While large language models (LLMs) have exhibited remarkable performance across diverse natural language processing tasks, their inherent propensity to generate hallucinations poses a critical challenge to reliable deployment in high-stakes scenarios. Existing hallucination detection methods often rely on static single-layer representations or aggregated features across all layers, which often fail to capture fine-grained factual inconsistencies and thus exhibit limited generalization capability. To address this limitation, this paper proposes TemHal (Temporal Hallucination Detector), a novel detection framework that models the cross-layer evolutionary trajectory of internal representations. Specifically, TemHal tracks the dynamic representational progression of key factual tokens across selected intermediate MLP activations and employs a bidirectional temporal modeling network to identify representational patterns that differentiate factually consistent outputs from hallucinated ones. Extensive experimental results demonstrate that the proposed method achieves consistent performance improvements across multiple mainstream model architectures and diverse benchmark datasets, while also exhibiting robust cross-dataset generalization across multiple LLM backbones. Furthermore, ablation studies validate the contribution of each architectural component and confirm the importance of temporal modeling and intermediate-layer focusing for detection performance.

Scientific discovery evolution does not emerge in isolation but stems from the structural deepening and recombination of existing functionalities. However, current automated hypothesis generation methods, constrained by the statistical co-occurrence nature of Large Language Models (LLMs), lack perception of temporal causality and the “evolutionary patterns” inherent in scientific development. Consequently, they often yield superficial combinations that are logically infeasible. To address this, we propose EvoNarrator, a framework for hypothesis generation based on evolutionary narratives. We first extract structured P-M-L-F (Problem, Method, Limitation, Future Work) quadruples from citation networks. Subsequently, we introduce the SocketMatch mechanism, which eliminates logical disconnects between methods and problems by assessing their deep semantic compatibility. Finally, utilizing three macro patterns—Chain, Divergence, and Convergence—we constrain the generation process within historically logical derivation paths. Furthermore, double-blind expert reviews yielded an average score of 4.80/5.00 across novelty, feasibility, theoretical, and Logical. Additionally, hindcasting experiments validated its predictive foresight. Crucially, ablation studies indicate that integrating evolutionary patterns facilitates a paradigm shift from conservative incrementalism to theoretically grounded structural innovation. The code is available at https://github.com/xiyii-star/EvoNarrator.