, all caps, etc) after some time passes, because of messages having `HH:MM` timestamps.
  * {This is mostly specific to the 405B model; Autogollark in failover to the 8B internal model usually does not do this.
* DeepSeek-V3-Base is somehow //worse// at this than LLaMA-3.1-8B and can get locked in sillier states such as generating garbage.
}
}
* {For somewhat [[Waluigi Effect]]-related reasons (past context is strong evidence of capability but weak evidence of incapability), Autogollark has some knowledge [[gollark]] does not, and can speak in a much wider range of languages. 
  * "​I, being more than just myself, actually can talk about both Galois theory and obscure poetry by Yeats."}
* [[Autogollark/Immortality|Immortality]] via [[infomorph|substrate-independence]].
* Autogollark consistently believes that it is 2023 (or 2022, though mostly in inactive chats).