The escalating complexity of modern codebases has intensified the need for code retrieval systems capable of interpreting cross-component change intents—a capability fundamentally absent in conventional function-level search paradigms. While recent research has improved alignment between queries and code snippets, retrieving contextually relevant code for certain change request remains underexplored. To bridge this gap, we present RepoAlignBench, the first benchmark designed to evaluate repository-level code retrieval for change request-driven scenarios, encompassing 52k columns. The benchmark shifts the paradigm from function-centric retrieval to holistic repository analysis. In addition, we propose ReflectCode, an adversarial reflection-augmented dual-tower architecture featuring disentangled code_encoder and doc_encoder towers. Our framework dynamically integrates syntactic patterns, function dependency, and semantic expansion intent through LLM. Comprehensive evaluations demonstrate that ReflectCode achieves 12.2% Top-5 Accuracy and 7.1% Recall improvements over state-of-the-art baselines.

Identifying causal relationships rather than spurious correlations between words and class labels plays a crucial role in building robust text classifiers. Previous studies proposed using causal effects to distinguish words that are causally related to the sentiment, and then building robust text classifiers using words with high causal effects. However, we find that when a sentence has multiple causally related words simultaneously, the magnitude of causal effects will be significantly reduced, which limits the applicability of previous causal effect-based methods in distinguishing causally related words from spuriously correlated ones. To fill this gap, in this paper, we introduce both the probability of necessity (PN) and probability of sufficiency (PS), aiming to answer the counterfactual question that ‘if a sentence has a certain sentiment in the presence/absence of a word, would the sentiment change in the absence/presence of that word?’. Specifically, we first derive the identifiability of PN and PS under different sentiment monotonicities, and calibrate the estimation of PN and PS via the estimated average treatment effect. Finally, the robust text classifier is built by identifying the words with larger PN and PS as causally related words, and other words as spuriously correlated words, based on a contrastive learning approach name CPNS is proposed to achieve robust sentiment classification. Extensive experiments are conducted on public datasets to validate the effectiveness of our method.

Role-playing is a crucial capability of Large Language Models (LLMs), enabling a wide range of practical applications, including intelligent non-player characters, digital twins, and emotional companions. Evaluating this capability in LLMs is challenging due to the complex dynamics involved in role-playing, such as maintaining character fidelity throughout a storyline and navigating open-ended narratives without a definitive ground truth. Current evaluation methods, which primarily focus on question-answering or conversational snapshots, fall short of adequately capturing the nuanced character traits and behaviors essential for authentic role-playing. In this paper, we propose CharacterBox, which is a simulation sandbox designed to generate situational fine-grained character behavior trajectories. These behavior trajectories enable a more comprehensive and in-depth evaluation of role-playing capabilities. CharacterBox consists of two main components: the character agent and the narrator agent. The character agent, grounded in psychological and behavioral science, exhibits human-like behaviors, while the narrator agent coordinates interactions between character agents and environmental changes. Additionally, we introduce two trajectory-based methods that leverage CharacterBox to enhance LLM performance. To reduce costs and facilitate the adoption of CharacterBox by public communities, we fine-tune two smaller models, CharacterNR and CharacterRM, as substitutes for GPT API calls, and demonstrate their competitive performance compared to advanced GPT APIs. The code is available at https://github.com/Paitesanshi/CharacterBox.