> ## Documentation Index > Fetch the complete documentation index at: https://docs.runtimelabs.ai/llms.txt > Use this file to discover all available pages before exploring further. # Tempo Tokens Tempo tokens are time-grounded embeddings or annotations that represent temporal structure — explicitly encoding timing, recurrence, and duration into a model’s input at inference time. **Time-Aware Transformers** Transformers today use fixed or learned positional encodings that reflect the order of tokens during training. That works well for language, but it doesn’t capture semantic time—like the difference between *Monday 3PM* and *Saturday 10AM*.\ There’s currently no built-in way to represent: * Recurring events * Temporal gaps or proximity * Day-of-week or time-of-day patterns * Future planning vs past reasoning **Language Models Lack Clock and Calendar Awareness**\ While LLMs can talk about time, they don’t *use* time as part of their internal context. They don’t know when something happened. They don’t model how long real-world events take. And they have no sense of what’s coming next unless it’s explicitly mentioned in a prompt. This limits their ability to plan, coordinate, or help users manage real-time tasks. By grounding model inputs in structured time—especially using external sources like `.ics` calendar data—we can start to bridge this gap and build agents that better understand the temporal context their users inhabit. **Proposed Function of Tempo Tokens** At training or inference, tempo tokens can be inserted as special annotations that; * Encode **absolute time**: @2025-04-30T14:00Z * Encode **relative time**: `@+3d`, `@past-1w` * Encode **calendar structure**: `@Monday`, `@weekend`, `@EoQ` * Encode **rhythmic recurrence**: `@biweekly`, `@every-Tuesday` These could be actual tokens (``) or embedded features added via a side-channel (like extra inputs in fine-tuning or adapters). Enables user adaptability and specification for agent tasks **Training-Time Augmentation** Use `.ics` or calendar data to: * Inject time-structured context into dialogue or task completion datasets. * Align text spans with event timing and sequence (e.g., doctor notes, project logs). * Generate training examples where **temporal cues improve performance** on downstream tasks (planning, scheduling, coordination, summarization). Example Transform ```txt theme={null} Submit payroll to HR next Tuesday May 13th. ``` into: ```txt theme={null} "Submit payroll to HR. " ``` **Inference-Time Augmentation** At inference: * Inject known temporal signals from `.ics` context into the prompt to improve grounding * Align responses with upcoming events, time constraints, or temporal reasoning goals * Enable planning agents to prioritize actions in realistic and user-defined time horizons **Use Cases with `.ics` Integration** **Chronologue Command Agent** * Converts `.ics` entries into tempo tokens for prompt construction * Use upcoming event timing to suggest actions, order and organize tasks, detect and manage conflicts in scheduling **Reasoning and CoT** * Tempo tokens give context to memory traces: when it happened, how often, what comes next **Time-Aware Retrieval and Summarization** * "Summarize the last week's events" --> uses tempo-annotated memory traces to construct a coherent and verifiable narrative *** Feasibility: * Can be done without architecture changes using adapter layers or embedding projection (to link events in chronological time) * Using time as an index dimension Logistically: * `.ics` data is structured and timestamped, making it the ideal import format for tempo token generation -- assembling largescale datasets and synthetic datasets * Many existing datasets (project logs, Github commits, emails) could be aligned with time *** Showcasing how an `.ics` calendar event can be parsed into tempo tokens -- these tokens can be retrieved to augment inference-time system-level prompts for temporally grounded chain-of-thought (CoT) reasoning. 1. Sample `.ics` Event ```ics theme={null} BEGIN:VEVENT UID:shipment-arrival-20250430T150000Z@chronologue.ai DTSTAMP:20250429T120000Z DTSTART:2025-04-30T15:00:00Z DTEND:2025-04-30T15:30:00Z SUMMARY:Inbound Shipment — Med Supplies (Batch #23B) LOCATION:Receiving Dock 3 DESCRIPTION:Urgent delivery. Requires cold chain transfer to Storage A by 16:00. END:VEVENT ``` 2. Structured Event JSON ```json theme={null} { "uid": "shipment-arrival-20250430T150000Z@logistics.ai", "title": "Inbound Shipment — Med Supplies (Batch #23B)", "start": "2025-04-30T15:00:00Z", "end": "2025-04-30T15:30:00Z", "location": "Receiving Dock 3", "description": "Urgent delivery. Requires cold chain transfer to Storage A by 16:00." } ``` 3. Tempo Tokens for Inference Context ```txt theme={null} ``` These tokens give the agent: * Exact time of arrival * Deadline for transfer * Event type (`shipment`) * Urgency and task completion constraints * Location grounding 4. Injected CoT Prompt for Temporal Reasoning Prompt: ``` You are coordinating logistics for medical shipments. Use the schedule below to assign responsibilities and detect any conflicts or risks. ``` System Prompt ```txt theme={null} Upcoming Event: Inbound Shipment — Med Supplies (Batch #23B) Current cold chain staff availability: - Luis (shift ends at 15:15) - Priya (available 14:00–18:00) - Forklift 1 in use until 14:55 Recent delivery delays: 12–18 min average at Receiving Dock 3 ``` Simulated Chain-of-Thought Output ``` The shipment is scheduled for 15:00 with a cold chain deadline at 16:00. Luis’s shift ends too early to ensure delivery, so Priya should be assigned. There’s a risk of a 12–18 minute delay at Receiving Dock 3, which means backup cold chain gear should be pre-cooled in Storage A. Forklift 1 will be available in time for transfer. Assign Priya and notify Receiving Dock to prioritize unloading. ``` ### **Inference Benefits** Tempo tokens allow the model to: * Predictively allocate resources based on event timing * Reason about deadlines, urgency, and gaps * Align agents (human or AI) with scheduled activity * Anticipate failure modes (e.g., delays, shift coverage, equipment availability) Required Fields | **Category** | **Token Format** | **Examples** | **Purpose** | | --------------- | ------------------------------------------- | ------------------------------------- | -------------------------------------------- | | `datetime` | `` | `` | Absolute event anchor | | `weekday` | `` | `` | Weekly structure awareness | | `timeofday` | `` | ``, `` | Task alignment and load distribution | | `duration` | `` | `` | Task commitment estimation | | `deadline` | `` | `` | Time-critical planning | | `recurrence` | `` | `` | Rhythmic events, repeated action logic | | `urgency` | `` / `` | `` | Task prioritization | | `event-type` | ``, `` | `` | Agent routing and classification | | `location` | `` | `` | Coordination and resource allocation | | `now` | `` | `` | Relative timing reference | | `relative-time` | `` / `` | `` / `` | Temporal comparison and windowing | | `status` | `` / `` | `` | Process and planning state | | `availability` | `` / `` | `` | Temporal resource constraints | | `conflict` | `` | `` | Conflict detection | | `soft-boundary` | `` / `` | `` | Non-hard constraints for collaborative plans | Optional Fields | **Category** | **Token Format** | **Examples** | | -------------- | ------------------------------------------------------- | ---------------------------------- | | `window` | `` | `` | | `phase` | `` | `` | | `task-linkage` | `` / `` | `` | | `reschedule` | `` | | | `handoff` | `` / `` | | | `buffer-time` | `` | | | `shift` | `` / `` | | | `type` | `` / `` | | | `milestone` | `` | `` | | `recency` | `` | `` | | `delay-risk` | `` | | | `reminder` | `` | |