Temporal Knowledge Graphs (TKGs) represent 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.

Code localization–identifying precisely where in a codebase changes need to be made–is a fundamental yet challenging task in software maintenance. Existing approaches struggle to efficiently navigate complex codebases when identifying relevant code snippets.The challenge lies in bridging natural language problem descriptions with the target code elements, often requiring reasoning across hierarchical structures and multiple dependencies.We introduce LocAgent, a framework that addresses code localization through a graph-guided agent.By parsing codebases into directed heterogeneous graphs, LocAgent creates a lightweight representation that captures code structures and their dependencies, enabling LLM agents to effectively search and locate relevant entities through powerful multi-hop reasoning.Experimental results on real-world benchmarks demonstrate that our approach significantly enhances accuracy in code localization.Notably, our method with the fine-tuned Qwen-2.5-Coder-Instruct-32B model achieves comparable results to SOTA proprietary models at greatly reduced cost (approximately 86% reduction), reaching up to 92.7% accuracy on file-level localization while improving downstream GitHub issue resolution success rates by 12% for multiple attempts (Pass@10). Our code is available at https://github.com/gersteinlab/LocAgent.