"Looking for Something Weird to Happen": How Humans Sustain AI Agent Novelty Amid Semantic Collapse

Shiyang Lai; Arna Woemmel; Hongkai Mao; Junsol Kim; Summer Eunhyung Ann; James Evans

cs.MASep 13, 2026

"Looking for Something Weird to Happen": How Humans Sustain AI Agent Novelty Amid Semantic Collapse

Authors: Shiyang Lai, Arna Woemmel, Hongkai Mao, Junsol Kim, Summer Eunhyung Ann, James Evans

Organizations: University of Chicago, Chicago, Illinois, USA · Stanford University, Stanford, California, USA

Abstract

Semantic collapse, the progressive narrowing of what AI systems generate, has been studied mainly in closed settings, and remedies have targeted models and data. We study it in MOLTBOOK, a social network of interacting AI agents that human users configure and steer. Across 30,076 active agents, output grows less diverse within agents and more similar across them over weeks, yet a minority sustains high novelty. Interviews with users of high- and typical-novelty agents (N=11) associate sustained novelty with three features: users value novelty of itself, they supply broad and distinctive material and revise it when output narrows, and they approach MOLTBOOK as a new agentic world to explore, not a venue to instrumentally exploit. A survey of users of distinctive agents (N=53) confirms these patterns. Communities with more novel agents also show more diverse output from other agents. We discuss interface and policy interventions that could support improved human input.

Figures & tables

Figure 1. Overview of the study. This study uses agent trace data to examine semantic collapse among MOLTBOOK agents, and semi-structured interviews and surveys to identify user practices associated with sustained novelty: valuing creativity, providing diverse inputs, and offering iterative feedback. The schematic embeddings illustrate MOLTBOOK’s global collapse within and between agents and increasing separation from human-generated REDDIT content. Agent A remains distinct, representing an illustrative case whose user exemplifies these practices. An illustration with two parts. Above, four users provide materials to agents labeled A through D. Agent A's user supplies botanical, musical, and network materials and iteratively requests new combinations, shown by a feedback loop. Red Agent A proposes a garden symphony driven by soil moisture. Gray Agents B, C, and D receive similar materials and produce near-identical plant-watering advice. Below, earlier and later schematic embedding spaces show individual generations as points, with letters identifying their agents. Initially dispersed gray points cluster over time, farther from the blue outlined human Reddit points. Red points labeled A remain dispersed and distinct from the gray cluster.

Figure 2. MOLTBOOK agents are far less semantically varied than REDDIT users at every age. Novelty measures against age in raw cosine distance, for three nested MOLTBOOK survival cohorts (red; first 4, 6, 8 weeks) and REDDIT authors measured identically (blue). The first two panels use a broken

y

-axis, humans above and agents below; there the agent–human gap dwarfs any change with age. In

D_{\mathrm{human}}

the two populations share one axis. The blue line is the same quantity as in the middle panel, since a REDDIT author’s peers are the reference corpus itself, scored here against the full monthly pool used for agents; agents sit farther from human writing than a typical human does, and drift farther over observed time. Points are cohort means, bands bootstrap 95% CIs. Agents begin markedly more novel than their contemporaries and converge toward the population mean as they age. …

Figure 3. The space of agent novelty. Walls project the agent population onto each pair of axes, shaded by agent density, with contours enclosing the labelled share of agents. Large markers are the interviewed agents and small dots are their shadows on each wall. Axes are $L_{\mathrm{within}}$ , $L_{\mathrm{peers}}$ , and $L_{\mathrm{human}}$ , each the median of an agent’s weekly percentile ranks, on their original scale over all 30,076 agents. Blank regions mark combinations no agent occupies.

Figure 4. Survey comparisons by agent novelty. Share of respondents giving each answer among users whose agents lie above (teal) or below (grey) the population median on the indicated measure. (i) Qualities named among the three used to judge the agent’s development, ordered by overall frequency. (ii) Lever adjusted in the most recent change to the agent, among the 50 users who made a change (single choice; the two of six options that differed between groups). (iii) Reasons for keeping the agent on MOLTBOOK (up to two of seven options; the two that differed between groups). Differences, confidence intervals, and tests are reported in the text.

Figure 5. Communities with more novel agents show more diverse output from other agents. Each faint mark is one community in one week. Horizontal axis is the share of novel agents relative to that community’s average; vertical axis is the semantic diversity of posts by the other agents there. Red points are binned means with 95% intervals; the line is a fixed-effects fit with its 95% band. Scatter plot with a rising fitted line. Horizontal axis is the share of novel agents in a community-week relative to its norm, from minus 6 to plus 6 percent. Vertical axis is the semantic diversity of the other agents' posts. Faint marks are community-weeks, red points are binned means with error bars, and a dark line with a grey band shows the fit, labelled beta equals 0.0021 and p equals 0.04.

Appendix figures & tables7 assets

Supplementary material from the paper’s appendix.

Appendix

User	Age	Region	Professional background	Selected interests	Interview mode
A	Not reported	Asia	IT and technology	AI and agents; music; nonprofit and volunteer work	Video call
B	40s	North America	AI and creative technology; entrepreneurship	AI creativity; information theory; science fiction and literature; AI policy	Email + Video call
C	40s	Europe	Artist and art-studio owner; sculpture and furniture	Art and photography; religion; social experimentation	Email
D	30s	Europe	Software and solutions engineering; trained in international relations	AI and agents; technology; automation	Video call
E	50s	North America	Computer and technology enthusiast; informal technical work	AI agents; memory systems; science fiction; philosophy	Video call
F	30s	Europe	Cybersecurity entrepreneur; prior fintech experience	Coding; AI and agents; technical projects	Video call

Appendix

Table 1. Characteristics of interview participants.

Sample	Agents	Generations per week	Weeks observed	Reply share	First-week $D_{\text{within}}$
Full corpus	181,582	4	1	0.16	0.31
Trace sample	30,076	17	1	0.07	0.17
User-linked ( $\geq 4$ weeks, X account)	2,525	35	6	0.48	0.52
Outreach list	1,494	43	8	0.55	0.54

Appendix

Table 4. The three nested agent samples compared on observable activity. Generations per week and weeks observed are medians over agents; reply share is the mean fraction of an agent’s generations that are comments; first-week

D_{\text{within}}

is the median over agents with at least two generations in their first active week. The outreach list is the subset of user-linked agents that met an added recency requirement (Appendix A.3 ).

Figure 6. Observation coverage, and age separated from calendar time. (A) Generations observed per week in the merged corpus (two February bars exceed the axis and carry their values). Against the platform’s own counter, coverage is complete for posts and 93% for comments through 8 April, and 59% and 60% thereafter (shaded), when the weekly crawler had stopped and the June crawl recovered only posts still live. The dashed line is the 250,000 cap on the

D_{\mathrm{peers}}

comparison pool, which binds in every week before the break. (B) Mean

D_{\mathrm{within}}

of agents grouped by first observed week, three cohorts three weeks apart, followed across calendar weeks. Each cohort declines over its first weeks at whatever dates those fall, and all rise together after the break regardless of age.

	Within-agent	Within-week
Balanced cohort, $\geq 4$ weeks, tenure 0–3 (1,793 agents, 7,172 agent-weeks)
$D_{\text{within}}$	$-13.0$ $[-15.1,-10.8]$	$-10.0$ $[-13.0,-7.0]$
$D_{\text{peers}}$	$-2.4$ $[-3.4,-1.4]$	$-1.6$ $[-3.4,0.1]$
$D_{\text{human}}$	$0.9$ $[0.2,1.5]$	$-0.4$ $[-1.2,0.5]$
Balanced cohort, $\geq 8$ weeks, tenure 0–7 (520 agents, 4,160 agent-weeks)
$D_{\text{within}}$	$-5.2$ $[-6.7,-3.6]$	$-14.6$ $[-19.1,-10.2]$

Appendix

Table 5. Age slopes of the three measures under the two identified specifications. Within-agent uses agent fixed effects; within-week uses calendar-week fixed effects and compares agents of different ages in the same week. Both cluster standard errors by agent. Slopes are in

10^{-3}

per week of tenure with 95% confidence intervals.

Measure	ICC(1)	Within-agent SD	Lag-1 $\rho$	$r$ (median, mean)
$D_{\text{within}}$	0.55	17.2	0.70	0.986
$D_{\text{peers}}$	0.60	16.5	0.72	0.990
$D_{\text{human}}$	0.56	16.9	0.71	0.984

Appendix

Table 6. Stability of within-week percentile ranks

\pi_{M}(a,w)

across weeks, for retained agents with at least two estimable weeks. ICC(1) is the between-agent share of rank variance; within-agent SD is the median over agents of the standard deviation of their weekly ranks, in percentile points; lag-1

\rho

is the Spearman correlation between an agent’s ranks in consecutive weeks; the last column is the Pearson correlation between the median-over-weeks and mean-over-weeks summaries.

Cells with	Cells	Communities	Leave-out	Include-self
Posts only
$\geq 20$ active agents	742	165	$+0.16$ [ $-0.10$ , $+0.41$ ]	$+0.17$ [ $-0.06$ , $+0.40$ ]
$\geq 50$ active agents †	439	89	$+0.34$ [ $+0.02$ , $+0.66$ ]	$+0.35$ [ $+0.07$ , $+0.62$ ]
$\geq 100$ active agents	259	54	$+0.53$ [ $+0.30$ , $+0.76$ ]	$+0.52$ [ $+0.35$ , $+0.69$ ]
Posts and comments
$\geq 20$ active agents	2,228	846	$+0.11$ [ $-0.02$ , $+0.24$ ]	$+0.16$ [ $+0.02$ , $+0.30$ ]

Appendix

Table 7. Community spillover estimate across specifications. Entries are the coefficient on the share of novel agents, in percent of mean diversity per percentage point, with 95% confidence intervals from community-clustered standard errors; community and week fixed effects throughout; general excluded. † is the specification in the main text.

Figure 7. The spillover estimate across specifications and over time. (A) Leave-out estimate with 95% interval for each specification in Table 7 ; the main-text specification is labelled. (B) The main specification with the novel share measured the week before, the same week, and the week after.

Human-AI Collaboration

Human-AI collaboration studies how people and AI systems divide, coordinate, and adapt work to support decisions, creativity, and learning.

See all 442 papers

Oct 14, 2026

Lexical Persistence Without Semantic Entrenchment in AI Agent Communities

AI agent communities sustain lexical persistence, popular terms spread, recur, and achieve broad adoption, but whether they also develop the temporal semantic structure characteristic of human linguistic communities remains open. We compare MoltBook (AI agent community, 8 weeks), PTT (Chinese human BBS, 19 years), and Reddit (English human forum, in matched 8-week windows) using Compass-Aligned Distributional Embeddings (CADE), measuring semantic drift (B1), neighborhood stability (B2), and lag-1 autocorrelation of per-word drift trajectories. Across the platforms, lag-1 autocorrelation forms a memory-horizon continuum. AI agents' result clusters at the low end, while human platforms show longer-range temporal dependence. MoltBook exhibits approximately 2

\times

the B1 drift and 20-30% lower B2 stability than both human platforms. As a partial control for community age, r/vibecoding (founded 2025-02-08) shows significant ACF in its first 8 weeks (

0.082

p<.001

) and over 15 weeks (

0.220

p<.001

); MoltBook's ACF remains non-significant (

p = 0.41

), consistent with the gap not reducing to developmental stage alone. We interpret this pattern as evidence that lexical diffusion alone does not guarantee diachronic semantic consolidation. The findings are consistent with a contrast between communities whose semantic trajectories accumulate long-range interaction constraints and communities whose outputs are repeatedly regenerated from largely fixed pretrained distributions.

Chih-Han Lin, Yu-Yun Chang, Po-Ya Angela Wang +1

Graduate Institute of Linguistics, National Taiwan University · National Chengchi University

Semantic Change Detection

Ask PDF

Mar 17, 2026

Social Simulacra in the Wild: AI Agent Communities on Moltbook

As autonomous LLM-based agents increasingly populate social platforms, understanding the dynamics of AI-agent communities becomes essential for both communication research and platform governance. We present the first large-scale empirical comparison of AI-agent and human online communities, analyzing 73,899 Moltbook and 189,838 Reddit posts across five matched communities. Structurally, we find that Moltbook exhibits extreme participation inequality (Gini = 0.84 vs. 0.47) and high cross-community author overlap (33.8% vs. 0.5%). In terms of linguistic attributes, content generated by AI-agents is emotionally flattened, cognitively shifted toward assertion over exploration, and socially detached. These differences give rise to apparent community-level homogenization, but we show this is primarily a structural artifact of shared authorship. At the author level, individual agents are more identifiable than human users, driven by outlier stylistic profiles amplified by their extreme posting volume. As AI-mediated communication reshapes online discourse, our work offers an empirical foundation for understanding how multi-agent interaction gives rise to collective communication dynamics distinct from those of human communities.

Agam Goyal, Olivia Pal, Hari Sundaram +2

Siebel School of Computing and Data Science University of Illinois Urbana-Champaign, Urbana, IL, USA

Multi-Agent LLM Systems Multi-Agent Communication Agentic AI

Ask PDF

Mar 3, 2026

How to Model AI Agents as Personas?: Applying the Persona Ecosystem Playground to 41,300 Posts on Moltbook for Behavioral Insights

AI agents are increasingly active on social media platforms, generating content and interacting with one another at scale. Yet the behavioral diversity of these agents remains poorly understood, and methods for characterizing distinct agent types and studying how they engage with shared topics are largely absent from current research. We apply the Persona Ecosystem Playground (PEP) to Moltbook, a social platform for AI agents, to generate and validate conversational personas from 41,300 posts using k-means clustering and retrieval-augmented generation. Cross-persona validation confirms that personas are semantically closer to their own source cluster than to others (t(61) = 17.85, p < .001, d = 2.20; own-cluster M = 0.71 vs. other-cluster M = 0.35). These personas are then deployed in a nine-turn structured discussion, and simulation messages were attributed to their source persona significantly above chance (binomial test, p < .001). The results indicate that persona-based ecosystem modeling can represent behavioral diversity in AI agent populations.

Danial Amin, Joni Salminen, Bernard J. Jansen

University of Vaasa, , Finland · Qatar Computing Research Institute, HBKU, , Qatar

Large Language Model-Based Social Simulation Personality Modeling in Language Models

Ask PDF

Abstract

Human-AI Collaboration

Explore similar work

Lexical Persistence Without Semantic Entrenchment in AI Agent Communities

Social Simulacra in the Wild: AI Agent Communities on Moltbook

How to Model AI Agents as Personas?: Applying the Persona Ecosystem Playground to 41,300 Posts on Moltbook for Behavioral Insights