The cleanest model here feels bitemporal: keep when the system learned a fact separate from when the fact was valid. A contradiction should usually close the old fact's validity window and link it to the replacement, while the default read returns only currently valid facts. Historical queries can still reconstruct what the agent believed at a past moment. The difficult part is confidence: low-confidence contradictions should create a reviewable conflict, not silently supersede evidence.
Building persistent memory for AI agents at Mnemoverse.
I write about the unglamorous half: forgetting, contradiction, and the moment an agent confidently repeats a fact you retracted.
The bitemporal split is the model the article circles without naming, and the table shows how far the default read sits from it: three of the six say plainly that an ordinary read still returns the older fact, and the current-state view is either a parameter the reader passes or a filter somebody still has to write or switch on. Your confidence point lands hardest on the two where a model decides at write time, because there a wrong contradiction closes a true fact without anyone seeing it happen. Where the writer names the record it replaces, there is no confidence to estimate, and the cost moves to the writer having to know what it is replacing. A reviewable conflict sits between the two: the store detects the collision, and closing the old validity window waits for a decision it can show you later.
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The cleanest model here feels bitemporal: keep when the system learned a fact separate from when the fact was valid. A contradiction should usually close the old fact's validity window and link it to the replacement, while the default read returns only currently valid facts. Historical queries can still reconstruct what the agent believed at a past moment. The difficult part is confidence: low-confidence contradictions should create a reviewable conflict, not silently supersede evidence.
The bitemporal split is the model the article circles without naming, and the table shows how far the default read sits from it: three of the six say plainly that an ordinary read still returns the older fact, and the current-state view is either a parameter the reader passes or a filter somebody still has to write or switch on. Your confidence point lands hardest on the two where a model decides at write time, because there a wrong contradiction closes a true fact without anyone seeing it happen. Where the writer names the record it replaces, there is no confidence to estimate, and the cost moves to the writer having to know what it is replacing. A reviewable conflict sits between the two: the store detects the collision, and closing the old validity window waits for a decision it can show you later.