arXiv:2605.23043v1 Announce Type: new 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
The rapid advancement and deployment of large language models are exposing critical limitations in agentic system reliability and interpretability, making uncertainty propagation a pressing research area.
Improving the understanding and control of uncertainty in agentic LLM systems is crucial for their safe and effective deployment across sensitive applications, impacting trust and adoption.
This research introduces a novel framework for analyzing how uncertainty propagates through agentic text simulations, offering tools to design more robust and predictable AI systems.
- · AI developers
- · Enterprises adopting AI agents
- · Researchers in AI safety
- · Developers of simulation platforms
- · Companies relying on unreliable 'black box' AI
- · Applications where error propagation is critical
More reliable and trustworthy AI agent systems can be developed, expanding their applicability.
Increased adoption of AI agents across various industries due to enhanced predictability and error handling.
The complexity and autonomy of AI systems could escalate significantly, necessitating new regulatory and ethical frameworks tuned to 'semantic uncertainty'.
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