Knowledge forgetting is a central challenge when adapting LLMs to new tasks. Prior studies indicate that pretrained knowledge is concentrated in the principal singular subspace of pretrained weight W₀; so recent Low-Rank Adaptation (LoRA) variants initialize LoRA in the minor subspace to steer early updates away from principal directions and mitigate forgetting. However, we observe that during fine-tuning, the update direction progressively shifts from the minor to the principal subspace, which is called as Singular-subspace Drift (SD), thereby allocating more energy to the directions that carry pretrained knowledge and leaving a persistent risk of forgetting. To address this issue, we propose Singular-subspace Drift Controlled LoRA (SDC-LoRA), which constrains the growth of update energy in the principal singular subspace of W₀ and thus mitigate SD. SDC-LoRA proposes Principal Subspace Energy-Controlled Learning, using Spectral Calibration factor 𝛾_sc to selectively downscale gradients along the principal singular subspace of W₀ while keeping minor-subspace updates unchanged. Across extensive experiments with LLaMA-3.1-8B-Instruct and Qwen2.5-7B-Chat on MetaMathQA and CodeFeedback, SDC-LoRA mitigates forgetting on MMLU, TruthfulQA, and HellaSwag while matching or improving GSM8K and HumanEval, offering a practical route to adapt LLMs without sacrificing prior knowledge.

Temporal Knowledge Graph Question Answering (TKGQA) aims to answer temporal questions using knowledge from Temporal Knowledge Graphs (TKGs).Existing LLM-based TKGQA methods typically utilize RAG-based or Agent-based paradigms, yet both struggle to construct reliable temporal evidence chains. RAG-based approaches primarily rely on semantic retrieval to fetch question-relevant contexts but overlook the structural dependencies within TKGs, leading to broken evidence chains, whereas iterative agents are prone to error propagation during multi-step reasoning.To address these limitations, we propose TECQA, a framework designed to construct temporal evidence chains for LLM reasoning. Firstly, TECQA employs structure-guided subgraph retrieval to capture structural dependencies and intermediate reasoning paths. Subsequently, it utilizes a k-nearest temporal neighbor pruning strategy to filter irrelevant noise while strictly preserving the continuous local history surrounding critical events. Finally, the retained temporal neighbors are serialized by temporal proximity to explicitly reconstruct a coherent temporal evidence chain. Extensive experiments on MultiTQ and CronQuestions demonstrate that TECQA achieves state-of-the-art performance, outperforming strong baselines by 45.3% particularly on complex queries. Code is available at https://github.com/SimonsLiu/TECQA.