SuperMem: Omni-Native Super-Hypergraph Memory for LLM Agents

Long-term memory lets a frozen large language model (LLM) agent answer from experience beyond its context window, increasingly omni-modal streams of text, images, audio and video. Agent memory has grown from flat stores to hypergraphs and from text to all modalities, yet hypergraph memories index only text while all-modal ones stay flat or pairwise. This leaves three challenges: modality collapse in storage, the modality gap in recall, and repetition and revision in update. We propose SuperMem, an omni-native super-hypergraph memory whose hyperedges bind each unit's text with its native media nodes and nest units into episodes, with no extra LLM call. Joint cross-modal recall credits each modality above its own per-query median and delivers whole units with raw media, while conservative consolidation merges repeats and supersedes revisions without deleting. On nine text, multimodal and omni-modal benchmarks, SuperMem has the best average against 13 memory baselines on each of three frozen backbones, 9.5 points above the strongest.