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arxiv: 2604.20651 · v1 · submitted 2026-04-22 · 💻 cs.AI

Recognition: unknown

CHORUS: An Agentic Framework for Generating Realistic Deliberation Data

A. Koursaris , G. Domalis , A. Apostolopoulou , K. Kanaris , D. Tsakalidis , I. E. Livieris
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Pith reviewed 2026-05-09 23:53 UTC · model grok-4.3

classification 💻 cs.AI
keywords deliberation dataagentic frameworkLLM actorssynthetic discussionsPoisson processonline discoursedata generation
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The pith

Chorus generates realistic deliberation data by directing LLM actors with consistent personas, shared memory, and Poisson-timed turns.

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

The paper addresses the scarcity of accessible, high-quality data on online discussions by building a framework that produces synthetic deliberations. It equips language-model actors with fixed behavioral personas and a running memory of the conversation, then uses a Poisson process to decide when each actor speaks. The resulting discussions were run on the Deliberate platform and rated by thirty experts on realism, logical flow, and usefulness for analysis, showing that the generated material can stand in for real human exchanges in discourse studies.

Core claim

Chorus shows that autonomous LLM agents, each carrying a stable persona and an accumulating memory of prior turns, can produce coherent, realistic deliberation threads when their participation times are drawn from a Poisson process. When deployed on an interactive platform and scored by domain experts, the output scores high on content realism, discussion coherence, and analytical utility, establishing the framework as a practical source of large-scale deliberation data.

What carries the argument

The Chorus agentic framework, which coordinates LLM actors through behaviorally consistent personas, discussion memory, structured tool access, and a Poisson-process model for turn timing.

If this is right

  • Researchers gain an on-demand supply of deliberation data without facing platform access restrictions or privacy barriers.
  • The same setup can be adapted to new topics simply by changing the personas and external tools available to the agents.
  • Generated threads can serve as training or test material for models that detect polarization, misinformation, or constructive discourse.
  • Platform operators can use the framework to simulate discussion dynamics before deploying new moderation rules.

Where Pith is reading between the lines

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

  • Synthetic deliberation data could let researchers run controlled experiments on how different platform designs affect conversation quality.
  • If the generated data proves statistically similar to real data on measurable features such as reply depth or sentiment trajectories, it could reduce reliance on scraped social-media corpora.
  • The approach opens a route to testing hypotheses about online behavior by varying only the personas or timing parameters while holding everything else fixed.

Load-bearing premise

That language-model actors given stable personas and conversation memory, with turns timed by a Poisson process, will produce exchanges close enough to real human deliberation to be useful for analysis.

What would settle it

A direct side-by-side comparison in which experts or quantitative measures find that Chorus-generated threads differ markedly from matched real human threads in coherence, topic drift, or engagement patterns.

Figures

Figures reproduced from arXiv: 2604.20651 by A. Apostolopoulou, A. Koursaris, D. Tsakalidis, G. Domalis, I. E. Livieris, K. Kanaris.

Figure 1
Figure 1. Figure 1: Simulated activity over T = 20 minutes: (a) posts and actions per minute across all actors, (b) aggregate post and action counts per actor and (c) new comments versus replies per actor. 7 [PITH_FULL_IMAGE:figures/full_fig_p007_1.png] view at source ↗
read the original abstract

Understanding the intricate dynamics of online discourse depends on large-scale deliberation data, a resource that remains scarce across interactive web platforms due to restrictive accessibility policies, ethical concerns and inconsistent data quality. In this paper, we propose Chorus, an agentic framework, which orchestrates LLM-powered actors with behaviorally consistent personas to generate realistic deliberation discussions. Each actor is governed by an autonomous agent equipped with memory of the evolving discussion, while participation timing is governed by a principled Poisson process-based temporal model, which approximates the heterogeneous engagement patterns of real users. The framework is further supported by structured tool usage, enabling actors to access external resources and facilitating integration with interactive web platforms. The framework was deployed on the \textsc{Deliberate} platform and evaluated by 30 expert participants across three dimensions: content realism, discussion coherence and analytical utility, confirming Chorus as a practical tool for generating high-quality deliberation data suitable for online discourse analysis

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.

Referee Report

3 major / 2 minor

Summary. The paper proposes CHORUS, an agentic framework that orchestrates LLM-powered actors equipped with behaviorally consistent personas, discussion memory, and a Poisson-process temporal model to generate realistic online deliberation data. The framework includes structured tool usage for external resources and platform integration; it was deployed on the Deliberate platform and evaluated by 30 expert participants on content realism, discussion coherence, and analytical utility, with the conclusion that it provides a practical tool for high-quality deliberation data suitable for online discourse analysis.

Significance. If the generated data can be shown to match real human deliberation dynamics at a level sufficient for downstream analysis, the framework would address a genuine scarcity of accessible, large-scale deliberation datasets while avoiding ethical and policy barriers. The combination of persona consistency, memory, and principled timing offers a scalable synthetic-data approach that could support research in discourse analysis, platform design, and AI-mediated interaction.

major comments (3)
  1. [Abstract] Abstract: The central claim that Chorus produces data 'suitable for online discourse analysis' rests on 30 expert ratings across three subjective dimensions, yet no quantitative comparison to real human deliberation data is reported (e.g., no Kolmogorov-Smirnov or similar tests on inter-participation times generated by the Poisson model, no metrics on reply-tree depth distributions, sentiment trajectories, or coherence with empirical corpora). Without such validation, the expert ratings alone do not establish that the generated dynamics are analytically useful rather than merely plausible.
  2. [Temporal model] Temporal model description: The Poisson-process timing is presented as approximating heterogeneous user engagement, but the manuscript supplies no procedure for setting or validating the rate parameters against real participation statistics, nor any sensitivity analysis showing that downstream discussion properties remain stable under reasonable parameter choices. This leaves the 'principled' character of the temporal component unverified.
  3. [Evaluation] Evaluation methodology: The expert study provides no inter-rater agreement statistics, no baseline comparisons against simpler LLM prompting or existing synthetic-dialogue generators, and no operational definition of 'realism' or 'analytical utility' that would allow replication or falsification. These omissions make it impossible to assess whether the positive ratings reflect genuine fidelity or merely surface-level coherence.
minor comments (2)
  1. [Abstract] The abstract and deployment description refer to 'structured tool usage' without enumerating the specific tools, their interfaces, or how they affect discussion realism; a brief enumeration or pseudocode would clarify the contribution.
  2. [Method] Notation for the Poisson process (rate parameters, memory state) is introduced without an explicit equation or pseudocode block, making the temporal model harder to reproduce from the text alone.

Simulated Author's Rebuttal

3 responses · 1 unresolved

We thank the referee for the constructive and detailed comments, which highlight important areas for strengthening the validation of CHORUS. We address each major comment below and commit to revisions that improve the manuscript's rigor while remaining honest about the constraints of synthetic data generation in this domain.

read point-by-point responses

  1. Referee: [Abstract] Abstract: The central claim that Chorus produces data 'suitable for online discourse analysis' rests on 30 expert ratings across three subjective dimensions, yet no quantitative comparison to real human deliberation data is reported (e.g., no Kolmogorov-Smirnov or similar tests on inter-participation times generated by the Poisson model, no metrics on reply-tree depth distributions, sentiment trajectories, or coherence with empirical corpora). Without such validation, the expert ratings alone do not establish that the generated dynamics are analytically useful rather than merely plausible.

    Authors: We agree that the current validation relies on expert judgment rather than direct statistical matching to real corpora, which limits the strength of the suitability claim. As explained in the introduction, large-scale real deliberation data remains inaccessible due to platform policies and ethical restrictions, precluding direct quantitative benchmarks such as KS tests on timing or reply structures. In revision we will temper the abstract to state that the data is 'promising for' rather than 'suitable for' online discourse analysis, add a limitations subsection explicitly noting the absence of such comparisons, and report descriptive statistics of the generated discussions (e.g., inter-participation time distributions and reply-tree depths) to allow readers to assess plausibility. revision: partial

  2. Referee: [Temporal model] Temporal model description: The Poisson-process timing is presented as approximating heterogeneous user engagement, but the manuscript supplies no procedure for setting or validating the rate parameters against real participation statistics, nor any sensitivity analysis showing that downstream discussion properties remain stable under reasonable parameter choices. This leaves the 'principled' character of the temporal component unverified.

    Authors: The Poisson model is motivated by prior empirical studies of online engagement heterogeneity, but we acknowledge the manuscript lacks explicit calibration details and sensitivity checks. We will revise the temporal model section to specify how rate parameters are derived (e.g., from average per-user posting frequencies reported in the deliberation literature) and add a sensitivity analysis showing that key discussion properties (coherence, participation balance) remain stable across plausible rate ranges. These additions will be included in the revised manuscript. revision: yes

  3. Referee: [Evaluation] Evaluation methodology: The expert study provides no inter-rater agreement statistics, no baseline comparisons against simpler LLM prompting or existing synthetic-dialogue generators, and no operational definition of 'realism' or 'analytical utility' that would allow replication or falsification. These omissions make it impossible to assess whether the positive ratings reflect genuine fidelity or merely surface-level coherence.

    Authors: We will expand the evaluation section to report inter-rater agreement (Fleiss' kappa) for the 30 experts. We will also add baseline comparisons against non-agentic LLM prompting (simple chain-of-thought without memory or Poisson timing) and clarify operational definitions of the three dimensions by providing the exact rating rubrics and example excerpts used in the study. These changes will improve replicability and allow direct assessment of the framework's incremental value. revision: yes

standing simulated objections not resolved
  • Direct statistical comparisons (e.g., Kolmogorov-Smirnov tests, reply-tree depth distributions, sentiment trajectories) against real human deliberation corpora, because such datasets are not publicly available owing to platform access restrictions and ethical constraints.

Circularity Check

0 steps flagged

No significant circularity; framework and expert evaluation are self-contained with no derivations or self-referential reductions.

full rationale

The paper describes an agentic framework (Chorus) that uses LLM actors with personas, discussion memory, structured tools, and a Poisson-process temporal model to generate deliberation data, then deploys it on the Deliberate platform for evaluation by 30 experts on content realism, coherence, and analytical utility. No equations, parameter fittings, or derivations are present in the provided text. The central claim rests on this external human evaluation rather than any internal reduction to inputs by construction. No self-citations, uniqueness theorems, or ansatzes are invoked in a load-bearing way. The derivation chain is therefore independent and non-circular.

Axiom & Free-Parameter Ledger

1 free parameters · 2 axioms · 0 invented entities

The framework rests on the untested premise that current LLMs can sustain consistent personas and memory-driven behavior that approximates human deliberation patterns. The Poisson timing model is presented as principled but its parameters and validation against real user data are not detailed in the abstract.

free parameters (1)
  • Poisson process rate parameters
    Used to govern participation timing; values not specified in abstract but required for the temporal model.
axioms (2)
  • domain assumption LLM actors can maintain behaviorally consistent personas across an evolving discussion
    Invoked as the basis for realistic actor behavior.
  • domain assumption A Poisson process adequately approximates heterogeneous real-user engagement patterns
    Stated as the temporal model without further justification in the abstract.

pith-pipeline@v0.9.0 · 5480 in / 1405 out tokens · 39828 ms · 2026-05-09T23:53:31.323895+00:00 · methodology

discussion (0)

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Reference graph

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