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EviGraph is a proof-carrying selective recommendation system for temporal public-service knowledge graphs, using an LLM agent to link decision-critical evidence and a deterministic checker to validate recommendations. Evaluated on a bilingual Hong Kong public-service benchmark, it shows that distinguishing critical from non-critical evidence reduces unnecessary abstention, while extra verification can withdraw supported recommendations without improving decision quality.
BridgeMem proposes a new method for temporal knowledge graph forecasting by modeling dyadic transition residuals, demonstrating consistent improvements over baselines on standard benchmarks.
EdgeReMIND is a scalable memorization baseline for temporal multi-relational link prediction that achieves top performance on the TGB 2.0 benchmark, particularly on large datasets where embedding methods fail due to computational limits.
This paper proposes GRATE (Gated Rotary Attention for Temporal Encoding), a parameter-free temporal encoding method that enhances inductive knowledge graph foundation models by incorporating relative time differences and query-conditioned gating. It also introduces new inductive temporal knowledge graph benchmarks (GDELTIndT and WIKIIndT) to evaluate cross-dataset transfer, demonstrating improved performance over static base models.
This paper studies temporal knowledge graph forecasting under controlled distribution shifts using a synthetic generator that encodes recurrence, homophily, and periodicity. Experiments on seven architectures reveal signal-dependent robustness and limitations in model adaptivity to structural breaks.
This paper proposes AdaTKG, a method for temporal knowledge graph reasoning that uses adaptive memory to refine entity representations dynamically as new interactions occur, improving performance over static baselines.