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 stateof-the-art performance, outperforming strong baselines by 45.3% particularly on complex queries.

With the rapid advancement of Large Vision-Language Models (VLMs), concerns about their potential misuse and abuse have grown rapidly.Prior research has exposed VLMs' vulnerability to jailbreak attacks, where carefully crafted inputs can lead the model to produce content that violates ethical and legal standards.However, current jailbreak methods often fail against cutting-edge models such as GPT-4o.We attribute this to the overexposure of harmful content and the absence of stealthy malicious guidance.In this work, we introduce a novel jailbreak framework: Multi-Modal Linkage (MML) Attack.Drawing inspiration from cryptography, MML employs an encryption-decryption process across text and image modalities to mitigate the over-exposure of malicious information.To covertly align the model's output with harmful objectives, MML leverages a technique we term evil alignment, framing the attack within the narrative context of a video game development scenario.Extensive experiments validate the effectiveness of MML.Specifically, MML jailbreaks GPT-4o with attack success rates of 99.40% on SafeBench, 98.81% on MM-SafeBench, and 99.