Rashomon Memory: Towards Argumentation-Driven Retrieval for Multi-Perspective Agent Memory

Computer Science > Artificial Intelligence [Submitted on 4 Apr 2026] Rashomon Memory: Towards Argumentation-Driven Retrieval for Multi-Perspective Agent Memory Albert Sadowski, Jarosław A. Chudziak 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. We propose Rashomon Memory: an architecture where parallel goal-conditioned agents encode experiences according to their priorities and negotiate at query time through argumentation. Each perspective maintains its own ontology and knowledge graph. At retrieval, perspectives propose interpretations, critique each other's proposals using asymmetric domain knowledge, and Dung's argumentation semantics determines which proposals survive. The resulting attack graph is itself an explanation: it records which interpretation was selected, which alternatives were considered, and on what grounds they were rejected. We present a proof-of-concept showing that retrieval modes (selection, composition, conflict surfacing) emerge from attack graph topology, and that the conflict surfacing mode, where the system reports genuine disagreement rather than forcing resolution, lets decision-makers see the underlying interpretive conflict directly. Comments: Accepted to the EXTRAAMAS workshop at AAMAS 2026 Subjects: Artificial Intelligence (cs.AI) Cite as: arXiv:2604.03588 [cs.AI]   (or arXiv:2604.03588v1 [cs.AI] for this version)   https://doi.org/10.48550/arXiv.2604.03588 Focus to learn more Submission history From: Albert Sadowski [view email] [v1] Sat, 4 Apr 2026 04:29:50 UTC (5,566 KB) Access Paper: HTML (experimental) view license Current browse context: cs.AI < prev   |   next > new | recent | 2026-04 Change to browse by: cs References & Citations NASA ADS Google Scholar Semantic Scholar Export BibTeX Citation Bookmark Bibliographic Tools Bibliographic and Citation Tools Bibliographic Explorer Toggle Bibliographic Explorer (What is the Explorer?) Connected Papers Toggle Connected Papers (What is Connected Papers?) Litmaps Toggle Litmaps (What is Litmaps?) scite.ai Toggle scite Smart Citations (What are Smart Citations?) Code, Data, Media Demos Related Papers About arXivLabs Which authors of this paper are endorsers? | Disable MathJax (What is MathJax?)