RECIPE-TKG: From Sparse History to Structured Reasoning for LLM-based Temporal Knowledge Graph Completion

Ömer Faruk Akgül, Feiyu Zhu, Yuxin Yang, Rajgopal Kannan, Viktor Prasanna

Abstract: Temporal Knowledge Graphs (TKGs) repre- sent dynamic facts as timestamped relations between entities. While Large Language Models (LLMs) show promise for TKG completion, current approaches typically apply generic pipelines (neighborhood sampling, supervised fine-tuning, uncalibrated inference) without task-specific adaptation to temporal relational reasoning. Through systematic analysis under unified evaluation, we reveal three key failure modes: (1) retrieval strategies miss multi-hop dependencies when target entities are not directly observed in history, (2) standard fine- tuning reinforces memorization over relational generalization, and (3) uncalibrated generation produces contextually implausible entities. We present RECIPE-TKG, a parameter- efficient framework that addresses each limitation through principled, task-specific design: rule-based multi-hop sampling for structural grounding, contrastive fine-tuning to shape relational compatibility, and test-time semantic filtering for contextual alignment. Experiments on four benchmarks show that RECIPE-TKG outperforms prior LLM-based methods across input regimes, achieving up to 22.4% relative improvement in Hits@10, with particularly strong gains when historical evidence is sparse or indirect.