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HawkesLLM: Semantic Uncertainty Propagation in Agentic Text Simulation

2026-05-21 21:17:34
Zewei Deng, Tinghan Ye, Liyan Xie
arXiv_CL
         
Abstract

Agentic text-simulation systems write in sequence, with each item becoming possible context for later steps. That makes uncertainty path-dependent: an early ambiguity can affect later outputs. This paper studies this problem with HawkesLLM, a framework that separates temporal influence modeling from text generation. We represent the cascade as a network whose nodes are text-generating agents. A multivariate Hawkes process models how these nodes activate over time and which earlier node outputs should influence later prompts. A language model then writes each new event from the compact memory selected by this temporal model. We evaluate the framework on a held-out Global Database of Events, Language, and Tone (GDELT) news-cascade case study. The diagnostics track semantic alignment with local held-out references and separate local drift from global drift. In this setting, HawkesLLM improves late-stage semantic alignment under a compact prompt-memory budget.

Abstract (translated)

URL

https://arxiv.org/abs/2605.23043

PDF

https://arxiv.org/pdf/2605.23043.pdf

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