Can LLM Agents Sustain Long-Horizon Organizational Dynamics?

Guoshun Nan; Shuaibing Wan; Xiaohan Zhang; Xuancheng Zhu; Yang Yue; Yongrui Liu; Zihan Dou

arxiv: 2606.01199 · v1 · pith:6IJXZ5M7new · submitted 2026-05-31 · 💻 cs.AI

Can LLM Agents Sustain Long-Horizon Organizational Dynamics?

Xuancheng Zhu , Yang Yue , Shuaibing Wan , Zihan Dou , Xiaohan Zhang , Yongrui Liu , Guoshun Nan This is my paper

Pith reviewed 2026-06-28 17:19 UTC · model grok-4.3

classification 💻 cs.AI

keywords LLM agentsorganizational simulationmulti-agent systemshierarchical memorylong-horizon taskscoordination frameworks

0 comments

The pith

A memory cycle enables LLM agents to sustain coherent behavior in long-horizon organizational simulations.

A machine-rendered reading of the paper's core claim, the machinery that carries it, and where it could break.

Large language model agents struggle to maintain consistent actions when simulating structured organizations over extended periods because goals must flow through hierarchies and tasks depend on previous results. The paper presents this as a coordination problem centered on memory and introduces TaskWeave, a framework that cycles through formulating, partitioning, diagnosing, and aligning plans while using dependency-aware traces to ground execution. In tests within a simulated IT company spanning a full year, TaskWeave produced more coherent dynamics and better artifacts than other multi-agent approaches, suggesting memory structure is essential for reliable organizational simulators.

Core claim

TaskWeave supports coherent and long-horizon organizational dynamics in LLM agent simulations through a Formulate-Partition-Diagnose-Align cycle for planning states and dependency-aware trace memory for execution grounding, leading to improved organizational coherence, grounded artifacts, and adaptation in a year-long IT company simulation.

What carries the argument

The Formulate-Partition-Diagnose-Align cycle and dependency-aware trace memory, which together maintain planning states and ground execution in hierarchical coordination.

If this is right

Coherent organizational behavior emerges over long time horizons.
Execution produces grounded and usable artifacts.
The framework adapts to external environment changes.
Downstream enterprise NLP tasks gain utility from the simulation outputs.

Where Pith is reading between the lines

These are editorial extensions of the paper, not claims the author makes directly.

Memory structures like this could extend to simulations of other types of organizations.
Testing with simulations longer than one year could expose scalability issues.
Connecting the simulation to real external data sources might better validate adaptation.

Load-bearing premise

Success in a single year-long IT company simulation accurately measures the ability to sustain real-world organizational dynamics.

What would settle it

Observing a breakdown in coherence when TaskWeave is applied to a simulation exceeding one year or in a non-IT domain.

Figures

Figures reproduced from arXiv: 2606.01199 by Guoshun Nan, Shuaibing Wan, Xiaohan Zhang, Xuancheng Zhu, Yang Yue, Yongrui Liu, Zihan Dou.

**Figure 1.** Figure 1: Overview of organizational dynamics. High-level goals are progressively refined into interdependent [PITH_FULL_IMAGE:figures/full_fig_p002_1.png] view at source ↗

**Figure 2.** Figure 2: Overview of TaskWeave. Organizational metadata is compiled into a role-structured prior. A hierarchical [PITH_FULL_IMAGE:figures/full_fig_p004_2.png] view at source ↗

**Figure 3.** Figure 3: Role assignment distribution across different [PITH_FULL_IMAGE:figures/full_fig_p006_3.png] view at source ↗

**Figure 4.** Figure 4: Case study. TaskWeave produces adaptive and temporally dependent workflows with diverse structured [PITH_FULL_IMAGE:figures/full_fig_p007_4.png] view at source ↗

**Figure 5.** Figure 5: Impact of external incidents on task allocation. [PITH_FULL_IMAGE:figures/full_fig_p008_5.png] view at source ↗

**Figure 6.** Figure 6: Generated tasks from TaskWeave using ChatGPT-4o-mini. [PITH_FULL_IMAGE:figures/full_fig_p015_6.png] view at source ↗

**Figure 7.** Figure 7: ITHC outputs of six models [PITH_FULL_IMAGE:figures/full_fig_p018_7.png] view at source ↗

**Figure 9.** Figure 9: Monthly completion time distribution. Here, C ′ t excludes critical context such as status summaries or key execution lists. This substantial drop confirms the necessity of comprehensive context and validates the system’s sensitivity to semantically grounded documentation. H Organizational Sensitive Span Detection Using LLMs to annotate or verify LLM outputs has proven effective in prior work on instructi… view at source ↗

**Figure 10.** Figure 10: Label distribution in OSSD. and finally checks logical consistency. By embedding explanatory rationales and enabling correction, OSSD supports transparency and interpretability in enterprise-level privacy annotation. I Generalizability I.1 Financial Company We evaluate TaskWeave by simulating the monthlong operation of a representative financial company, denoted as Fin. The organization is composed of… view at source ↗

read the original abstract

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.

Editorial analysis

A structured set of objections, weighed in public.

Desk editor's note, referee report, simulated authors' rebuttal, and a circularity audit. Tearing a paper down is the easy half of reading it; the pith above is the substance, this is the friction.

Desk Editor's Note private letter to a colleague

TaskWeave adds a Formulate-Partition-Diagnose-Align cycle plus dependency-aware trace memory to multi-agent LLMs, but the year-long IT simulation lacks ablations or metrics so the gains are hard to trust.

read the letter

TaskWeave is the main new piece. It treats long-horizon organizational simulation as a memory-centered coordination problem and layers a Formulate-Partition-Diagnose-Align cycle on hierarchical agents along with dependency-aware trace memory to track task dependencies and accumulated artifacts. The evaluation runs a year-long IT company simulation and compares against other multi-agent frameworks on coherence, grounding, and downstream NLP utility.

The approach makes sense for the stated problem. Most multi-agent LLM work stays short or uses loose shared state; making memory explicit and tying it to a planning cycle is a direct engineering response to hierarchy and accumulation. The year-long scale is also more ambitious than typical short-horizon tests.

The soft spot is the evaluation. The abstract reports positive results but supplies no numbers, baseline descriptions, statistical tests, or ablation data. We do not know how the simulation generated external events or task dependencies, whether the environment was adjusted after seeing baseline failures, or whether the scoring favors exactly the structures the trace memory maintains. The stress-test note on simulation design choices holds up on the available text. Without those controls, the performance gap does not yet demonstrate general long-horizon capability.

This is for researchers working on multi-agent LLM simulators for enterprise or management settings. A reader already building coordination mechanisms would pick up the cycle and memory design as concrete options. The paper shows clear thinking on the coordination problem and honest engagement with the limits of current agents, so it deserves a serious referee even if the experiments need expansion. I would bring it to a reading group to discuss the memory mechanism but would not cite it until the full results and controls are visible.

Referee Report

3 major / 2 minor

Summary. The paper formulates long-horizon organizational simulation as a memory-centered coordination problem and introduces TaskWeave, a hierarchical agentic framework that uses a Formulate-Partition-Diagnose-Align cycle together with dependency-aware trace memory. It evaluates the framework in a year-long IT company simulation, reporting that TaskWeave achieves superior organizational coherence, execution grounding, and downstream enterprise NLP utility relative to other multi-agent frameworks, and concludes that structured simulation memory is key for reliable LLM-based organizational simulators.

Significance. If the experimental results prove robust and the simulation is shown not to embed design biases favoring the proposed mechanisms, the work would be significant for multi-agent LLM research by supplying a concrete, memory-centric architecture and an extended simulation testbed that could serve as a benchmark for long-horizon coordination.

major comments (3)

[Abstract / Experiments] Abstract and Experiments section: The abstract asserts positive comparative results on organizational coherence, execution grounding, and downstream NLP utility, yet supplies no quantitative metrics, baseline implementations, statistical tests, or controls. This absence is load-bearing for the central claim that TaskWeave sustains long-horizon dynamics.
[Experiments] Experiments section: No information is given on how external events, task dependencies, or artifact accumulation were generated in the year-long IT simulation, nor whether the environment or rubrics were tuned after observing baseline failures. Without such details or an ablation isolating the Formulate-Partition-Diagnose-Align cycle and trace memory from generic prompting or shared state, it is impossible to rule out that observed gains arise from simulation construction rather than the proposed mechanisms.
[Experiments] Experiments section: The paper reports no ablation studies that remove or replace the dependency-aware trace memory while keeping other components fixed, leaving the contribution of the memory mechanism unisolated.

minor comments (2)

[Abstract] The phrase 'grounded artifacts' is used repeatedly but never given an operational definition or measurement procedure.
[Experiments] The downstream enterprise NLP utility task is mentioned but its construction, data, and evaluation protocol are not described.

Simulated Author's Rebuttal

3 responses · 0 unresolved

We thank the referee for the detailed and constructive comments regarding experimental transparency and controls. We address each major point below and will make targeted revisions to strengthen the manuscript.

read point-by-point responses

Referee: [Abstract / Experiments] Abstract and Experiments section: The abstract asserts positive comparative results on organizational coherence, execution grounding, and downstream NLP utility, yet supplies no quantitative metrics, baseline implementations, statistical tests, or controls. This absence is load-bearing for the central claim that TaskWeave sustains long-horizon dynamics.

Authors: The experiments section contains quantitative comparisons against other multi-agent frameworks, including tables reporting coherence, grounding, and utility scores along with baseline implementations. The abstract, however, is written at a summary level without specific numbers. We will revise the abstract to include representative quantitative metrics and note the presence of statistical comparisons in the main text. revision: partial
Referee: [Experiments] Experiments section: No information is given on how external events, task dependencies, or artifact accumulation were generated in the year-long IT simulation, nor whether the environment or rubrics were tuned after observing baseline failures. Without such details or an ablation isolating the Formulate-Partition-Diagnose-Align cycle and trace memory from generic prompting or shared state, it is impossible to rule out that observed gains arise from simulation construction rather than the proposed mechanisms.

Authors: We will add a new subsection in Experiments that fully specifies the stochastic generation process for external events, the dependency graph construction for tasks, and the artifact logging mechanism. We will also explicitly state that the simulation parameters and rubrics were fixed before any agent runs and were not adjusted after observing baseline performance. In addition, we will include an ablation comparing the full Formulate-Partition-Diagnose-Align cycle against a generic prompting baseline with shared state only. revision: yes
Referee: [Experiments] Experiments section: The paper reports no ablation studies that remove or replace the dependency-aware trace memory while keeping other components fixed, leaving the contribution of the memory mechanism unisolated.

Authors: We agree that an explicit ablation isolating the dependency-aware trace memory is necessary. We will add results from a controlled ablation in which the trace memory is replaced by a standard shared memory buffer (while retaining the planning cycle and all other components) and report the resulting impact on long-horizon coherence metrics. revision: yes

Circularity Check

0 steps flagged

No circularity: empirical evaluation is external to framework definition

full rationale

The paper formulates organizational simulation as a memory-centered coordination problem and introduces TaskWeave with its Formulate-Partition-Diagnose-Align cycle plus trace memory. It then reports results from a year-long IT company simulation on external metrics (coherence, grounding, NLP utility). No equations, fitted parameters, or 'predictions' are described that reduce by construction to the framework's own definitions or inputs. No self-citation chains, uniqueness theorems, or ansatzes are invoked as load-bearing. The simulation is presented as an independent test environment; any concerns about its construction favoring the method fall under validity rather than circular derivation. The derivation chain is therefore self-contained against external benchmarks.

Axiom & Free-Parameter Ledger

0 free parameters · 1 axioms · 1 invented entities

Abstract-only review; ledger entries are inferred at high level from the stated problem formulation and framework description. Full paper would likely reveal additional fitted parameters in the simulation or cycle implementation.

axioms (1)

domain assumption LLM agents can sustain coherent organizational behavior when equipped with explicit hierarchical planning cycles and dependency-aware memory
This is the load-bearing premise that the TaskWeave design and evaluation rest on.

invented entities (1)

TaskWeave framework no independent evidence
purpose: Maintain planning states through Formulate-Partition-Diagnose-Align cycle and ground execution via dependency-aware trace memory
Newly introduced agentic system for long-horizon coordination.

pith-pipeline@v0.9.1-grok · 5687 in / 1309 out tokens · 27037 ms · 2026-06-28T17:19:27.105343+00:00 · methodology

discussion (0)

Reference graph

Works this paper leans on

22 extracted references · 2 canonical work pages · 1 internal anchor

[1]

Autogen: Enabling next-gen llm applica- tions via multi-agent conversation.arXiv preprint arXiv:2308.08155. Tianbao Xie, Danyang Zhang, Jixuan Chen, Xiaochuan Li, Siheng Zhao, Ruisheng Cao, Toh Jing Hua, Zhou- jun Cheng, Dongchan Shin, Fangyu Lei, Yitao Liu, Yiheng Xu, Shuyan Zhou, Silvio Savarese, Caiming Xiong, Victor Zhong, and Tao Yu. 2024. OSWorld: B...

work page internal anchor Pith review Pith/arXiv arXiv 2024
[2]

arXiv preprint arXiv:2410.11782 , year=

Graph-of-agents: A graph-based framework for multi-agent LLM collaboration. InThe F our- teenth International Conference on Learning Repre- sentations. Guibin Zhang, Yanwei Yue, Xiangguo Sun, Guancheng Wan, Miao Yu, Junfeng Fang, Kun Wang, Tianlong Chen, and Dawei Cheng. 2025. G-designer: Archi- tecting multi-agent communication topologies via graph neura...

work page arXiv 2025
[3]

You will receive a document from enterprise operations
[4]

Analyze the document and classify it using the hierarchical classification system
[5]

Assign the most specific applicable label(s) to the document
[6]

If a document does not fit an existing label, propose a new label under the appropriate category
[7]

Output the classification result in LIST format
[8]

Avoid adding extra commentary or explanation outside the final classification output
[9]

Strictly output in the expected format
[10]

Category > Subcategory > Label

Multiple labels are allowed when necessary. EXPECTED OUTPUT: ("Category > Subcategory > Label", "reason") Where "reason" is a short explanation of why the document fits this label. DOCUMENT TO BE CLASSIFIED: {file_content} ITHC REVIEW PROMPT AGENT ROLE: You are a Classification Review Agent at PriGen. Your responsibility is to review and validate existing...
[11]

You will receive a document and its initial classification result
[12]

Review whether each label is broadly reflective of the document content
[13]

If a label is clearly wrong, misleading, or unrelated, replace it with a more suitable one
[14]

If a label is generally acceptable, even if not perfect, retain it
[15]

Be conservative in making changes and minimize revisions unless strongly justified
[16]

Maintain the existing label hierarchy and structure
[17]

Output the revised classification result in LIST format
[18]

Category > Subcategory > Label

Do not include extra comments outside the required format. EXPECTED OUTPUT: ("Category > Subcategory > Label", "reason") DOCUMENT TO BE REVIEWED: {file_content} ORIGINAL LABELS: {original_labels} In the first stage, GPT-4o-mini assigns a top- level category based solely on the original model output. In the second stage, it self-reviews the same input with...
[19]

Understand the task purpose
[20]

Review the summaries and output files
[21]

The goal was initiated or achieved

Consider a task completed if: a. The goal was initiated or achieved. b. Indirect outcomes fulfill the task's intent. c. Partial work clearly contributes to the task
[22]

Reasoning..., yes

Mark the task uncompleted if there is no meaningful evidence of progress. OUTPUT FORMAT: Return exactly one of the following: "Reasoning..., yes" "Reasoning..., no" TASK: {task} CONTEXT: {context} G.4 Sliding-Window Lifecycle Tracking If a task remains uncompleted, it is carried into the next week’s evaluation pool, provided that it still falls within the...

2023

[1] [1]

Autogen: Enabling next-gen llm applica- tions via multi-agent conversation.arXiv preprint arXiv:2308.08155. Tianbao Xie, Danyang Zhang, Jixuan Chen, Xiaochuan Li, Siheng Zhao, Ruisheng Cao, Toh Jing Hua, Zhou- jun Cheng, Dongchan Shin, Fangyu Lei, Yitao Liu, Yiheng Xu, Shuyan Zhou, Silvio Savarese, Caiming Xiong, Victor Zhong, and Tao Yu. 2024. OSWorld: B...

work page internal anchor Pith review Pith/arXiv arXiv 2024

[2] [2]

arXiv preprint arXiv:2410.11782 , year=

Graph-of-agents: A graph-based framework for multi-agent LLM collaboration. InThe F our- teenth International Conference on Learning Repre- sentations. Guibin Zhang, Yanwei Yue, Xiangguo Sun, Guancheng Wan, Miao Yu, Junfeng Fang, Kun Wang, Tianlong Chen, and Dawei Cheng. 2025. G-designer: Archi- tecting multi-agent communication topologies via graph neura...

work page arXiv 2025

[3] [3]

You will receive a document from enterprise operations

[4] [4]

Analyze the document and classify it using the hierarchical classification system

[5] [5]

Assign the most specific applicable label(s) to the document

[6] [6]

If a document does not fit an existing label, propose a new label under the appropriate category

[7] [7]

Output the classification result in LIST format

[8] [8]

Avoid adding extra commentary or explanation outside the final classification output

[9] [9]

Strictly output in the expected format

[10] [10]

Category > Subcategory > Label

Multiple labels are allowed when necessary. EXPECTED OUTPUT: ("Category > Subcategory > Label", "reason") Where "reason" is a short explanation of why the document fits this label. DOCUMENT TO BE CLASSIFIED: {file_content} ITHC REVIEW PROMPT AGENT ROLE: You are a Classification Review Agent at PriGen. Your responsibility is to review and validate existing...

[11] [11]

You will receive a document and its initial classification result

[12] [12]

Review whether each label is broadly reflective of the document content

[13] [13]

If a label is clearly wrong, misleading, or unrelated, replace it with a more suitable one

[14] [14]

If a label is generally acceptable, even if not perfect, retain it

[15] [15]

Be conservative in making changes and minimize revisions unless strongly justified

[16] [16]

Maintain the existing label hierarchy and structure

[17] [17]

Output the revised classification result in LIST format

[18] [18]

Category > Subcategory > Label

Do not include extra comments outside the required format. EXPECTED OUTPUT: ("Category > Subcategory > Label", "reason") DOCUMENT TO BE REVIEWED: {file_content} ORIGINAL LABELS: {original_labels} In the first stage, GPT-4o-mini assigns a top- level category based solely on the original model output. In the second stage, it self-reviews the same input with...

[19] [19]

Understand the task purpose

[20] [20]

Review the summaries and output files

[21] [21]

The goal was initiated or achieved

Consider a task completed if: a. The goal was initiated or achieved. b. Indirect outcomes fulfill the task's intent. c. Partial work clearly contributes to the task

[22] [22]

Reasoning..., yes

Mark the task uncompleted if there is no meaningful evidence of progress. OUTPUT FORMAT: Return exactly one of the following: "Reasoning..., yes" "Reasoning..., no" TASK: {task} CONTEXT: {context} G.4 Sliding-Window Lifecycle Tracking If a task remains uncompleted, it is carried into the next week’s evaluation pool, provided that it still falls within the...

2023