Saved in:
| Main Authors: | , , , , , , |
|---|---|
| Format: | Preprint |
| Published: |
2026
|
| Subjects: | |
| Online Access: | https://arxiv.org/abs/2606.01199 |
| Tags: |
Add Tag
No Tags, Be the first to tag this record!
|
| _version_ | 1866914620911910912 |
|---|---|
| author | Zhu, Xuancheng Yue, Yang Wan, Shuaibing Dou, Zihan Zhang, Xiaohan Liu, Yongrui Nan, Guoshun |
| author_facet | Zhu, Xuancheng Yue, Yang Wan, Shuaibing Dou, Zihan Zhang, Xiaohan Liu, Yongrui Nan, Guoshun |
| contents | Large language agents are increasingly used for social simulation, yet it remains unclear whether they can sustain coherent behavior in structured organizations, where goals must propagate through hierarchy, tasks depend on prior execution, and artifacts accumulate over long horizons. We formulate long-horizon organizational simulation as a memory-centered coordination problem and introduce TaskWeave, a hierarchical agentic framework that maintains planning states through a Formulate-Partition-Diagnose-Align cycle and grounds execution through dependency-aware trace memory. We evaluate TaskWeave in a year-long IT company simulation and compare it with other multi-agent frameworks on organizational coherence, execution grounding, and downstream enterprise NLP utility. Experiments show that TaskWeave supports coherent and long-horizon organizational dynamics while producing grounded artifacts and adapting to external environments. These findings suggest that structured simulation memory is a key mechanism for building reliable LLM-based organizational simulators. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2606_01199 |
| institution | arXiv |
| publishDate | 2026 |
| record_format | arxiv |
| spellingShingle | Can LLM Agents Sustain Long-Horizon Organizational Dynamics? Zhu, Xuancheng Yue, Yang Wan, Shuaibing Dou, Zihan Zhang, Xiaohan Liu, Yongrui Nan, Guoshun Artificial Intelligence Large language agents are increasingly used for social simulation, yet it remains unclear whether they can sustain coherent behavior in structured organizations, where goals must propagate through hierarchy, tasks depend on prior execution, and artifacts accumulate over long horizons. We formulate long-horizon organizational simulation as a memory-centered coordination problem and introduce TaskWeave, a hierarchical agentic framework that maintains planning states through a Formulate-Partition-Diagnose-Align cycle and grounds execution through dependency-aware trace memory. We evaluate TaskWeave in a year-long IT company simulation and compare it with other multi-agent frameworks on organizational coherence, execution grounding, and downstream enterprise NLP utility. Experiments show that TaskWeave supports coherent and long-horizon organizational dynamics while producing grounded artifacts and adapting to external environments. These findings suggest that structured simulation memory is a key mechanism for building reliable LLM-based organizational simulators. |
| title | Can LLM Agents Sustain Long-Horizon Organizational Dynamics? |
| topic | Artificial Intelligence |
| url | https://arxiv.org/abs/2606.01199 |