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Description
Summary
Spreading activation with lateral inhibition and temporal decay over a dynamic memory graph, replacing static vector similarity for retrieval. Triple Hybrid Retrieval fuses geometric embeddings with activation-based graph traversal.
Source: arXiv 2601.02744 — "SYNAPSE: Episodic-Semantic Memory via Spreading Activation" (January 2026)
Technique
Agent memory is modeled as a dynamic graph. Retrieval activates a seed node (the query embedding's nearest neighbor), then propagation spreads through edges with decay factor lambda^depth. Lateral inhibition suppresses already-activated neighbors to prevent echo-chamber retrieval. Temporal decay discounts older edges. Triple Hybrid Retrieval fuses:
- Standard vector similarity (existing Qdrant path)
- Spreading activation over the entity/edge graph
- BM25 keyword match
Outperforms SOTA on LoCoMo multi-hop and temporal reasoning benchmarks.
Applicability to Zeph
HIGH. Zeph already has:
- graph-memory feature with entities/edges/communities tables in SQLite
- graph_recall BFS traversal in SemanticMemory
- Qdrant for vector similarity
- BM25 + RRF hybrid search in zeph-memory
Spreading activation is a direct enhancement to graph_recall: instead of fixed-depth BFS, use activation scores that decay with graph distance and edge age. No new infrastructure required — extends the existing entity graph traversal with a scoring function.
Implementation sketch
- New scoring function spreading_activation(seed_entity_id, decay_lambda, max_hops) in zeph-memory/src/graph/
- Activation score: a(node) = sum(a(parent) * lambda) * recency_weight(edge.timestamp)
- Lateral inhibition: skip nodes already at activation threshold
- Fuse with existing RRF combiner in SemanticMemory::recall()
- Config: [memory.graph] spreading_activation = false, decay_lambda = 0.7, max_hops = 3
Complements #1821 (MAGMA multi-graph) and #1839 (AOI hierarchical memory) — spreading activation is a retrieval strategy that works on any graph structure.