Rashomon Memory: Towards Argumentation-Driven Retrieval
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Rashomon Memory: Towards Argumentation-Driven Retrieval for Multi-Perspective Agent Memory
Announce Type: replace Abstract: AI agents operating over extended time horizons accumulate experiences that serve multiple concurrent goals, and must often maintain conflicting interpretations of the same events. A concession during a client negotiation encodes as a ``trust-building investment'' for one strategic goal and a ``contractual liability'' for another. Current memory architectures assume a single correct encoding, or at best support multiple views over unified storage.