HawkesLLM: Semantic Uncertainty Propagation in Agentic Text Simulation

Background

The article discusses the challenges of generating coherent and contextually accurate sequences using agentic text-simulation systems, which are prone to path-dependent uncertainties due to early ambiguities affecting later outputs. To tackle this issue, the research introduces HawkesLLM, a framework that decouples temporal influence modeling from actual text generation.

Key Points

• Temporal Influence Modeling: HawkesLLM uses a multivariate Hawkes process to model how different text-generating agents activate over time and which earlier node outputs influence later prompts.
• Network Representation: The cascade of text generation is represented as a network where nodes are text-generating agents, allowing for the tracking of dependencies between them over time.
• Language Model Integration: A language model generates each new event based on compact memory selected by the temporal model, ensuring that only relevant past outputs influence current prompts.

Significance

• Path Dependence Mitigation: By separating temporal influences from text generation, HawkesLLM can mitigate path dependence and produce more coherent sequences. This is particularly useful in scenarios where early decisions significantly impact later outputs.
• Memory Efficiency: The framework operates with a compact prompt-memory budget, making it efficient for applications requiring real-time or resource-constrained environments.
• Evaluation Metrics: Diagnostics track semantic alignment with local held-out references and distinguish between local and global drifts, providing insights into the system's performance.

Key Insights

• The separation of temporal influence modeling from text generation allows for more precise control over sequence coherence.
• Using a multivariate Hawkes process effectively captures complex dependencies between agents, enhancing overall output quality.
• Evaluating with GDELT news data ensures real-world applicability and reliability of the framework.