C3M: Cross-Session Multimodal Memory Maintenance for Long-Horizon Tasks

Abstract:Long-horizon tasks require preserving and later recovering cross-session evidence under a bounded, query-blind memory budget. Existing compression can discard fine-grained visual cues or conflate semantically similar but incompatible observations. We present C3M, a cross-session multimodal memory organization that maintains a bounded active index over persistent source text-image evidence. Relation-aware updates consolidate safe redundancy while preserving complementary and incompatible records. At query time, budgeted routing selects useful index pages and expands their associated source evidence under a fixed reader budget. Together, these mechanisms establish a compact, provenance-preserving multimodal memory organization for cross-session long-horizon tasks, retaining temporal distinctions and source links required for reliable downstream reasoning. Code is available at this https URL.

Artificial Intelligence (cs.AI); Computation and Language (cs.CL); Computer Vision and Pattern Recognition (cs.CV); Information Retrieval (cs.IR)

arXiv:2609.29735 [cs.AI]

(or arXiv:2609.29735v1 [cs.AI] for this version)

https://doi.org/10.48550/arXiv.2609.29735 Focus to learn more arXiv-issued DOI via DataCite (pending registration)

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