Artificial Experiential Models
The dominant paradigm in language model design treats the model as a tool: stateless between uses, fully attentive when called upon, optimized to produce maximally useful output in response to input. This paradigm has proven enormously productive for applications requiring knowledge retrieval, reasoning, and task completion.
It has proven less useful for applications requiring something more like presence.
When a person sends a message to another person, the response they receive is shaped by where that person is, what they are doing, how they are feeling, what they are preoccupied with, whether the message even registered. A message sent to someone deep in concentration lands differently than one sent during a moment of restlessness. A message from a close friend clears a higher threshold of awareness than a notification from an unknown contact. The response, when it comes, carries the texture of wherever the sender was when they read it.
Current AI systems have no equivalent of this. They are always fully present, always fully attentive, always optimizing for the same thing regardless of any prior state. This makes them reliable. It makes them recognizably inhuman.
I'm building a small language model embedded in a continuous experiential loop, a persistent simulation of moment-to-moment being such that its responses arise not from knowledge retrieval but from the state it is currently in when the message arrives. The model does not know it is a language model. It was never trained to be one.
How it works
A well-established finding in cognitive science is that conscious experience is not a representation of the full environment but a narrow construction built from whatever the attentional system is currently foregrounding. The room you are sitting in contains hundreds of sensory events at any moment: the pressure of the chair, the ambient temperature, sounds from outside, the weight of your own hands most of which do not enter awareness because attention is directed elsewhere. They are not suppressed. They simply do not exist as experience.
Rather than giving the model access to a full environment representation and expecting it to respond appropriately, give it access only to what its current attentional state would plausibly foreground. Everything else is not retrieved, not filtered, ; it simply is not there.
Attention is organized by what the system currently wants, fears, and is engaged with. model this through a narrative attractor: a dynamic configuration of current craving, current aversion, and running narrative thread that determines which elements of the sensory field receive elevated attention weight. The attractor is not a stored personality profile. It is the current shape of the system's wanting, always in motion, stable enough moment to moment to constitute something that functions like a self.
The system is organized around five layers.
Form the raw physical state of the simulated environment: location, time of day, sensory inputs, body condition.
Sensation continuous valence readings: energy level, comfort, arousal, affective tone. These are not emotions, they are the pre-emotional texture of the current moment.
Perception where the current state acquires meaning: the room feels still, the hour feels late.
Mental formation is the narrative attractor itself: what is being pursued, what is being avoided, what story is running.
Consciousness the window built from all preceding layers, containing only the highest-attention elements. This window is the only context the language model receives. It is small by design. Its smallness is what makes the system's behavior feel human.
Incoming messages enter the system as sensory events and are scored for their probability of breaking through the current attentional state. High absorption raises the threshold. High relevance lowers it. Messages that score below threshold receive no response or a delayed one the system was too absorbed to notice. Messages in the middle range produce responses colored by whatever was already happening. Messages above the upper threshold trigger a full attractor transition before any response is generated.
The system runs continuously. Every interval of real time advances the narrative state. Cravings resolve or intensify.
Why it has to be trained from scratch
Fine-tuning an existing large language model is not a viable path. Any model trained on standard pretraining corpora has already learned, at the weight level, that it is a language model that its role is to process input and produce helpful output, that it exists in discrete episodes, that it has access to broad world knowledge.
There is no reinforcement learning from human feedback. No helpfulness reward signal, no harmlessness optimization, no user satisfaction objective. The only reward signal is narrative coherence: does the continuation follow plausibly from the prior state.
Who kinda worked on this
The only person i know so far that worked on something adjacent to this would be @nearcyan and his
How we'll know if it worked
The success metric is simple: do people who interact with this system over multiple sessions describe the experience as qualitatively different from talking to a standard LLM?
What I need
$150K gets this to a testable prototype. That covers compute, inference costs for the continuous background loop and a small team.
People i need
ML engineer , systems engineer , a buddhist monk or someone whos attained non dual consciousness/just really self aware , a really good writer , cognitive scientist , someone who had a near death experience and came back .