HawkesLLM: Semantic Uncertainty Propagation in Agentic Text Simulation
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| Format: | Preprint |
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2026
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| _version_ | 1866913154363031552 |
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| author | Deng, Zewei Ye, Tinghan Xie, Liyan |
| author_facet | Deng, Zewei Ye, Tinghan Xie, Liyan |
| contents | 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. |
| format | Preprint |
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arxiv_https___arxiv_org_abs_2605_23043 |
| institution | arXiv |
| publishDate | 2026 |
| record_format | arxiv |
| spellingShingle | HawkesLLM: Semantic Uncertainty Propagation in Agentic Text Simulation Deng, Zewei Ye, Tinghan Xie, Liyan Computation and Language Machine Learning 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. |
| title | HawkesLLM: Semantic Uncertainty Propagation in Agentic Text Simulation |
| topic | Computation and Language Machine Learning |
| url | https://arxiv.org/abs/2605.23043 |