Large Language Models (LLMs) face a fundamental challenge with delayed disambiguation: How is the meaning of an ambiguous word updated when clarifying context arrives only after it has been processed? While LLMs possess the latent capacity to resolve such ambiguities—as revealed when a full, non-causal context is provided—their unidirectional architecture prevents immediate updates. We investigate the underlying computational mechanism and show this semantic re-evaluation is deferred to subsequent tokens in a process we term “Deferred Semantic Drift (DSD)”. Through targeted analysis of attentional pathways, we find that later tokens actively retrieve context-dependent “informational packets” from the ambiguous word’s value vector to steer the final interpretation. We demonstrate this mechanism in metaphor comprehension and provide causal validation by steering model outputs towards literal or metaphorical meanings via targeted activation interventions. This research uncovers a key computational strategy for meaning construction, offering crucial insights for understanding and guiding the behavior of LLMs.

Human cognition excels at extending knowledge through analogy, where word meanings evolve along structured pathways from concrete prototypes to abstract senses via metaphor and metonymy. Do Large Language Models (LLMs) internalize this generative logic, or merely mimic statistical patterns? To investigate this, we introduce CogEvolve, a cognitive linguistic benchmark designed to test these evolutionary pathways across textual and visual modalities. Our evaluation reveals a distinct cognitive profile: models function as “Super-Associators” expert at static recognition yet fail at causal reasoning. In text, they exhibit a Frequency-Primacy Conflation, confusing statistical prevalence with cognitive basicness. Crucially, this reasoning collapses further in the visual domain. We term this deficit the Ungrounded Arrow: models possess high-fidelity concept representations (the “dots”) but lack the transformational operators (the “arrows”) essential for true relational understanding.

Bragging, as a pervasive social-linguistic phenomenon, reflects complex human interaction patterns. However, the understanding and generation of appropriate bragging behavior in large language models (LLMs) remains underexplored. In this paper, we propose a comprehensive study that combines analytical and controllable approaches to examine bragging in LLMs. We design three tasks, bragging recognition, bragging explanation, and bragging generation, along with novel evaluation metrics to assess the models’ ability to identify bragging intent, social appropriateness, and account for context sensitivity. Our analysis reveals the challenges of bragging in the social context, such as recognizing bragging and responding appropriately with bragging in conversation. This work provides new insights into how LLMs process bragging and highlights the need for more research on generating contextually appropriate behavior in LLMs.