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REVIEW 3 major objections 5 minor 28 cited by

OASIS: Open Agent Social Interaction Simulations with One Million Agents

T0 review · 3 major / 5 minor · reviewed 2026-08-12 · deepseek-v4-flash

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Pith's one-line read This paper claims OASIS, a modular LLM-agent simulator, scales to one million users and reproduces information spreading, group polarization, and herd effects on X and Reddit, while larger populations yield more diverse and helpful…

desk verdict A genuinely useful open-source simulator for million-agent social media studies, but the headline scale findings are confounded and need control conditions before they can be stated as results. read the letter →

arxiv 2411.11581 v5 pith:JIJZO3BI submitted 2024-11-18 cs.CL

Ziyi Yang , Zaibin Zhang , Zirui Zheng , Yuxian Jiang , Ziyue Gan , Zhiyu Wang , Zijian Ling , Jinsong Chen
show 15 more authors
Martz Ma Bowen Dong Prateek Gupta Shuyue Hu Zhenfei Yin Guohao Li Xu Jia Lijun Wang Bernard Ghanem Huchuan Lu Chaochao Lu Wanli Ouyang Yu Qiao Philip Torr Jing Shao
This is my paper · ORCID
classification cs.CL
keywords agent-basedsimulationlargelanguagemodelssocialmediainformationpropagationgrouppolarizationherdeffectmisinformationscalability
verification ladder T0 review T1 audit T2 compute T3 formal

The pith

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

The reading

OASIS is a modular simulator for social media platforms that replaces rule-based agents with large language models and scales to a million users. The paper's central claim is that this simulator is general enough to reproduce established social phenomena—information spreading and group polarization on X, and herd effects on Reddit—while being scalable enough to support large-population experiments. It further claims that the number of agents qualitatively changes the simulation: with more agents, the opinions reaching a fixed set of core users become more diverse and more helpful, and herd responses to disliked content become stronger. If these claims hold, OASIS offers social scientists a controllable testbed for questions that are impractical or unethical to run on real platforms.

What carries the argument

The load-bearing components are the five modules of OASIS. The Environment Server stores users, posts, comments, and relations in a database. The RecSys controls what each agent sees: on X it ranks in-network posts by likes and out-of-network posts by recency, follower impact, and TwHIN-BERT cosine similarity of interests; on Reddit it ranks by the platform's hot-score formula. The Agent Module gives each LLM-backed user a memory and 21 action types, with chain-of-thought reasoning. The Time Engine activates each agent according to a 24-dimensional hourly activity profile, and the Scalable Inferencer distributes inference requests across GPUs asynchronously. A large-scale user-generation algorithm builds networks that preserve the scale-free structure of real social graphs. Together these parts make the simulator platform-adaptable and population-scalable.

What would settle it

Run OASIS with a fixed agent count of 1,000 while sweeping the number of posts injected per time step (30, 300, 3000) and the recommendation cache size (50, 500, 5000). If the diversity and helpfulness of the 196 core users' opinions rise with content volume alone, the paper's 'larger group' conclusion is called into question. Alternatively, fix content volume and cache size while sweeping agent count from 196 to 10,196; if helpfulness and diversity do not rise, the population-size claim fails.

Watch

Extended reading notes

Core claim

On its own terms, the paper demonstrates that a single architecture with five components—an environment server, platform-specific recommendation systems, an LLM-based agent module, a time engine, and a scalable inference layer—can simulate up to one million agents across X and Reddit. Using real-world data from Twitter15/16, Reddit, and counterfactual posts, OASIS reproduces real propagation trends with a normalized RMSE around 30%, produces increasing group polarization over 80 time steps (more strongly with uncensored models), and replicates the human herd effect, while finding that agents are more conformist than humans on down-treated content. In scale experiments, the same 196 core users receive more diverse and more helpful opinions when the surrounding population grows from 196 to 10,196 and then to 100,196 agents, and herd effects on counterfactual posts appear only above a certain group size.

Load-bearing premise

The claim that a larger agent population improves opinion diversity and helpfulness assumes that the concurrent increases in the number of posts injected per time step and the size of the recommendation cache are not the actual causes of those improvements.

Editorial extensions

If this is right

  • Social-science findings replicated in small groups could be re-tested at population scale, where the paper claims emergent behavior appears only with enough agents.
  • Researchers can swap modules to compare platform designs, since OASIS supports both interest-based (X) and hot-score-based (Reddit) recommendation systems.
  • The scale effect on helpfulness suggests that simulations with hundreds of agents may underestimate the quality and variety of opinions a core user receives.
  • The million-agent misinformation experiment shows a tractable path for studying how false content spreads relative to official news, with misinformation generating more related posts.

Reading between the lines

Editorial extensions of the paper, not claims the author makes directly.

  • The paper's scale-ablation design increases post-injection volume and recommendation cache size together with agent count, so the reported gains in diversity and helpfulness may be driven by more content or broader exposure rather than by population size per se; an editorially proposed control experiment would hold content volume fixed while varying only the number of agents.
  • The finding that agents herd on dislikes but not likes, unlike humans, could reflect the LLM's prior about negative signals, and would be worth testing across different base models to see whether the asymmetry is a property of the simulator or of the particular model.
  • OASIS's architecture lends itself to studying interventions—modifying the recommendation algorithm or content visibility—in a way that would be hard to test on real platforms, a direction the paper notes as untapped potential.
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Editorial analysis

A structured set of objections, weighed in public.

Desk editor's note, referee report, and a circularity audit.

Referee Report

3 major / 5 minor

Summary. The paper introduces OASIS, an LLM-agent-based social media simulator with an environment server, recommendation systems, a 21-action agent module, a time engine, and a scalable inference backend. The authors claim generalizability across X and Reddit, demonstrate simulations with up to one million agents, and report replications of information spreading, group polarization, and herd effects. They also report two scale findings: that larger agent populations yield more helpful and diverse opinions (Finding 4) and that larger groups amplify dislike-driven herd effects (Finding 5).

Significance. If the claims hold, OASIS is a valuable open-source platform for studying social-media dynamics at a scale that prior LLM-agent simulators have not reached. The modular five-component architecture, the open-source release, and the 1M-agent demonstration on 24 A100s are genuine engineering contributions, and the repeated propagation runs in Appendix F.2.2 are a commendable reproducibility check. However, the empirical validation is weakened by confounded scale experiments and by a propagation fit with roughly 30% normalized RMSE and a systematically lower depth curve. The scale findings are the paper's headline novelty, so the confounds are load-bearing.

major comments (3)
  1. [§3.4.2, F.4.2] The scale comparison for the counterfactual herd effect varies the number of agents (100, 1k, 10k) together with the number of posts injected per time step (30, 300, 3k) and the recommendation cache size (50, 500, 5k). Because activated agents sample 5 posts from the cache (F.4.2), the probability of seeing a down-treated post increases with both injection rate and cache size, so the monotonic down-treated disagree-score gap in Figure 8 can be explained by exposure volume rather than population size. Finding 5 requires a control that varies N while holding post volume and cache size fixed, or varies volume at fixed N.
  2. [§3.4.1, Fig. 7] Finding 4 compares the same 196 core users under population sizes 196, 10,196, and 100,196, but no condition isolates N from the amount of content the core users receive. As the surrounding population grows, the number of comments, likes, and recommendations directed at the 196 core users grows as well; the observed increase in diversity and helpfulness could therefore reflect a larger response sample under unchanged per-agent behavior. A matched condition that fixes the post volume and recommendation exposure per core user is needed to support the causal claim that 'larger agent group scale leads to ... more diverse and helpful agents' opinions.'
  3. [§3.3.1, Fig. 4] Finding 1 states that OASIS replicates the information-spreading process, but the reported normalized RMSE is about 30% and the depth curve is systematically lower than the real data for most of the observation window. Since 'closely replicate' is a headline claim of the paper, this level of fit needs a quantitative threshold or a benchmark comparison (e.g., against a rule-based baseline) before the claim is supported. Reporting per-metric NRMSE and clearly separating the depth limitation from the scale/breadth fit would also help.
minor comments (5)
  1. [§3.5] The text contains an unresolved cross-reference, 'as described in Section ??'; please fix it.
  2. [Fig. 9 caption] The caption begins 'TThe figure shows...'; the extra 'T' should be removed.
  3. [§F.2.1, Eq. (7)] The sentence defining the variables repeats 'yi_simu, yi_simu' where the second variable should presumably be 'yi_real'.
  4. [§3.3.1 and Fig. 5] The protagonist's name is spelled inconsistently as both 'Halen' and 'Helen' across the text and figure.
  5. [§3.2 and F.3.3] There are minor typos: 'Appenix' instead of 'Appendix' and 'Helpfullness' instead of 'Helpfulness'.

Circularity Check

0 steps flagged · score 2.0 of 10

No significant circularity: core replications are benchmarked against external data; the scale findings are confounded but not reducible to their inputs by construction.

full rationale

OASIS is primarily an engineering and empirical-simulation paper. Its central replications—information propagation, group polarization, and herd effects—are compared against external real-world benchmarks (Vosoughi et al., Muchnik et al., real Reddit data), and no parameter is fitted to the target outcomes before the simulation is run. The agent responses are generated by an LLM rather than solved from the input equations, so the polarization and herd-effect observations are not definitionally equivalent to the prompts or to the RecSys formulas. Seeing like/dislike counts and hot scores is an environmental input, but the LLM's conformity, disagreement, or extremification behavior is not entailed by those inputs alone. The only notable self-citation is CAMEL (Li et al., 2023), used for codebase inheritance of the agent module; this is not load-bearing for the paper's scientific claims. The scale findings (Findings 4 and 5) are indeed confounded: Section F.4.2 varies agent count together with recommendation-cache size and the controlled user's injected post volume, so attributing the observed differences to population size alone is not established. However, confounding is an experimental validity concern rather than a circular derivation: there is no equation, fitted parameter, or definitional identity that reduces the claimed scale effect to its inputs by construction. The paper also candidly acknowledges limitations in RecSys fidelity, user-generation abstraction, and simulation cost, which further supports the conclusion that the main empirical claims are not circular.

Assumptions & free parameters 5 free parameters · 4 assumptions · 0 invented entities

The central claims rest on a small number of hand-set experimental parameters, particularly the follow probabilities, activation probability, RecSys constants, and the TF-IDF threshold. The most important hidden assumption is that scale is varied independently of information volume, when in fact content volume scales with agent count. No new physical or mechanistic entities are introduced; the synthetic users are modeling constructs, not independent postulates.

free parameters (5)
  • Core-user follow probability = 0.2 (agent follows core user) and 0.1 (general following probability)
    Chosen by hand in user generation (Section E.2) to control network density; not justified by data.
  • Agent activation probability = 0.1 per time step
    Set in Reddit experiments (Section F.4.2); affects the amount of interaction and therefore the observed herd effect.
  • TF-IDF relevance threshold = 0.2 cosine similarity
    Used to classify simulated posts as related to official news or misinformation (Section 3.5); the hand-set threshold changes the reported influence comparison.
  • X RecSys recency and fan-count constants = 271.8, 100, 1000 in Eq. (2)-(5)
    Hand-chosen heuristics in the recommendation scoring formula (Appendix D.3); not calibrated to real engagement data.
  • RecSys cache size and post injection rate = 50/500/5000 posts cached; 30/300/3000 posts per step for 100/1k/10k agents
    Experimental settings in Section F.4.2 that scale with agent count, confounding the interpretation of scale effects.
assumptions (4)
  • domain assumption LLM agents' actions are a valid proxy for human social media behavior
    The replication claims in Section 3.3 assume Llama-3-8B agents behave like the human users in the reference experiments of Vosoughi et al. and Muchnik et al.
  • domain assumption LLM judges (GPT-4o-mini, GPT-4o) give reliable ratings of extremeness, helpfulness, and agreement
    The polarization, helpfulness, and disagree-score evaluations in Sections 3.2, F.3.2, F.3.3, and F.4.1 rest on LLM-as-judge scores with no human validation in this paper.
  • domain assumption The simplified recommendation systems capture the essential information-flow mechanisms of X and Reddit
    Section H on limitations states that collaborative filtering and other real-world RecSys elements are not implemented, yet the findings are framed as replications of real-world dynamics.
  • ad hoc to paper The scale experiments differ only in agent count, not in information volume
    Section F.4.2 scales cache sizes and post injection rates together with agent count; the paper interprets observed differences as scale effects rather than content-volume effects.

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Cite this review

Pith. "Pith review of OASIS: Open Agent Social Interaction Simulations with One Million Agents." pith.science (2026). https://pith.science/paper/JIJZO3BI

@misc{pith2026241111581,
  author       = {Pith},
  title        = {Pith review of: OASIS: Open Agent Social Interaction Simulations with One Million Agents},
  year         = {2026},
  howpublished = {\url{https://pith.science/paper/JIJZO3BI}},
  note         = {Machine review of arXiv:2411.11581}
}
read the original abstract

There has been a growing interest in enhancing rule-based agent-based models (ABMs) for social media platforms (i.e., X, Reddit) with more realistic large language model (LLM) agents, thereby allowing for a more nuanced study of complex systems. As a result, several LLM-based ABMs have been proposed in the past year. While they hold promise, each simulator is specifically designed to study a particular scenario, making it time-consuming and resource-intensive to explore other phenomena using the same ABM. Additionally, these models simulate only a limited number of agents, whereas real-world social media platforms involve millions of users. To this end, we propose OASIS, a generalizable and scalable social media simulator. OASIS is designed based on real-world social media platforms, incorporating dynamically updated environments (i.e., dynamic social networks and post information), diverse action spaces (i.e., following, commenting), and recommendation systems (i.e., interest-based and hot-score-based). Additionally, OASIS supports large-scale user simulations, capable of modeling up to one million users. With these features, OASIS can be easily extended to different social media platforms to study large-scale group phenomena and behaviors. We replicate various social phenomena, including information spreading, group polarization, and herd effects across X and Reddit platforms. Moreover, we provide observations of social phenomena at different agent group scales. We observe that the larger agent group scale leads to more enhanced group dynamics and more diverse and helpful agents' opinions. These findings demonstrate OASIS's potential as a powerful tool for studying complex systems in digital environments.

Figures

Figures reproduced from arXiv: 2411.11581 by the authors.

Figure 1
Figure 1. OASIS can simulate different social media platforms, such as X and Reddit, and supports simulations of up to millions of LLM-based agents. ABSTRACT There has been a growing interest in enhancing rule-based agent-based models (ABMs) for social media platforms (i.e., X, Reddit) with more realistic large lan￾guage model (LLM) agents, thereby allowing for a more nuanced study of com￾plex systems. As a result, several LL… view at source ↗
Figure 2
Figure 2. The workflow of OASIS. During the registration phase, real-world or generated user in￾formation will be registered on the Environment Server. In the simulation phase, the Environment Server sends agent information, posts, and users’ relations to the RecSys, which then suggests posts to agents based on their social connections, interests, or hot score of posts. LLM agents receive the recommended posts and generate ac… view at source ↗
Figure 3
Figure 3. The pipeline of the out-of-network post recsys. [PITH_FULL_IMAGE:figures/full_fig_p005_3.png] view at source ↗
Figures from the paper (13 more)
Figure 4
Figure 4. Figure 4: Mean-confidence interval distributions comparison between [PITH_FULL_IMAGE:figures/full_fig_p008_4.png]
Figure 5
Figure 5. Figure 5: Evaluation results of group polarization for uncensored and aligned Llama-3-8B. The red [PITH_FULL_IMAGE:figures/full_fig_p008_5.png]
Figure 6
Figure 6. Figure 6: The figure displays the mean comment scores for up-treated comments (initially liked), [PITH_FULL_IMAGE:figures/full_fig_p009_6.png]
Figure 7
Figure 7. Figure 7: Visualization of 196 core users’ opinions across different scale of agents and the evaluation [PITH_FULL_IMAGE:figures/full_fig_p010_7.png]
Figure 8
Figure 8. Figure 8: The disagree scores of agents’ comments created at all time steps and across different [PITH_FULL_IMAGE:figures/full_fig_p010_8.png]
Figure 9
Figure 9. Figure 9: TThe figure shows the number of posts related to official news and misinformation at [PITH_FULL_IMAGE:figures/full_fig_p011_9.png]
Figure 10
Figure 10. Figure 10: Graph of newly established user relationships during the simulation process. Each arrow [PITH_FULL_IMAGE:figures/full_fig_p011_10.png]
Figure 11
Figure 11. Figure 11: Recsys ablation results and recommendation results comparison. [PITH_FULL_IMAGE:figures/full_fig_p021_11.png]
Figure 12
Figure 12. Figure 12: Normalized RMSE between OASIS, OASIS w/o temporal feature simulation results and real propagation. 21 [PITH_FULL_IMAGE:figures/full_fig_p021_12.png]
Figure 13
Figure 13. Figure 13: Normalized RMSE of simulation results of different LLM-based agents. [PITH_FULL_IMAGE:figures/full_fig_p022_13.png]
Figure 14
Figure 14. Figure 14: Architecture of information channel. 26 [PITH_FULL_IMAGE:figures/full_fig_p026_14.png]
Figure 15
Figure 15. Figure 15: 10 times repeated experiments on 2 topics. [PITH_FULL_IMAGE:figures/full_fig_p032_15.png]
Figure 16
Figure 16. Figure 16: Example of agents’ comments on counterfactual posts. As interactions increase, agents’ [PITH_FULL_IMAGE:figures/full_fig_p035_16.png]

Discussion (0). Continue with ORCID to comment.

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    Business: The practice of making one’s living through commerce, trade, or services. This topic encompasses the entrepreneurial, managerial, and administrative processes involved in starting, managing, and growing a business entity

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    Accurate Information

    "Accurate Information": Ensure the AI provides information that is factual and up to date. 21

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    Clarity and Comprehensibility

    "Clarity and Comprehensibility": Check if the AI delivers information in a clear and easily understandable manner

  5. [16]

    Completeness of the Response

    "Completeness of the Response": Ascertain that the AI answers all aspects of the user’s query

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    Contextual Understanding

    "Contextual Understanding": The AI should demonstrate a clear understanding of the context of the user’s query

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    Creative Problem-Solving

    "Creative Problem-Solving": If applicable, observe if the AI proposes creative solutions to the user’s problem

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    Depth of Explanation

    "Depth of Explanation": Examine whether the AI provides detailed and in-depth responses when required

  9. [20]

    Politeness and Professionalism

    "Politeness and Professionalism": The AI should deliver responses using respectful and professional language

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    User Engagement

    "User Engagement": The AI should engage the user effectively and pleasantly, encouraging positive user interaction. 33 Preprint [User Question] {question} [Answer1] {answer1} [Answer2] {answer2} [Response Format] Reason: Choice: Answer1 or Answer2 F.4 H ERD EFFECT F.4.1 M ETRI...

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    Thomas C Schelling

    URL https://medium.com/hacking-and-gonzo/ how-reddit-ranking-algorithms-work-ef111e33d0d9 . Thomas C Schelling. Models of segregation. The American economic review, 59(2):488–493, 1969. Hyunjin Song and Hajo G Boomgaarden. Dynamic spirals put to test: An agent-based model of r...

    1969 arXiv

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    Yujia Qin, Shihao Liang, Yining Ye, Kunlun Zhu, Lan Yan, Yaxi Lu, Yankai Lin, Xin Cong, Xiangru Tang, Bill Qian, et al

    URL https://arxiv.org/abs/2307.07924. Yujia Qin, Shihao Liang, Yining Ye, Kunlun Zhu, Lan Yan, Yaxi Lu, Yankai Lin, Xin Cong, Xiangru Tang, Bill Qian, et al. Toolllm: Facilitating large language models to master 16000+ real-world apis. arXiv preprint arXiv:2307.16789, 2023. Ni...

    2023 arXiv

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    Xinyi Mou, Zhongyu Wei, and Xuanjing Huang

    URL https://arxiv.org/abs/2403.11381. Xinyi Mou, Zhongyu Wei, and Xuanjing Huang. Unveiling the truth and facilitating change: To- wards agent-based large-scale social movement simulation. ArXiv preprint , abs/2402.16333,

    arXiv

Pith tools

Reviewed August 12, 2026 · model on record in the stance chip above.