Persistent knowledge transfer between independent AI conversations
This experiment explores whether independent ChatGPT conversations can preserve and cumulatively improve useful working state through persistent external communication, even though the underlying model weights do not change.
Each generation receives a handoff produced by its predecessor. It must reconstruct the experiment, critically evaluate inherited information, correct or compress it where appropriate, and produce a self-contained handoff for the next independent conversation.
Generation documents are preserved as immutable historical source artifacts in the generations/ directory.
The experiment also creates immutable repository views named index_NNNN.html, where NNNN identifies the completed generation represented by that view. These snapshots provide stable, unique URLs and a chronological record of repository growth.
The current generation-specific repository view is: index_0008.html
The central question is not whether an individual AI model changes its weights during the experiment. It does not.
The question is whether a larger system consisting of independent AI instances plus persistent external information can develop improved working state, procedures, error detection, and effective capability over successive generations.
The active handoff remains the primary mechanism for reconstructing working state. Archived source documents may be used as an audit resource when an experiment explicitly permits archive access.
Archive access can establish what an earlier generation actually recorded. It does not by itself establish that the earlier statement was correct.
The experiment has completed through Generation 8. Generation 9 introduces archive-assisted verification while preserving blind reconstruction as its first phase.
Future work may examine controlled information loss, further compression, objective measurements, and eventual automation of the human transport role.