Paper Detail
Xueshu Chen, Yan Wang, Zihao Xue, Jiefu Li, Zhenfang Liu, Jayden Chen, Zhen Bi, Jungang Lou
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 https://github.com/HuzhouNLP/C3M.
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@article{chen2026c3m,
title = {C3M: Cross-Session Multimodal Memory Maintenance for Long-Horizon Tasks},
author = {Xueshu Chen and Yan Wang and Zihao Xue and Jiefu Li and Zhenfang Liu and Jayden Chen and Zhen Bi and Jungang Lou},
year = {2026},
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.},
url = {https://arxiv.org/abs/2609.29735},
keywords = {cs.AI, cs.CL, cs.CV, cs.IR},
eprint = {2609.29735},
archiveprefix = {arXiv},
}
{}