When Reconstruction Survives the Easy Cases but Fails the Hard Ones
I think the project has now reached the point where the real question is no longer:
“Can AI reconstruct a messy file?”
It is:
“Can the reconstruction survive hostile edge cases?”
Because the dangerous failures are not usually obvious hallucinations anymore.
They are procedural distortions that still produce perfectly reasonable-looking summaries.
A few of the edge cases we are now stress-testing against:
- conflicting timestamps across systems
- forwarded emails that subtly change evidentiary meaning
- duplicate-looking records that are procedurally distinct
- downstream documents inheriting unresolved assumptions
- summaries that quietly convert uncertainty into settled fact
- contradictory narratives that are both internally coherent
- missing participants later restored into chronology
- records that only become contradictory after awareness timing is reconstructed
The interesting part is that many of these files still generate “clean” outputs.
The distortion only becomes visible once chronology and procedural context are restored.
That has shifted the testing focus away from:
“Did the AI summarize the file correctly?”
and toward:
“Did the reconstruction preserve the uncertainty structure of the file?”
At this point, the hardest problems are no longer document problems.
They are procedural-state problems.
Meaning:
- when did an assumption start propagating?
- when did uncertainty stop being treated as uncertainty?
- when did later records begin inheriting disputed context as if it were settled fact?
That is where a lot of the reconstruction logic either survives or breaks.
For people here who deal with messy litigation, investigations, compliance reviews, or chronology-heavy disputes:
What are the nastiest reconstruction failure modes you have personally encountered?
Not legal theory.
The actual procedural edge cases that turn a file into a reconstruction sinkhole.