Investigating social alignment via mirroring in a system of interacting language models

Carey E. Priebe; Harvey McGuinness; Hayden Helm; Tianyu Wang

REVIEW 3 major objections 5 minor 1 cited by

Investigating social alignment via mirroring in a system of interacting language models

T0 review · 3 major / 5 minor · reviewed 2026-08-11 · deepseek-v4-flash

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Pith's one-line read In a system of interacting language models, the communication range k decides whether agents form stable silos, one consensus, or unstable groups; mirroring rate p amplifies that pattern.

desk verdict A simple honest LLM-agent alignment framework; the high-k unstable-silo claim is a finite-horizon artifact and the mirroring knob mostly slows information exchange by no-op updates. read the letter →

arxiv 2412.06834 v2 pith:YIBCMIS7 submitted 2024-12-07 cs.MA cs.AIcs.CY

Harvey McGuinness , Tianyu Wang , Carey E. Priebe , Hayden Helm This is my paper · ORCID

classification cs.MAcs.AIcs.CY

keywords socialalignmentmirroringmulti-agentsystemslargelanguagemodelsretrieval-augmentedgenerationopiniondynamicssiloformationk-nearest-neighborinteractions

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

Mirroring—an agent copying the opinion of the person it talks to—is one proposed mechanism behind social alignment, but its system-level effects have been hard to study at scale. The paper builds a minimal multi-agent system in which each of n chat language models holds a private database of flower descriptions, picks one of its k nearest neighbors to talk to each step, and with probability p is mirrored (keeping its own opinion) or with probability 1-p receives the neighbor's opinion instead. The central claim is that the communication range k is the primary determinant of population structure: small k produces stable silos, $k/n$ around $0.5$ produces consensus, and k near n produces unstable or decaying silos, with the mirroring rate p exaggerating these tendencies rather than reversing them. A sympathetic reader should care because the framework offers a scalable, parameterized proxy for opinion dynamics that connects directly to echo chambers, polarization, and the difficulty of polling a highly mirroring population.

What carries the argument

The load-bearing mechanism is the interaction-update cycle. At each time step every agent is asked to describe the prettiest flower; the sentences are embedded with nomic-embed-v1.5 into vectors in $\mathbb{R}^{768}$, and pairwise Euclidean distances define each agent's k-nearest-neighbor communication graph. The agent then interacts with exactly one neighbor chosen uniformly at random, and its database is overwritten with either its own answer (mirroring, probability p) or the neighbor's answer (information exchange, probability $1-p$). Silos are extracted by finding the named flower species in each response. The metrics $S(t)$ (proportion of agents whose silo is unchanged) and $E(t)$ (entropy of silo counts) turn this into the classification of stable, unstable, and decaying silos.

What would settle it

Run the same k-by-p grid with $n = 30$ and $T = 80$ but change the update rule so each agent's database keeps the last m received responses (or a weighted average of them) before generating its next answer; if the pattern of stable silos at small k, consensus near $k/n \approx 0.5$, and unstable silos at large k persists for $m > 1$, the memoryless overwrite is not load-bearing, and if it vanishes, the central claim fails.

Watch

Extended reading notes

Core claim

The discovery is a simple phase-like ordering of group behavior across the $(k,p)$ grid at $T = 80$. When agents can talk only to a small neighborhood, the population settles into stable, separate silos whose members rarely change. When the neighborhood is roughly half the population and mirroring is rare, the whole population collapses into one silo. When communication is nearly global, the population tends to contain multiple silos that are unstable (members oscillate between groups) or decaying (one group absorbs the others), and high mirroring rates push this splintering further and slow convergence. The authors account for this with a perspective-filtering effect: local communication restricts agents to similar others, so mirroring mostly delays information exchange in large-range settings; at $p = 1$ no informative interaction ever occurs, and the number of silos equals the number of distinct initial opinions.

Load-bearing premise

Everything rests on the rule that an agent's opinion is simply the single response from its latest interaction: no agent accumulates history, so the process is a first-order Markov chain, and the reported k and p effects could change if agents remembered and blended multiple past opinions.

Editorial extensions

If this is right

Small communication ranges lock populations into stable, separate silos, so global consensus is unreachable no matter how often agents mirror one another.
At an intermediate range around $k/n \approx 0.5$, consensus is the typical outcome provided the mirroring probability is not too high.
Near-global communication does not guarantee consensus; it tends to create several unstable or decaying groups, and larger p makes this splintering more severe.
Higher mirroring rates lengthen transient states, so a population observed at a fixed time may look more fragmented than its eventual steady state, as the paper's Figure 6 shows.
If the dynamics transfer to human populations, high mirroring rates could make opinion polls less reliable because many groups remain unstable or decaying at the time of measurement.

Reading between the lines

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

The paper does not test this, but replacing the overwrite rule with a cumulative memory (keeping several past responses and letting the model synthesize them) would show whether the consensus peak near $k/n \approx 0.5$ is an artifact of the memoryless update.
The embedding vectors contain similarity information across flower species that the Flower-ID silo labels discard; clustering the vectors directly could reveal partial alignment long before silo membership changes.
The paper's own suggestion that Gaussian mixture models approximate the system implies that the phase boundaries in $(k,p)$ space might be derivable analytically, giving a testable mathematical model of when silos form.
A practical extension suggested by the mirroring-as-delay reading is that lowering the effective mirroring rate—for example by forcing informative exchange in a fraction of interactions—should speed consensus in large-range populations; the paper does not run this experiment.

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Editorial analysis

A structured set of objections, weighed in public.

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

Desk Editor's Note

read the letter

Quick take: this is a tidy small-scale simulation paper that builds a reusable framework for studying communication range and mirroring in LLM agent groups. The genuinely new piece is the three-part silo taxonomy (stable/unstable/decaying) and the demonstration that k, not p, is the primary structural driver. Credit where due: the design is simple and interpretable, the figures are clear, and the Limitations section is candid about T-dependence and about the GMM approximation.

The soft spots are real, though. The strongest claim—that k→n yields unstable or decaying silos—is made at T=80, and their own Figure 6 shows a p=0.2, k=29 system that looks unstable at T=80 but reaches a single silo by T>105. So the high-k 'unstable' regime is largely a finite-horizon classification artifact, not a distinct long-term pattern. The paper does note this in Limitations, but the abstract and Section 2.2 state it without the qualifier. That matters.

Second, mirroring is implemented as 'Ai updates itself with its own answer'—i.e., a no-op. So increasing p simply reduces the effective number of copying events per time step. The finding that high p slows convergence is then a direct consequence of the update rule, not an emergent social mechanism. The authors partly acknowledge this ('there is little opportunity for actual information to be shared'), but they still frame p as 'exacerbating' the k effect, which oversells it.

The empirics are also thin: no statistical tests, hand-chosen thresholds (m=8, T=80), only 8 random initializations per (p,k), and no released code. That limits reproducibility, though the paper is honest about the small n.

The reader's weak-assumption point is fair: the memoryless overwrite of the database makes the process first-order Markov, and the silo patterns likely depend on that. The paper flags that in Limitations only for miscommunication extensions, not as a core caveat.

Bottom line: this deserves a serious referee. The framework is useful and the paper is honest, but it needs longitudinal analysis across T, statistical grounding, and a sharper statement that the p-effect is a trivial consequence of the no-op update. I'd cite the framework if I were working on LLM agent testbeds, but not the headline results.

Referee Report

3 major / 5 minor

Summary. The paper introduces a computational framework for studying social alignment through mirroring in a system of interacting large language models. Each of n=30 LLaMA agents has a distinct RAG database of flower descriptions, and at each time step agents answer a query, then one of their k nearest neighbors is selected; with probability p the neighbor mirrors the agent (no state change), and with probability 1-p the agent copies the neighbor's answer. The authors measure alignment via embedding distances and classify the resulting dynamics into stable, unstable, decaying, and one-silo patterns as functions of the communication range k and the mirroring probability p. The central empirical claim is that k is the primary determinant of silo structure (small k gives stable silos, k/n≈0.5 gives consensus, k→n gives unstable/decaying silos) while p acts as a secondary amplifier that slows convergence and increases silo counts at large k. The paper discusses parallels to human social dynamics, including echo chambers and information overload.

Significance. If the central claims hold, the paper offers a simple, transparent, and scalable testbed for studying emergent group dynamics in LLM-based multi-agent systems, an area of growing interest. The framework's clarity is a strength: the update rule, metrics S(t) and E(t), and classification criteria are explicitly defined, and the authors are unusually candid about limitations (Section 4), including the T-dependence of classification and the possibility of approximating the system with interacting Gaussian mixtures. However, the results are primarily descriptive and the two main effects are not equally robust: the k effect is clouded by a finite-horizon artifact that the paper's own Figure 6 reveals, and the p effect is partly built into the update rule because 'mirroring' is a no-op that simply reduces the effective number of copying events. The paper is best seen as a proof-of-concept framework with qualitative findings that require stronger statistical support and longer-horizon validation before the sociological interpretations can be endorsed.

major comments (3)

[Section 2.2, Figure 6, and Section 4] The claim that 'when k approaches n we observe unstable or decaying silos' is not supported beyond the chosen finite horizon T=80. Figure 6 shows a representative system with p=0.2 and k=29 (k/n≈0.97) that is classified as unstable at T=80 but reaches a single silo for T>105. Since Figures 4 and 5 classify all systems at T=80, the high-k unstable silos appear to be transient states of a voter-model-like process that has not yet reached consensus by the evaluation time, not a distinct long-term regime. The paper's own Limitations section concedes that 'classification of a system will depend on the choice of T,' yet the abstract and Section 2.2 state the k→n result without qualification. The authors should either re-run the analysis with longer horizons (e.g., T≥200) for the high-k regime and report whether the unstable/decaying classification persists, or explicitly reframe the central claim as a finite-T statement. The current wording overstates the finding.
[Section 1, Interaction Step] Mirroring is implemented as the receiving agent 'updating itself with its own answer,' i.e., no state change. Consequently, increasing p directly reduces the number of information-exchanging events and slows convergence by construction; at p=1, the initial flower IDs are frozen. The statement in Section 2.2 that 'when p is large, systems stay in transient states for longer' and Figure 5's conclusion that 'increasing p decreases the likelihood of global consensus' are therefore in large part a mechanistic consequence of the update rule rather than an emergent social phenomenon. This is not a fatal flaw, but the paper should explicitly separate this timescale effect from the k-dependent silo structure, and ideally include a control condition in which mirroring is implemented as copying the neighbor's opinion with some probability (rather than a no-op) to distinguish the effect of reduced information flow from the effect of alignment behavior.
[Section 2.2, Figures 4 and 5] The empirical support for the qualitative claims rests on hand-chosen classification thresholds (T=80, m=8) and only 8 random initializations per (p,k) setting, with no statistical tests. The shaded regions in Figures 4 and 5 represent ±3 standard errors of the mean silo count, but the central claims are about the type of silos (stable/unstable/decaying/one-silo), and no confidence intervals or hypothesis tests are provided for the classification proportions. For example, the claim that 'for k/n≈0.5 systems typically contain a single silo' is based on a small number of dots that are not statistically summarized. The authors should provide at least a sensitivity analysis of the thresholds and standard errors for the classification proportions, or use a more robust method such as repeated sampling with more initializations and reporting bootstrap confidence intervals.

minor comments (5)

[Section 3] The first paragraph refers to 'Section 2.2.1' and 'Section 2.2.2' for the system classification and the effects of p and k, but the manuscript has no numbered subsections; these should refer to Section 2.1 and Section 2.2 respectively.
[Figure 6 caption and Section 4] There is an inconsistency in the reported time of convergence to a single silo: the Figure 6 caption says 'for T > 105' while the Limitations text says 'a single silo for t>110'. Please make these numbers consistent.
[Section 1, Interaction Step] The sentence 'The agents in which agent Ai can communicate with is determined by its k-nearest neighbors (says D(t))' is grammatically unclear; presumably 'D(t)' refers to the distance matrix computed from the embeddings. Please clarify the notation and whether the k-nearest neighbors are computed over all other agents using the rows of D(t).
[Section 1, Measurement Step] The choice of l2 distance over cosine similarity is motivated by a future multi-query extension, but the current paper uses a single query; a brief note that the results are not sensitive to this choice (or a cosine-based control) would strengthen the presentation.
[Section 2.1] The definition of Pattern III ('decaying silos') uses 'suitably chosen m' and then sets m=T/10=8; since this is an arbitrary choice, it should be stated as a modeling decision rather than a natural choice, and a brief sensitivity check (e.g., m=5 or m=12) would help.

Circularity Check

1 steps flagged · score 5.0 of 10

The p-dependent slowdown is a no-op built into the update rule; k-dependent silo regimes are emergent and keep the paper from being fully circular.

self definitional [Section 1, Interaction Step (update probabilities p/1−p); Section 2.2, first paragraph and p=1 remark]
"With probability p (constant for all agents), agent Aj mirrors Ai. Functionally, this means Ai “updates” itself with its own answer B_i^{(t)}. ... We note that when p = 1, the number of silos is equal to the number of flower IDs present in the initial conditions, as the opportunity for an informative interaction has been completely replaced by mirroring interactions."

Mirroring is implemented as a self-copy: in the mirroring branch the receiving agent's database is unchanged, so the only information-transmitting event is the complementary 1−p branch. Hence the expected number of actual opinion transfers per round is n(1−p); the paper's finding that “when p is large, systems stay in transient states for longer” restates this arithmetic rather than testing it. The p=1 remark concedes the endpoint is fixed by the initial flower IDs. Since this no-op mechanism is the basis of the paper's secondary claim that mirroring “exaggerates” the k trend, part of the reported effect is present by construction.

full rationale

Score 5 reflects one partial, by-construction reduction. The p-effect is not a fitted prediction but the update rule's definition: larger p mechanically reduces the number of copying events, and p=1 freezes initial conditions, as the paper itself notes. This is a genuine self-definitional component of the central narrative, but it is secondary. The k-dependent patterns (stable silos for small k, one silo near k/n=0.5, unstable/decaying near k=n) are not equivalent to the inputs: they emerge from nearest-neighbor interaction probabilities, LLM generation, and the T=80 classification procedure, and are therefore independent content. No load-bearing self-citation occurs: Yang et al. (2021) and Helm et al. (2023, 2024) support context or techniques, and no uniqueness theorem from the authors is invoked to forbid alternatives. Section 4 explicitly flags a temporal-horizon limitation—“The classification of a system will depend on the choice of T, as seen in Figure 6 where we observe unstable silos at t=80 and a single silo for t>110”—which I weigh as a correctness risk for the k→n claim, not as circularity. Because the paper's own strongest claim about the k→n regime is undercut by that admitted finite-horizon artifact, the overall finding is partial circularity rather than no circularity, but the emergent k-regime behavior prevents a score of 8 or higher.

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

The central claims rest on treating LLM outputs as measurements of opinion, on embedding distance as a proxy for social alignment, and on a memoryless interaction update. These are domain assumptions, not derived results. The main simulation parameters p and k are experimental controls, and T and m are hand-chosen analysis thresholds.

free parameters (4)

p (mirroring probability) = swept (values not tabulated)
Key independent variable; probability that communicating agent mirrors. Not fitted to data.
k (communication range) = swept up to n
Key independent variable; number of nearest neighbors an agent can interact with. Not fitted.
T (time horizon) = 80
Hand-chosen horizon for classification; authors acknowledge results depend on T.
m (decay window) = 8
Hand-chosen threshold, T/10, used to classify decaying silos.

assumptions (4)

domain assumption LLMs equipped with per-agent RAG databases generate responses that vary like individual human opinions.
The framework uses prompt plus database as a proxy for human cognitive diversity; no external validation is provided.
domain assumption l2 distance between sentence embeddings is a valid measure of alignment between agents.
Embedding distance is used to define nearest neighbors and to quantify alignment, but its correspondence to human social alignment is assumed.
ad hoc to paper Mirroring is represented by the receiving agent updating with its own answer, i.e., no state change.
This specific operationalization, described in Section 1 and Figure 1, means mirroring is equivalent to no information exchange; this is a modeling choice particular to this paper.
domain assumption Each agent's database is overwritten by the single latest interaction, giving memoryless dynamics.
The Interaction Step replaces the database content with the received response; this first-order Markov assumption is load-bearing for the observed silo dynamics.

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Cite this review

Pith. "Pith review of Investigating social alignment via mirroring in a system of interacting language models." pith.science (2026). https://pith.science/paper/YIBCMIS7

@misc{pith2026241206834,
  author       = {Pith},
  title        = {Pith review of: Investigating social alignment via mirroring in a system of interacting language models},
  year         = {2026},
  howpublished = {\url{https://pith.science/paper/YIBCMIS7}},
  note         = {Machine review of arXiv:2412.06834}
}

read the original abstract

Alignment is a social phenomenon wherein individuals share a common goal or perspective. Mirroring, or mimicking the behaviors and opinions of another individual, is one mechanism by which individuals can become aligned. Large scale investigations of the effect of mirroring on alignment have been limited due to the scalability of traditional experimental designs in sociology. In this paper, we introduce a simple computational framework that enables studying the effect of mirroring behavior on alignment in multi-agent systems. We simulate systems of interacting large language models in this framework and characterize overall system behavior and alignment with quantitative measures of agent dynamics. We find that system behavior is strongly influenced by the range of communication of each agent and that these effects are exacerbated by increased rates of mirroring. We discuss the observed simulated system behavior in the context of known human social dynamics.

Figures

Figures reproduced from arXiv: 2412.06834 by the authors.

**Figure 2.** Three patterns of silos: Stable (Pattern I), Unstable (Pattern II), and Decaying (Pattern III), coming from [PITH_FULL_IMAGE:figures/full_fig_p004_2.png] view at source ↗

**Figure 3.** Example systems of n = 30 with different agent behaviors. Graph color (red/blue/green/orange) indicates the pattern of stable/decaying/unstable/one silo(s) at T = 80. Small range of communication for each agent (k) appears to prohibit global alignment. Large likelihood of mirroring (p) delays global alignment. We investigate these relationships further in Figures 4 and 5. t > 20. The rightmost figure shows an exampl… view at source ↗

Figures from the paper (2 more)

**Figure 4.** Figure 4: Number of Silos vs. k (the range of communication) for several values of p (the likelihood of an agent interacting with a mirroring agent). For each setting of agent behavior we include the number of silos observed at T = 80 for 8 random agent initializations. Dot col…

**Figure 6.** Figure 6: Example system with p = 0.2, k = 29, T = 160. The system has unstable silos at T = 80 and a single silo for T > 105 – indicating that longitudinal analysis may provide additional insights into system behavior. database for each agent is updated to perfectly reflect it…

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Works this paper leans on

30 extracted references · 16 canonical work pages · cited by 1 Pith paper

[1]

online" 'onlinestring :=

ENTRY address archivePrefix author booktitle chapter edition editor eid eprint eprinttype howpublished institution journal key month note number organization pages publisher school series title type volume year doi pubmed url lastchecked label extra.label sort.label short.list INTEGERS output.state before.all mid.sentence after.sentence after.block STRING...
[2]

write newline

" write newline "" before.all 'output.state := FUNCTION n.dashify 't := "" t empty not t #1 #1 substring "-" = t #1 #2 substring "--" = not "--" * t #2 global.max substring 't := t #1 #1 substring "-" = "-" * t #2 global.max substring 't := while if t #1 #1 substring * t #2 global.max substring 't := if while FUNCTION word.in bbl.in capitalize " " * FUNCT...
[3]

Trosset, Carey E

Aranyak Acharyya, Michael W. Trosset, Carey E. Priebe, and Hayden S. Helm. 2024. https://arxiv.org/abs/2409.17308 Consistent estimation of generative model representations in the data kernel perspective space . Preprint, arXiv:2409.17308

arXiv 2024
[4]

Arriaga, and Adam Tauman Kalai

Gati V Aher, Rosa I. Arriaga, and Adam Tauman Kalai. 2023. Using large language models to simulate multiple humans and replicate human subject studies. In Proceedings of the 40th International Conference on Machine Learning, volume 202 of Proceedings of Machine Learning Research, pages 337--371. PMLR

work page 2023
[5]

Andrew Burton-Jones, Alicia Gilchrist, Peter Green, and Michael Draheim. 2020. https://cacm.acm.org/research/improving-social-alignment-during-digital-transformation/ Improving social alignment during digital transformation

work page 2020
[6]

Richard Byrne. 2005. Social cognition: Imitation, imitation, imitation. Current Biology

work page 2005
[7]

Daniel Carlson and Chris Knoester. 2011. https://doi.org/10.1177/0192513X10396662 Family structure and the intergenerational transmission of gender ideology . Journal of Family Issues - J FAM ISS, 32:709--734

work page doi:10.1177/0192513x10396662 2011
[8]

Matteo Cinelli, Gianmarco De Francisci Morales, Alessandro Galeazzi, Walter Quattrociocchi, and Michele Starnini. 2021. https://doi.org/10.1073/pnas.2023301118 The echo chamber effect on social media . Proceedings of the National Academy of Sciences, 118(9):e2023301118

doi:10.1073/pnas.2023301118 2021

Show all 30 references

[9]

Helm, and Carey E

Brandon Duderstadt, Hayden S. Helm, and Carey E. Priebe. 2024. https://arxiv.org/abs/2305.05126 Comparing foundation models using data kernels . Preprint, arXiv:2305.05126

2024 arXiv
[10]

Mattia Gallotti, Merle Fairhurst, and Chris Frith. 2017. https://doi.org/10.1016/j.concog.2016.12.002 Alignment in social interactions . Consciousness and Cognition, 48:253--261

2017 doi
[11]

Yunfan Gao, Yun Xiong, Xinyu Gao, Kangxiang Jia, Jinliu Pan, Yuxi Bi, Yi Dai, Jiawei Sun, Meng Wang, and Haofen Wang. 2024. https://arxiv.org/abs/2312.10997 Retrieval-augmented generation for large language models: A survey . Preprint, arXiv:2312.10997

2024 arXiv
[12]

James L Gibson and Joseph L Sutherland. 2023. https://doi.org/10.1093/psquar/qqad037 Keeping your mouth shut: Spiraling self-censorship in the united states . Political Science Quarterly, 138(3):361--376

2023 doi
[13]

Uri Hasson and Chris D Frith. 2016. https://pmc.ncbi.nlm.nih.gov/articles/PMC4843605/ Mirroring and beyond: Coupled dynamics as a generalized framework for modelling social interactions

2016
[14]

Hayden Helm, Aranyak Acharyya, Brandon Duderstadt, Youngser Park, and Carey E. Priebe. 2024 a . https://arxiv.org/abs/2410.01106 Embedding-based statistical inference on generative models . Preprint, arXiv:2410.01106

2024 arXiv
[15]

Hayden Helm, Brandon Duderstadt, Youngser Park, and Carey Priebe. 2024 b . https://aclanthology.org/2024.emnlp-main.90 Tracking the perspectives of interacting language models . In Proceedings of the 2024 Conference on Empirical Methods in Natural Language Processing, pages 15...

2024
[16]

Priebe, and Weiwei Yang

Hayden Helm, Carey E. Priebe, and Weiwei Yang. 2023. https://arxiv.org/abs/2309.08913 A statistical turing test for generative models . Preprint, arXiv:2309.08913

2023 arXiv
[17]

Will Jennings and Christopher Wlezien. 2018. Election polling errors across time and space. Nature Human Behaviour, 2(4):276--283

2018
[18]

Alison Ledgerwood and Y Wang. 2018. Achieving local and global shared realities: distance guides alignment to specific or general social influences. Current Opinion in Psychology

2018
[19]

Gilat Levy and Ronny Razin. 2020. https://ppr.lse.ac.uk/articles/10.31389/lseppr.5 Social media and political polarisation

2020 doi
[20]

Manning, Prabhakar Raghavan, and Hinrich Schütze

Christopher D. Manning, Prabhakar Raghavan, and Hinrich Schütze. 2008. Introduction to Information Retrieval. Cambridge University Press

2008
[21]

Sachin Modgil, Rohit Kumar Singh, Shivam Gupta, and Denis Dennehy. 2021. https://www.ncbi.nlm.nih.gov/pmc/articles/PMC8604707/ A confirmation bias view on social media induced polarisation during covid-19

2021
[22]

Morris, Brandon Duderstadt, and Andriy Mulyar

Zach Nussbaum, John X. Morris, Brandon Duderstadt, and Andriy Mulyar. 2024. https://arxiv.org/abs/2402.01613 Nomic embed: Training a reproducible long context text embedder . Preprint, arXiv:2402.01613

2024 arXiv
[23]

Joon Sung Park, Joseph O'Brien, Carrie Jun Cai, Meredith Ringel Morris, Percy Liang, and Michael S Bernstein. 2023. Generative agents: Interactive simulacra of human behavior. In Proceedings of the 36th annual acm symposium on user interface software and technology, pages 1--22

2023
[24]

Ransom, Rick Dale, Roger J

Tailer G. Ransom, Rick Dale, Roger J. Kreuz, and Deborah Tollefsen. 2019. How do different types of alignment affect perceived entity status? Journal of Psycholinguistic Research

2019
[25]

Sidney Redner. 2019. https://doi.org/10.1016/j.crhy.2019.05.004 Reality-inspired voter models: A mini-review . Comptes Rendus Physique, 20(4):275--292

2019 doi
[26]

Hugo Touvron, Louis Martin, Kevin Stone, Peter Albert, Amjad Almahairi, Yasmine Babaei, Nikolay Bashlykov, Soumya Batra, Prajjwal Bhargava, Shruti Bhosale, et al. 2023. Llama 2: Open foundation and fine-tuned chat models. arXiv preprint arXiv:2307.09288

2023 arXiv
[27]

Bahar Tunçgenç, Martha Newson, Justin Sulik, Yi Zhao, Guillaume Dezecache, Ophelia Deroy, and Marwa El Zein. 2022. Social alignment matters: Following pandemic guidelines is associated with better wellbeing. BioMed Central

2022
[28]

Holyoak, and Hongjing Lu

Taylor Webb, Keith J. Holyoak, and Hongjing Lu. 2023. https://arxiv.org/abs/2212.09196 Emergent analogical reasoning in large language models . Preprint, arXiv:2212.09196

2023 arXiv
[29]

Chao Xu, Jinyang Li, Dachun Sun, Ruijie Wang, Tarek Abdelzaher, Jesse Graham, and Boleslaw Szymanski. 2021. https://workshop-proceedings.icwsm.org/ On polarization dynamics in the age of information overload

2021
[30]

Vicky Chuqiao Yang, Mirta Galesic, Harvey McGuinness, and Ani Harutyunyan. 2021. https://doi.org/10.1073/pnas.2106292118 Dynamical system model predicts when social learners impair collective performance . Proceedings of the National Academy of Sciences, 118(35):e2106292118

2021 doi

Pith tools

Reviewed August 11, 2026 · model on record in the stance chip above.

[1] [1]

online" 'onlinestring :=

ENTRY address archivePrefix author booktitle chapter edition editor eid eprint eprinttype howpublished institution journal key month note number organization pages publisher school series title type volume year doi pubmed url lastchecked label extra.label sort.label short.list INTEGERS output.state before.all mid.sentence after.sentence after.block STRING...

[2] [2]

write newline

" write newline "" before.all 'output.state := FUNCTION n.dashify 't := "" t empty not t #1 #1 substring "-" = t #1 #2 substring "--" = not "--" * t #2 global.max substring 't := t #1 #1 substring "-" = "-" * t #2 global.max substring 't := while if t #1 #1 substring * t #2 global.max substring 't := if while FUNCTION word.in bbl.in capitalize " " * FUNCT...

[3] [3]

Trosset, Carey E

Aranyak Acharyya, Michael W. Trosset, Carey E. Priebe, and Hayden S. Helm. 2024. https://arxiv.org/abs/2409.17308 Consistent estimation of generative model representations in the data kernel perspective space . Preprint, arXiv:2409.17308

arXiv 2024

[4] [4]

Arriaga, and Adam Tauman Kalai

Gati V Aher, Rosa I. Arriaga, and Adam Tauman Kalai. 2023. Using large language models to simulate multiple humans and replicate human subject studies. In Proceedings of the 40th International Conference on Machine Learning, volume 202 of Proceedings of Machine Learning Research, pages 337--371. PMLR

work page 2023

[5] [5]

Andrew Burton-Jones, Alicia Gilchrist, Peter Green, and Michael Draheim. 2020. https://cacm.acm.org/research/improving-social-alignment-during-digital-transformation/ Improving social alignment during digital transformation

work page 2020

[6] [6]

Richard Byrne. 2005. Social cognition: Imitation, imitation, imitation. Current Biology

work page 2005

[7] [7]

Daniel Carlson and Chris Knoester. 2011. https://doi.org/10.1177/0192513X10396662 Family structure and the intergenerational transmission of gender ideology . Journal of Family Issues - J FAM ISS, 32:709--734

work page doi:10.1177/0192513x10396662 2011

[8] [8]

Matteo Cinelli, Gianmarco De Francisci Morales, Alessandro Galeazzi, Walter Quattrociocchi, and Michele Starnini. 2021. https://doi.org/10.1073/pnas.2023301118 The echo chamber effect on social media . Proceedings of the National Academy of Sciences, 118(9):e2023301118

doi:10.1073/pnas.2023301118 2021