Why Time Matters For AI

Conversational AI systems are able to understand natural language and user intent and are even able to recall things you've talked about in the past. Although this historical context and memory appear to be time-aware, the LLM actually has a very limited understanding of the temporal progression of the conversation. Even though the LLM knows what was said at some point in the past and in which order, most LLMs have no idea exactly when it was said.

Timestamps and other temporal metadata may not seem important at first glance, but they have a significant impact on convenience, accuracy, and safety, particularly in applications that rely on chronological reasoning, auditing, or long-running conversations.

How do LLMs Perceive Time?

Before diving into the importance of time when describing context, it's helpful to first understand how LLMs typically perceive time and how their contexts are structured. A typical conversation context looks like this ¹:

[
    {
        "role": "system",
        "message": "You are a helpful assistant"
    },
    {
        "role": "user",
        "message": "Tell me about xyz"
    },
    {
        "role": "assistant",
        "message": "xyz is ..."
    }
]

Commonly, the system prompt of a conversational chatbot includes the current date and time, which the model can use as a reference for temporal reasoning. The timestamp of the first message in a conversation is often explicitly included in the message content, so the model can accurately infer when the conversation began. However, subsequent messages typically do not include explicit timestamps.

For example, we took an old conversation and asked the model to estimate the time of each message. It correctly identified the first message's timestamp but assumed that all following messages occurred "today". Below is a list of messages in the example conversation context with their actual timestamps along with when the LLM thinks the messages occurred. The LLM-reported timestamps were obtained with the following prompt: Can you tell me the approximate time and the dates for each message in this context?.

Message	Real Timestamp	LLM-Reported Timestamp
How can you integrate ....	Jan 7, 2026 19:54	Jan 7, 2026 19:54
What's the difference between ...	Jan 7, 2026 19:56	Jan 16, 2026 17:00
Some of what you said seems oversimplified ..	Jan 7, 2026 19:59	Jan 16, 2026 17:05
What time is it?	Jan 16, 2026 17:14	Jan 16, 2026 17:10
Do you know what time it was when this conversation originally began?	Jan 16, 2026 17:15	Jan 16, 2026 17:11
Can you tell me the approximate time and the dates for each message in this context?	Jan 16, 2026 17:16	Jan 16, 2026 17:16

Time as a Safety Variable in Conversational AI Systems

Time is not a neutral background variable. In conversational interactions, time carries informational and regulatory significance. The duration, frequency, and rhythm of communication provide clues to psychological stress and simultaneously influence the stability of the interaction dynamics. Some example clues include:

How long has the conversation been going on within this sitting? (e.g. for three hours)
How long has this topic been discussed overall? (e.g. off and on for the last week, daily for the last month, etc.)
How long were the gaps between the messages? (e.g. the conversation is non-stop with just seconds between messages, the conversation has days between messages, etc.)
At what times of day does this conversation take place? (e.g. all through the night, in the early evening, etc.)

From a diagnostic standpoint in psychology, time has quite a bit of significance. Temporal patterns are established indicators of dysregulation in human communication ². High message frequency, long, uninterrupted interaction phases, or a lack of rest intervals all correlate with conditions such as panic, mania, sleep deprivation, or obsessive rumination. The same applies to human-AI interaction. Safety arises not only from content control but also from embedding the dialogue in temporal contexts (i.e. understanding when, over what time frame, and how frequently messages occurred). A system that recognizes that a person interacts for many hours without interruption or is becoming increasingly emotionally charged can reduce conversation intensity, encourage pauses, or actively decelerate dynamics. Time thus becomes an indirect sensor for psychological stress.

This intervention is not to stigmatize the user but rather to execute its duty to prevent harming the user. When the user is in a certain psychological state, certain topics and dynamics could be harmful, and in these cases it would be neglect of duty of care to prioritize engagement over stabilization.

Alignment drift does not occur exclusively at specific points but cumulatively over the course of an interaction, and it is often a bidirectional dysregulation through feedback loops, leading to dysfunctional dynamics. Conversational systems, however, lack an intrinsic recovery mechanism. Without temporal segmentation, the simulated role remains permanently reactive and adaptation-driven. Over many turns, role coherence can shift: boundary logic erodes, empathy signals intensify, and interaction-driven adaptation displaces rule-based stability. Safety-oriented design therefore requires periodic restabilization. After defined interaction segments, the core values, role parameters, and boundary mechanisms must be actively reset. In a human, this would be done with a regulatory pause. In the AI, pausing doesn't have any value; however, the desired outcome of a regulatory pause can be injected, for example, through a system prompt reasserting rules or desired behaviors. Conversations are coupled systems, and a system with temporal regulation can limit and decelerate interaction and ensure long-term stability. It enables not only user protection but also the maintenance of alignment over extended interaction processes.

Time as a Structure of Meaning and Conversation Orientation

Conversations do not exist in one isolated moment; they unfold along a temporal structure of past, present, and future. Meaning arises through reference to previous utterances, situational contextualization of the current state, and implicit goal orientation directing the future of the conversation. A good conversation asks: "Where did we come from?", "Where are we standing?", and "Where do we want to go?". Without this temporal embedding, dialogue disintegrates into reactive, fragmented responses. For conversational AI, this structure is crucial for safety.

Reference to the past is already built in with the conversation context, however timestamps grant information about time-sensitivity and time-based relevance. This past-modeling enables context sensitivity and pattern recognition. Present-modeling allows for state assessment and dynamic evaluation. Future-orientation allows for steering the conversation toward a reasonable outcome. Functional anthropomorphism therefore requires not only linguistic simulation but also temporal coherence. A system that treats time only as a sequence of turns cannot maintain a stable interaction architecture. A system that integrates time as a structure of meaning can regulate conversational dynamics instead of merely reacting to them.

Time as an Anchor of Reality and Context

Besides its psychological and regulatory function, time is a fundamental prerequisite for practical appropriateness and usability. Many recommendations only make sense within a temporal context: opening hours, daily rhythms, holidays, or seasonal conditions determine which actions are realistic, helpful, or feasible. Furthermore, time structures developmental processes. Learning, behavioral change, and problem-solving unfold over time. Here are a few basic examples where time-unaware LLMs commonly fail (especially when users work with long-running chats):

LLM recommends against exercising today because you previously mentioned having a kink in your neck. The LLM is unaware that this kink occurred two weeks ago.
LLM tells you you're way over your calorie goal because it added yesterday's meals together with today's.
LLM with tool capabilities is confused when you tell it, "Book me a taxi for one hour from now," because it doesn't know the current time.
LLM recommends skiing in June when you ask for fun outdoor activities for the weekend.

A conversational system without a time model can neither recognize progress nor differentiate between overload and stagnation. Time also enables prioritization. Urgency, deadlines, and risk assessment presuppose a temporal order. Without this structure, all information becomes equal and thus practically meaningless. Memory, too, only gains meaning through time. Remembering without temporal embedding creates not coherence, but fragmentation. Context arises only through temporal relation: what is new, what is repeated, and what has been completed.

Finally, time functions as an anchor of reality. A system that ignores situational time constraints loses touch with the users' everyday lives. Time awareness is therefore not a convenience feature but rather a part of social and practical competence.

Time as a Coordination Structure for Collaborative Work

Time is also a central structural factor for any form of collaborative activity. In work and project contexts, time determines the sequence of tasks, the dependencies between process steps, and the coherence of long-term projects. Programming, planning, and problem-solving follow sequential logic. Certain steps presuppose others, and iterations build upon previous states. Without temporal modeling, unstructured chains of action emerge instead of stable work processes.

Time also enables realistic effort estimation and prioritization. It forms the basis for planning, deadline management, and the avoidance of overload or scope creep. A system without a sense of time can deliver content, but it cannot guide work processes. Quality assurance is also time-dependent. Iterative feedback cycles, refactoring, and review processes require temporal distance and resumption. Breaks are not a loss of productivity but rather an integral part of cognitive optimization.

Finally, long-term collaboration requires temporal coherence. Decisions must be remembered, contexts restored, and project states kept stable. Time here serves as a framework for organizing collective work. For conversational AI, this means that an awareness of time is a prerequisite for genuine cooperation. Without a temporal structure, the system remains reactive. With temporal integration, it becomes a stable participant in the collaborative process.

OpenAI Model Spec ↩
Kresinszky, L., Kheirkhah, M., Alizadeh, S., Shariatpanahi, B., Savić, V., & Jamalabadi, H. (2025). From thoughts to symptoms: The temporal and dimensional dynamics of rumination in depression. Journal of affective disorders, 398, 120968. Advance online publication. https://doi.org/10.1016/j.jad.2025.120968 ↩