Bridges load past text references by matching terms from the current message, with recent lines as a fallback. A shared name or common topic can retrieve a genuine passage that is not actually about the current situation.
Once inserted into a prompt without provenance, that passage may sound like established memory. The model is then completing from supplied evidence, not intentionally lying, but the result can still be a false association.
What was actually going wrong
Lexical overlap was treated as proof that a historical snippet referred to the current subject.
What I tried
- Increasing the number of keyword matches
- Removing timestamps to save prompt space
- Blending retrieved lines directly into the system identity
References remain short, carry source and date cues, prefer scoped bridge history, and are framed as possible context rather than verified fact.
Why it worked
The model can distinguish recalled evidence from current-session truth and decline an uncertain match.
Retrieval accuracy includes attribution, not just finding similar words.
Where EACI uses this today
Caelum's bridge history references are scoped to their channel and treated as supporting context.
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