HawkesLLM: Semantic Uncertainty Propagation in Agentic Text Simulation
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Computer Science > Computation and Language
Title:HawkesLLM: Semantic Uncertainty Propagation in Agentic Text Simulation
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.
| Comments: | 10 pages, 4 figures, Accepted at the ICML 2026 Workshop on Statistical Frameworks for Uncertainty in Agentic Systems |
| Subjects: | Computation and Language (cs.CL); Machine Learning (stat.ML) |
| Cite as: | arXiv:2605.23043 [cs.CL] |
| (or arXiv:2605.23043v1 [cs.CL] for this version) | |
| https://doi.org/10.48550/arXiv.2605.23043
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