Data attribution methods often compress the effect of each training data point into a single score, computed either at a final checkpoint or by aggregating quantities along the training trajectory. This static view hides how the contribution of a training data point changes during training. To overcome this limitation, we propose the Window-level Influence Estimator (WIE), a framework for estimating the contribution of a training data point zⱼ over a user-specified training window [t₁, t₂]. WIE models a leave-one-out-style counterfactual under a first-order linearization of SGD. An influence propagator P[t] = I − ηₜH[t] at each step models how parameter deviation is amplified or dampened by the loss landscape. Empirically, the window-level estimator reveals four distinct influence patterns: Early Influencer, Late Bloomer, Stable, Highly Fluctuating. Across multiple benchmarks, WIE improves mislabeled-data detection over the considered static and trajectory-based baselines. On BERT-IMDB with 40% label noise, WIE outperforms these baselines across the evaluated inspection ratios. On ViT-EMNIST, WIE reaches 70% recall for mislabeled data while inspecting up to 7.5× fewer candidates than TracIn requires to reach the same recall.

Large Language Models (LLMs) struggle to automate real-world vulnerability detection due to two key limitations: the heterogeneity of vulnerability patterns undermines the effectiveness of a single unified model, and manual prompt engineering for massive weakness categories is unscalable.To address these challenges, we propose MulVul, a retrieval-augmented multi-agent framework designed for precise and broad-coverage vulnerability detection. MulVul adopts a coarse-to-fine strategy: a Router agent first predicts the top- coarse categories and then forwards the input to specialized Detector agents, which identify the exact vulnerability types. Both agents use evidence retrieved from vulnerability knowledge bases to mitigate hallucinations. Crucially, to automate the generation of specialized prompts, we design Cross-Model Prompt Evolution, a prompt optimization mechanism where a generator LLM iteratively refines candidate prompts while a distinct executor LLM validates their effectiveness. This decoupling mitigates the self-correction bias inherent in single-model optimization. Evaluated on 130 CWE types, MulVul achieves 34.79% Macro-F1, outperforming the best baseline by 41.5%. Ablation studies validate cross-model prompt evolution, which boosts performance by 51.6% over manual prompts by effectively handling diverse vulnerability patterns.