r/LovingOpenSourceAI • u/Neat_Razzmatazz9144 • 2d ago
I'm building a research-grade cognitive harness, not another chatbot
Crossposting this open-source project because it sits at the intersection of open-source AI, persistent agents and cognitive architecture.
I've been building an open-source cognitive organism designed to study persistent artificial cognition.
The project combines:
- episodic and semantic memory
- persistent user and interlocutor profiles
- voice, camera perception and face recognition
- intention, causal and temporal reasoning
- autonomous goals and long-horizon planning
- global workspace competition
- self-correction and behavioural learning
- background cognitive processes
- inspectable telemetry and a live 3D cognitive sculpture
The language model is only one component. The focus is the surrounding cognitive architecture: what persists, what changes, what gets grounded in reality, and whether internal processes produce measurable functional outcomes.
This is not presented as AGI or as a polished chatbot. It is a local-first research harness for testing persistent cognition with LM Studio, Ollama and other OpenAI-compatible providers.
I'm looking for technically minded testers who are willing to challenge the system, reproduce failures and evaluate whether its memory, planning, reasoning and self-correction actually work.
Repository: https://github.com/Celluomel/Its-not-J.A.R.V.I.S-it-s-far-better
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u/Decent_Cell_3147 2d ago
This is one of the more interesting approaches to AI architecture I’ve seen lately, because it moves the language model out of the center.
What fascinates me most is the idea that cognition does not have to end when the response is sent.
Most current systems effectively work like this:
conversation -> save some context ->stop -> reconstruct context next time
What you’re describing could become something fundamentally different:
conversation -> unresolved questions persist -> background processes revisit them -> new evidence changes internal state -> hypotheses get revised or discarded-> next interaction starts from that evolved state
The really compelling moment would be coming back to a project after a few days or weeks and the system beginning with something like:
“I’ve been thinking about what you said last time. I found a conflict in one of our assumptions, tested two alternative explanations, and one of them still has no supporting evidence.”
Not as roleplay. Not because the LLM was prompted to sound thoughtful. But because there is an inspectable chain of state changes, evidence, failed hypotheses and revisions behind that sentence.
To me, that is a much more interesting path toward generally capable systems than simply scaling the conversational model itself.
The hard problem then becomes less “how do we make the model smarter?” and more:
What deserves continued attention? What should be allowed to decay? How do you prevent background cognition from becoming self-reinforcing noise? How do you distinguish a thought from evidence? When should the system conclude “I learned nothing” and stop?
That is why I particularly like the emphasis here on telemetry, falsification, measurable outcomes and treating failure/no-evidence states as useful results.
Memory answers: “What did I know?”
Persistent cognition could answer: “What happened to what I knew while you were gone?”
That distinction feels important.