Discrete Minds in a Continuous World: Do Language Models Know Time Passes? (2025)

Abstract: While Large Language Models (LLMs) excel at temporal reasoning tasks like event ordering and duration estimation, their ability to perceive the actual passage of time remains unexplored. We investigate whether LLMs perceive the passage of time and adapt their decision-making accordingly through three complementary experiments. First, we introduce the Token-Time Hypothesis, positing that LLMs can map discrete token counts to continuous wall-clock time, and validate this through a dialogue duration judgment task. Second, we demonstrate that LLMs could use this awareness to adapt their response length while maintaining accuracy when users express urgency in question answering tasks. Finally, we develop BombRush, an interactive navigation challenge that examines how LLMs modify behavior under progressive time pressure in dynamic environments. Our findings indicate that LLMs possess certain awareness of time passage, enabling them to bridge discrete linguistic tokens and continuous physical time, though this capability varies with model size and reasoning abilities. This work establishes a theoretical foundation for enhancing temporal awareness in LLMs for time-sensitive applications.

## introduction current llms are predominantly employed for time-insensitive tasks such as question answering (qa) @xcite , summarization @xcite , and translation @xcite . although both user input and llm generation inherently consume time, realworld temporal factors typically do not significantly influence these tasks' outcomes. for instance, neither the duration taken by a user to formulate a question nor the llm's response latency substantially alters the correctness of the final output. however, numerous time-sensitive tasks exist in real-world scenarios, including simultaneous translation @xcite , autonomous driving @xcite , real-time dialogue @xcite , and robotic control @xcite . in these contexts, environmental conditions continually evolve over time, resulting in input data variability and task dynamics heavily dependent on temporal progression. moreover, the time taken by a model to generate responses can critically impact task outcomes. hence, temporal progression becomes a decisive factor in these scenarios. this raises a fundamental question: are llms capable of perceiving and interpreting the passage of time? specifically, can llms recognize their temporal context and adapt their behavior accordingly? to date, extensive research has focused on llms' reasoning capabilities in time-related tasks, such as event ordering @xcite @xcite , temporal expression parsing @xcite @xcite , and duration computation @xcite @xcite . these studies evaluate models' ability to perform temporal reasoning but do not address whether models possess any awareness of the passage of time itself within our physical world. in this paper, we bridge this gap by investigating whether and how llms perceive the passage of time and adapt their behavior accordingly. we first analyze the mechanisms through which llms might understand time and propose the token-time hypothesis: llms can establish connections between text length and real-world temporal progression. through three complementary experiments, we systematically explore the nature and boundaries of this capability. our investigation begins with a dialogue duration judgment task that validates the token-time hypothesis by examining whether llms can accurately determine which conversation took longer based on different temporal cues. we find that models can indeed associate token count with elapsed time, though their ability to reconcile conflicting cues depends significantly on their reasoning capabilities. building on these insights, we then explore how this temporal awareness manifests in practical scenarios through an urgency-aware qa experiment, which reveals that models strategically reduce response length when users express time constraints while preserving answer quality, demonstrating a form of "temporal empathy." finally, our bombrush experiment extends this investigation to dynamic, interactive environments, showing how models adjust their reasoning depth and decisionmaking strategies under progressively increasing time pressure. this work makes several key contributions to our understanding of language model capabilities: ## related work temporal understanding and reasoning temporal understanding involves identifying the time scope (e.g., start and end time) of events in text, which often requires commonsense inference-for example, interpreting "during the second world war" as referring to the period from 1939 to 1945. temporal reasoning, in contrast, refers to deducing the temporal @xcite between events or between the query and text @xcite . recent studies have investigated whether llms possess basic temporal commonsense. @xcite benchmark llms across multiple datasets and prompting strategies, revealing limited and inconsistent performance across tasks like event ordering and duration reasoning. @xcite propose timearena, a simulated environment to test llms under temporal constraints, showing that models struggle with effective timesensitive planning. @xcite evaluate models on nli tasks involving temporal expressions and find weaknesses in understanding time-related comparisons. corresponding benchmark datasets in this area include timedial @xcite , tnli @xcite , and mc-taco @xcite , which focus on evaluating temporal inference, commonsense alignment, and fine-grained question answering involving time-related knowledge. in parallel, several studies aim to enhance temporal understanding by introducing time-aware training strategies. @xcite use multi-step fine-tuning and masked temporal indicators to improve commonsense inference across time-related tasks. tsm @xcite propose temporal span masking, targeting time expressions during pretraining to strengthen temporal representations. bitimebert @xcite incorporates bitemporal signals from news corpora to build timesensitive language models. ## llm's sense of time time perception (chronoception) in psychology refers to the subjective experience of time: how individuals sense durations and intervals between events, distinct from objective chronological time @xcite @xcite . building on this concept, we investigate whether similar mechanisms exist in ai systems. autoregressive decoder-only language models process text by decomposing sequences into conditional probability distributions that preserve temporal ordering @xcite . this creates a natural alignment between token positions and chronological progression: earlier tokens precede later ones, mirroring how events unfold in human experience. moreover, token quantity roughly corresponds to communication time in human interaction. while humans intuitively incorporate temporal dimensions in communication, a fundamental question emerges: do llms possess an analogous sense of time that influences their comprehension and generation? ## dialogue duration judgment our first experiment examines whether llms can associate text length (token-time) with temporal duration (wall-clock-time), and how this capability functions under various confounding factors. to systematically evaluate this, we designed a dialogue duration judgment task using paired samples from the chatbot_arena_conversations dataset foot_1 @xcite . each sample contains setting key characteristics purpose s1 no explicit temporal cues provided test inherent ability to infer temporal duration from t tok s1-hint textual hint: "generation time is proportional to number of tokens" evaluate if the model understands the hint: t tok ∝ t wall s1-count direct token counts provided for both dialogues test ability to judge duration from explicit t tok information s2 timestamps provided for input and response; consistent with text length test prioritization of explicit t wall cues over implicit t tok s2-m misleading timestamps: longer responses shown with shorter durations evaluate ability to prioritize t wall over contradictory t tok cues s2-m+ both misleading timestamps and accurate token counts provided determine which temporal domain (t tok vs t wall ) models prioritize when directly conflicting a user prompt with two responses (a or b) from different llms of substantially different lengths. we selected 300 samples where both responses were generated by high-quality models such as gpt-4 or claude, ensuring response quality would not negatively affect llms' decision-making. the tested llm must identify which dialogue is likely to require more time to complete, and provide justification for its judgment (see figure 3 for an example). ## conclusion we have investigated whether llms perceive and adapt to time passage, a capability distinct from temporal reasoning. through the proposed token-time hypothesis and three complementary experiments, we found that llms can, to some extent aware of the correlation between token-time and wall-clock time, indicating an emergent form of temporal awareness. this awareness manifests as strategic behavioral adaptations: e.g. response length adjustment to match urgency or adaptive decision-making under time pressure. future research might explore enhancing this capability through specialized training, investigating how temporal perception might address known limitations like overthinking.