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HawkesLLM framework tackles semantic uncertainty in agentic text simulations

By PulseAugur Editorial · [3 sources] ·

Researchers have developed HawkesLLM, a new framework designed to manage semantic uncertainty in agentic text-simulation systems. This framework separates temporal influence modeling from text generation, representing the cascade of agent interactions as a network. A multivariate Hawkes process is used to model how agents activate over time and which previous outputs should influence future prompts, with a language model then generating new events based on this temporal selection. AI

IMPACT Introduces a novel method for improving semantic alignment in sequential text generation by managing uncertainty.

RANK_REASON The cluster contains an academic paper detailing a new framework for agentic text simulation.

Read on arXiv cs.AI →

AI-generated summary · Google Gemini · from 3 sources. How we write summaries →

COVERAGE [3]

  1. arXiv cs.AI TIER_1 English(EN) · Suresh Raghu, Satwik Pandey, Shashwat Pandey ·

    Proper Scoring Rules for Agentic Uncertainty Quantification

    arXiv:2605.24756v1 Announce Type: new Abstract: Language-model agents increasingly emit uncertainty signals throughout a trajectory, but existing agentic UQ evaluations often conflate ranking usefulness with probabilistic truthfulness. AUROC, AUPRC, risk-coverage, Trajectory ECE,…

  2. arXiv stat.ML TIER_1 English(EN) · Zewei Deng, Tinghan Ye, Liyan Xie ·

    HawkesLLM: Semantic Uncertainty Propagation in Agentic Text Simulation

    arXiv:2605.23043v1 Announce Type: cross 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 …

  3. arXiv stat.ML TIER_1 English(EN) · Liyan Xie ·

    HawkesLLM: Semantic Uncertainty Propagation in Agentic Text Simulation

    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 inf…

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