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M:3L research

Event Sequence and Temporal Models

M:3L develops value-grounded event representations that encode what happened, when it happened, and which outcome followed.

Overview

The laboratory is developing a foundational event-embedding model that jointly captures event meaning, temporal progression, and outcome or value signals. The work is intended for multi-domain retrieval, monitoring, diagnostics, and predictive modeling.

Applications include anomaly and churn detection, next-action and value prediction, causal and counterfactual analysis, and grounded conversational investigation through a Semantic Vector MCP.

Demonstrated M:3L research

  • A foundational event-embedding model that jointly represents event semantics, temporal progression, and downstream outcome or value signals.
  • A Semantic Vector MCP concept for grounded conversational investigation over time-sensitive event sequences.

Technical capabilities

  • Temporal and event-sequence embeddings
  • Time-sensitive retrieval and agent memory
  • Anomaly, churn, lifetime-value, and health modeling
  • Next-action and outcome prediction
  • Causal and counterfactual analysis

Representation objective

The research seeks representations that preserve event meaning, order, timing, and downstream consequences. This differs from embeddings that treat an event stream as an unordered collection of labels or text fragments.

  • What happened: semantic event identity
  • When it happened: temporal spacing and progression
  • What followed: outcome, value, or health signals

Research directions

  • Semantic and temporal representations that transfer across domains
  • Outcome-aware and value-grounded sequence embeddings
  • Temporal retrieval and memory for tool-using agents
  • Causal and counterfactual analysis over event trajectories

Potential applications

  • Monitoring and diagnostics
  • Anomaly and churn detection
  • Behavioral and trajectory prediction
  • Next-action and lifetime-value modeling
  • Time-sensitive retrieval

These are relevant application domains, not claims that every application is deployed.

Connected research

  • Agentic AI: M:3L develops autonomous, tool-using single-agent and multi-agent AI systems for scientific, engineering, and enterprise workflows.

Related projects and news

Enhancing Event-Sequence Embeddings for RAG Systems

M:3L is developing a foundational event-embedding model that jointly captures event meaning, temporal progression, and outcome/value signals. The work targets time-sensitive retrieval and agent memory across domains, supporting anomaly and churn detection, next-action and value prediction, causal and counterfactual analysis, and grounded conversational investigation through a Semantic Vector MCP. This work was conducted within the scope of the Faculty Research Funding Program 2025 of the Enterprise Incubator Foundation (EIF).