Interpretability methods are increasingly invoked to diagnose Large Language Model (LLM) failures, but most evidence comes from short prompts or toy templates, leaving their utility on practical benchmarks unclear. We cast failure diagnosis as contrastive attribution over target–contrast token pairs, attributing the logit gap between incorrect and correct output tokens to input tokens and internal states, and introduce a batch-packed multi-target backpropagation scheme that scales cross-layer attribution graphs to long-context inputs. Instantiated with LRP-style attribution, the framework is evaluated on IFEval, GAIA2, MATH, and EvalPlus across multiple model sizes and post-training checkpoints. Along the four axes of coverage, visibility, reliability, and dynamics, contrastive attribution surfaces recurring failure modes such as underweighting task-relevant tokens and overweighting distractors, demonstrating its practical value as a scalable diagnostic tool for realistic LLM failures.

Vision-Language Models (VLMs), such as CLIP, have exhibited significant advancements in recognizing visual concepts through natural language guidance. However, adapting these models to downstream tasks remains challenging. Existing adaptation methods either overlook the structural knowledge between the text and image modalities or create overly complex graphs containing redundant information for alignment, leading to suboptimal classification performance and increased computational overhead. This paper proposes a novel adapter-tuning methodology named Homogeneous Graph Adapter (HomoGraphAdapter), which transforms diverse textual and visual descriptions into a unified set of node representations and establishes edges between nodes for inter-modal and cross-modal semantic alignment. We leverage a straightforward homogeneous Graph Neural Network (GNN) to adapt positive and negative classifiers across text and image modalities. The classifiers comprehensively enhance the performance for few-shot classification and OOD generalization. Compared with the SOTA approach HeGraphAdapter, HomoGraphAdapter improves classification accuracy by an average of 1.51% for 1-shot and 0.74% for 16-shot on 11 datasets, while also reducing both precomputation time and training time.