TFE-GraphRAG: Temporal-Frequency Evidence Retrieval for Efficient Event-Based MLLMs

Event-based multimodal large language models (MLLMs) enable robust perception in high-speed and low-light scenarios. However, current event-based MLLMs often convert event streams into frame-like inputs, fixing the event evidence representation before reasoning, while independent token selection can omit evidence connections needed to compose an answer. We propose TFE-GraphRAG, a temporal-frequency GraphRAG framework with two major modules. Offline Wavelet-Guided Event Memory Construction (WEMC) applies CWT directly to raw events, uses patch-local frequency-group energy flux to perform CWT Adaptive Binning (CAB), and builds a patch–time–frequency Event Evidence Graph whose frequency-specific nodes each carry a semantic address key, one signed coefficient block pointer, and conservative typed edges. Online Query-Conditioned Evidence Retrieval (QCER) scores those lightweight addresses, expands candidate seeds into a compact connected component, and loads only the referenced signed CWT coefficient blocks for the MLLM. This division bounds online visual context while preserving relationships among the selected regions. On EventMind, TFE-GraphRAG achieves higher reported scores than EventFlash on all four metrics at both 3B and 7B scales. At 7B, it also improves EventChat-Sub GDC, while EventFlash remains higher on EventChat-Sub FGQA. In the controlled 3B comparison, TFE-GraphRAG provides $9.1\times$ the online throughput of the Dense Event Baseline while improving all four EventMind metrics.