Earlier research has shown that metaphors influence human decision-making, raising the question of whether metaphors also influence large language models (LLMs)’ reasoning pathways, given that their training data contain a large number of metaphors. In this work, we investigate the problem in the scope of the emergent misalignment problem, where LLMs can generalize patterns learned from misaligned content in one domain to another domain. We find that *metaphors can act as bridges for behavior transfer, enabling misaligned behavior learned in one domain to an unrelated domain*. Based on this, We find strong evidence that metaphors in training data contribute to cross-domain misalignment in LLMs’ reasoning outputs. With metaphor-based interventions during continued pre-training and fine-tuning for inducing misalignment, models exhibit significantly different degrees of emergent cross-domain misalignment. We also observe similar effects in re-alignment settings. As we further investigate this phenomenon, we find that metaphors are linked to the activation of latent features in large reasoning models. By monitoring these latent features, we design a detector that predicts misaligned content with high accuracy.

Multimodal GUI agents generally operate on raw visual and textual observations, which creates a fundamental scalability challenge. While current state-of-the-art frameworks predominantly rely on inference-intensive test-time scaling or the accumulation of unbounded raw logs to maintain task coherence, we attribute the underlying bottleneck to insufficient state abstraction.To address this, we propose HiSA, a hierarchical state abstraction approach that actively restructures knowledge rather than passively retaining historical information by organizing raw histories into a three-level hierarchy of abstracted steps, refined contexts, and induced patterns.By synthesizing high-dimensional observations into compact semantic states, HiSA decouples reasoning efficacy from context length, enabling precise and scalable decision-making as interaction histories grow.When evaluating using Spider2-V, our approach establishes a new state-of-the-art, achieving a 40.58% success rate while reducing token consumption by 69.85% and monetary costs by 55.10% compared to the best-performing baseline.

Despite advances in legal NLP, no comprehensive evaluation of Transformer-based models customized for legal tasks (referred to as ’legal-specific’ models in this paper) exists for contract classification tasks. To address this gap, we present an evaluation of 13 legal-specific transformer-based models on 3 English-language contract classification tasks and compare them with 9 generalist models. The results show that legal-specific models consistently outperform generalist models, especially on tasks requiring nuanced legal understanding. They also help reduce misclassification of rare classes in imbalanced datasets. Legal-BERT and Contracts-BERT establish new SOTAs on two of the three tasks, despite having 69% fewer parameters than the best-performing generalist models. We also identify CaseLaw-BERT and LexLM as strong additional baselines for contract classification. Our results highlight the shortcomings of generalist models, emphasizing the need for domain-specific customization, particularly in the context of legal applications.