🔍 Seeking Collaborators: Stateless Adaptive RAG

[coral swan]

Hey everyone, I’m an undergrad researcher working on a research-oriented RAG system targeting publication (AIED2027).

Research directions:

1. Stateless Cognitive Load Adaptation (CLAG)
   Infer learner cognitive level directly from query linguistics (no user history)
2. Adaptation-Grounding Resolution (AGR)
   Distinguish valid transformations vs hallucinations during generation

The core issue I’m exploring is what I call the adaptation–grounding tension: valid pedagogical transformations (analogies, simplifications) diverge from source form but current verification methods penalize them.
If you're a Final Year Undergrad/Graduate or PhD student whose research overlaps, I’d value collaboration/feedback. Happy to share a research brief upon request.

[amber pasture]

Isn't that so, because you can't model intention and association? Also interpretation in natural settings act independently. There's never 100% accuracy. Even 1+1 is not the same due to emotional loads.

Thus if you take the wrong assumption; because of understanding then whatever the method the outcome will be a wrong outcome. Here also is the tricky part. I have answered with a personal estimation of understanding in a limited context with a certain intention.

Non of which AI does. So, the tension might be the result of statistical inflexibility. I.e. the system cannot produce what a living system can. That is interjecingt this uncertainty.

Even my own answer is so abstract that it imaginary hurts. Going very deep thinking about thinking itself describing real time how this feels and what it does. This meta reflection is what AI can't do.

Even if you read my answer you might as well think that I produced AI hallucinations. But it's not. I just try to interpret what you mean and feel the process while doing it. We cannot even be sure the other can, will or feel the same with any question he or she encounters. That's the difficulty of catching a live system with an artificial dead one.

[coral swan]

You're raising a general point about subjective interpretation and human cognition, but my work is scoped to a narrower problem:
"Modeling controlled transformations over retrieved content under grounding constraints."
The tension I describe is not about human-level understanding, but about:
"How to distinguish valid semantic-preserving adaptations from hallucinations when surface form diverges from source text."

If you have thoughts specifically on evaluation or verification under these constraints, I’d be interested.

[limber helm]

i beleive latest engram by deepseek can help in it as leverages fact tables and can be used to further improve the llms

[coral swan]

Don't think so.