I’m an independent researcher who has arrived here, at AI safety through an unusual path, outside the standard academic or industry pipelines. Along this journey, I encountered the recurring problem of large language models exhibiting “hallucinations”[^1] - outputs that can be inconsistent or outright fabricated - and became curious whether a more systematic self-correction mechanism could exist.

This led me to develop a concept I call **Recursive Cognitive Refinement (RCR)**, intended to help large language models detect and reduce internal contradictions and factual errors across multi-turn interactions. I’m posting these early ideas here, seeking constructive criticism, potential collaboration, and feedback from alignment-minded readers, researchers and experts.

Modern LLMs often produce highly fluent but occasionally contradictory or false statements. The typical solutions, including chain-of-thought prompting or RLHF, offer incremental improvements but they rarely force a model to revisit and refine its own outputs in a structured loop. Each query is still largely independent, so errors from prior turns can become ‘baked in’. RCR aims to close this gap by introducing repeated self-checks, requiring the model to examine and refine prior statements until contradictions or missteps are resolved or until a time limit is reached.

Some details of RCR remain unpublished, both for intellectual property reasons and to ensure responsible development. My accompanying white paper covers the conceptual foundation. I’m really hopeful RCR might noticeably reduce hallucinations by compelling a model to spot and correct its own errors across multiple turns, instead of relying on a single-pass solution. Potential benefits include improved consistency and possibly better alignment, but pitfalls exist—like increased overhead or the possibility of “infinite loop” refinements that don’t truly fix factual inaccuracies.

I’d value perspectives on where RCR might conflict with interpretability or alignment strategies. For instance, does repeatedly forcing a model to refine previous answers risk entrenching subtle biases or overshadowing external forms of oversight? 
From a safety standpoint, is this approach genuinely helpful, or might it mask deeper issues? I’m also, very aware, that as an outsider, developing or validating RCR will require collaboration with experienced AI safety researchers. If anyone has suggestions on labs, forums, or minimal pilot tests to demonstrate partial code or results, I’d be grateful and open to collaboration and/or guidance.

Ultimately, I see RCR as an attempt to embed a self-correcting function into LLM dialogue, moving us beyond single-pass generation. If this idea proves viable, it could help achieve safer, more consistent AI outputs. If not, critiques will guide me on how best to refine or discard the concept. Either way, I eagerly await and welcome serious discussion, pointers to relevant communities, or cautionary notes. Thanks for reading, and for any insight you can share.

Michael Xavier Theodore
 

[^1]: “Hallucinations” typically refer to confident but false LLM outputs.