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SWORD framework optimizes user behavior simulation with autonomous prompt and workflow discovery

By PulseAugur Editorial · [1 sources] ·

A new framework called SWORD has been developed to improve user behavior simulation by jointly optimizing multi-agent workflow topology and natural language prompts. This approach, guided solely by a scalar task metric, autonomously discovers domain-relevant signals and priors without requiring domain initialization or task-specific engineering. SWORD demonstrates statistically significant improvements over existing baselines, achieving higher accuracy with less training data and lower API costs, while also establishing textual gradients as a mechanism for unsupervised feature importance discovery. AI

IMPACT This framework could significantly improve the efficiency and accuracy of user behavior modeling in various applications, reducing the need for manual engineering.

RANK_REASON The item is an academic paper detailing a new framework and its experimental results. [lever_c_demoted from research: ic=1 ai=1.0]

Read on arXiv cs.AI →

AI-generated summary · Google Gemini · from 1 sources. How we write summaries →

SWORD framework optimizes user behavior simulation with autonomous prompt and workflow discovery

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The item is an academic paper detailing a new framework and its experimental results. [lever_c_demoted from research: ic=1 ai=1.0]
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COVERAGE [1]

  1. arXiv cs.AI TIER_1 English(EN) · Nipun B Nair (Monash University), Tongtong Wu (Monash University), Hongzhi Yin (The University of Queensland), Hui Li (Xiamen University), Weiqing Wang (Monash University) ·

    Joint Workflow and Prompt Optimization for User Behavior Simulation

    arXiv:2610.07663v1 Announce Type: cross Abstract: User behavior simulation is the computational modeling of user interactions within information systems through the use of simulated agents in place of live users. It supports system testing and evaluation, decision-making and fore…

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