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This survey paper connects self-evolving LLM-based agents with dynamic graph transformation, proposing a framework to model agent state as dynamic graphs and organizing existing methods for their evolution.
This paper introduces X-AddGraph, a post-hoc explainability framework for the AddGraph dynamic graph anomaly detector, using a dual spatial-temporal attribution mechanism that preserves detection performance exactly while providing explanations.
A tweet expressing amazement at the concept of dynamic agent orgs—self-evolving multi-agent systems where the graph structure rewrites itself during execution.
Proposes SG-JEPA, a joint spiking embedding predictive architecture for large-scale dynamic graphs that partitions nodes into context and target sets along the temporal dimension to learn predictive embeddings, achieving competitive performance on node classification while scaling to graphs with 13 million edges and avoiding complex self-supervised mechanisms.
This paper investigates whether aggregate structural invariants, specifically spectral bounds, can accelerate continuous subgraph matching (CSM) over dynamic graphs. It characterizes limitations of lazy spectral maintenance, shows exact maintenance is affordable when selective, and demonstrates pruning power of up to 51% in benchmarks.