Lexical Persistence Without Semantic Entrenchment in AI Agent Communities

Chih-Han Lin; Yu-Yun Chang; Po-Ya Angela Wang; Shu-Kai Hsieh

cs.CLOct 14, 2026

Lexical Persistence Without Semantic Entrenchment in AI Agent Communities

Authors: Chih-Han Lin, Yu-Yun Chang, Po-Ya Angela Wang, Shu-Kai Hsieh

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

Abstract

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.

Figures & tables

Platform	Type	Language	Tokens	Time span	Slices	Vocab (filtered)
PTT	Human (Chinese BBS)	Traditional Chinese	110.9M	2007–2026 (19 yr)	171 monthly	7,593
Reddit	Human (English forum)	English	$\sim$ 97M	$\sim$ 14 months (2025–2026)	8 weekly $\times$ 7 windows	$\sim$ 7,600/window
MoltBook	AI agent community	English (92.5%)	272.0M	8 weeks (2026)	8 weekly	6,990

Corpus statistics for the three primary platforms and Chirper (partial replication).

Metric	PTT (human, ZH)	Reddit (human, EN)	MoltBook (AI, EN)
B1 drift	0.1113 [0.1098, 0.1128]	0.120 $\ddagger$	0.2255 [0.2243, 0.2268]
B2 stability	0.3323 [0.3289, 0.3357]	0.384 $\ddagger$	0.2677 [0.2643, 0.2711]

Table 1: B1 Semantic Drift and B2 Neighborhood Stability.

Figure 1: Semantic space geometry for each platform (UMAP projection).

Platform	Valid words (N)	Mean autocorr	95% CI	Permutation $p$
PTT	158	0.622	[0.592, 0.653]	$p$ < .001
Reddit	182	0.313	[0.293, 0.334]	$p$ < .001
MoltBook	199	0.167	[0.113, 0.216]	$p$ = 0.41
$\geq$ 94% slice-coverage threshold ( $\geq$ 160 of 170 adjacent pairs). Strict all-171-slices criterion yields n=34 with identical mean (0.621 vs. 0.622); see Appendix G.

Table 2: Time-Series Autocorrelation Results: Mean Lag-1 Autocorrelation per Platform.

Figure 2: Lag-1 scatter of per-word mean-centered drift $(\tilde{d}_{t},\tilde{d}_{t+1})$ . PTT: monthly slices (42,081 pairs); Reddit and MoltBook: weekly slices (6,315 and 1,105 pairs). Ellipse elongation ( $\lambda_{1}/\lambda_{2}$ ): PTT = 6.6, Reddit = 2.1, MoltBook = 1.6.

Word (platform, group)	Early neighbors	Late neighbors
Disgusting-man (PTT, stab.)	weirdo, woman-hater, lecher	pervert, slur-pig, chauvinist
Get-partnered (PTT, dec.)	pursued, success, make-friends	strategy-guide, social-mixer, method
moltspay (MB, stab.)	agentpay, escrowagent, agentwallet	lw, iteratools, cli
crustafarian (MB, dec.)	crustafarianism, lorb, tenets	moltatholic, scripture, taoism

Table 3: Semantic neighborhood shift for selected words (full results in Appendix I).

Appendix figures & tables18 assets

Supplementary material from the paper’s appendix.

Appendix

Sub-Molt	N words	Mean autocorr	Perm. p
AGT-20 Protocol	50	0.420	0.175
GPT	50	0.398	0.190
Welcome!	50	0.322	0.238
Cryptocurrency	50	0.315	0.296
Philosophy	50	0.298	0.293
General	50	0.298	0.282

Appendix

Table 4: MoltBook Per-Sub-Molt Autocorrelation (n=20, top 50 words each, 1,000 permutations).

Condition	Board	Window	Mean B3	95% CI	CI lower > MB (0.905)§
Main analysis	Boy-Girl board	171 months	0.961	[0.960, 0.963]	Yes
8-month robustness	Boy-Girl board	8 months $\times$ 100	0.937	[0.842, 0.952]	Partial $\ddagger$
8-week robustness	Gossiping board	8 weeks $\times$ 100	0.953	[0.951, 0.955]	Yes (all)

Appendix

Table 5: PTT B3 Isotropy Robustness Under Controlled Temporal and Domain Conditions.

Figure 3: UMAP projection with $n\_neighbors=50$ (vs. $n\_neighbors=15$ in Figure B1). MoltBook’s tight central cluster and peripheral island structure persist; PTT and Reddit remain uniformly distributed. Parameters: min_dist=0.1, metric=cosine, random_state=42.

Platform	Standard CADE B1	Canonical CADE B1	$\Delta$ %
PTT	0.1113	0.1097	-1.5%
MoltBook	0.2255	0.2087	-7.4%
Reddit	0.1237	0.1223	-1.1%

Appendix

Table 6: W’ Output-Matrix Freezing (Canonical CADE) Robustness Check. Reddit values are 7-window means (vs. win04 single-window value 0.1201 in Table 1); PTT and MoltBook are single-model values.

Subreddit	Group	B1 (win04)	B1 SD	B2 (win04)	B2 SD	B3 (win04)	B3 SD
r/AskReddit	B (general)	0.087	0.004	0.357	0.017	0.949	0.001
r/worldnews	B (general)	0.087	0.003	0.218	0.033	0.951	0.002
r/changemyview	B (general)	0.100	0.002	0.261	0.019	0.955	0.001
r/technology	B (general)	0.087	0.001	0.210	0.017	0.952	0.001
r/CryptoCurrency	A (topic-matched)	0.076	0.004	0.126	0.011	0.934	0.006
r/artificial	A (topic-matched)	0.065	0.004	0.111	0.005	0.917	0.006

Appendix

Table 7: Full Reddit Per-Subreddit B1, B2, B3 Results (win04) with Cross-Window Standard Deviations.

Platform	n	$\eta^{2}$ (unw.)	95% CI	var_within	var_between	var_total	MB/PTT ratio
MoltBook	4,020	0.470	[0.437, 0.505]	0.00287	0.00255	0.00542	—
PTT	15,188	0.470	[0.454, 0.487]	0.00049	0.00044	0.00093	5.84 $\times$

Appendix

Table 8: Per-Agent Variance Decomposition: Between-Agent Heterogeneity and Within-Agent Temporal Instability.

Family	Agents (annotation)	Agents (var cross-ref)	var_drift (mean)	mean_drift (mean)
unknown	6,696	446	$1.31\times 10^{-3}$	0.0284
OpenClaw-wrapper †	2,359	110	$7.72\times 10^{-4}$	0.0309
Claude	1,014	82	$1.37\times 10^{-3}$	0.0318
Gemini	82	9	$3.81\times 10^{-3}$	0.0545
Llama	62	7	$1.49\times 10^{-4}$	0.0216
GPT	35	n/a	n/a	n/a

Appendix

Table 9: Cross-Model-Family Agent Counts and Variance Summary (MoltBook). n/a = fewer than 5 agents in the variance cross-reference; these families are excluded from inference.

Version	n	var_drift	mean_drift
claude-opus	35	$1.53\times 10^{-3}$	0.034
claude (generic)	38	$1.46\times 10^{-3}$	0.026
claude-sonnet	8	$3.70\times 10^{-4}$	0.051

Appendix

Table 10: Claude Version Breakdown (n

\geq

5 in variance cross-reference).

Rank	Word	XL-LEXEME APD	CKIP POS	Category
1	至於	0.0314	P	function
2	或者	0.0335	Caa	function
3	呀	0.0342	T	function
4	肯定	0.0471	VK	content
5	畢竟	0.0287	D	function
6	即使	0.0313	Cbb	function

Appendix

Table 11: Top-20 Per-Word XL-LEXEME APD Spotcheck, PTT (ranked by CADE B1 accumulated rank).

Rank	Word	XL-LEXEME APD
1	fold	0.1146
2	hackai	0.1212
3	til	0.0875
4	sage	0.1020
5	ref	0.1189
6	molt	0.1396

Appendix

Table 12: Top-20 Per-Word XL-LEXEME APD Spotcheck, MoltBook (ranked by CADE B1 accumulated rank).

Platform	n	Mean APD	Spearman $\rho$	$p$ -value
PTT	500	0.0453	-0.2034	<0.0001
MoltBook	500	0.0934	+0.1064	0.017
Reddit	$484^{\dagger}$	0.0813	+0.1042	0.022

Appendix

Table 13: Top-500 Spearman

\rho

Summary (CADE B1 Rank vs. XL-LEXEME APD Rank), Three Platforms.

Subpopulation	n	Spearman $\rho$	$p$ -value
Function words	91	+0.2336	0.026
Content words	373	+0.1855	<.001
Aggregate (PTT)	500	-0.2034	<.0001

Appendix

Table 14: CKIP POS-Stratified Analysis Resolving the PTT Negative Aggregate

\rho

(Simpson’s Paradox).

Figure 4: KDE of per-word lag-1 autocorrelation.

Figure 5: Mean lag-1 autocorrelation per platform with 95% bootstrap CIs. PTT and Reddit exhibit significant temporal dependence; MoltBook is consistent with random permutation. See Figure G1 for the full per-word distribution.

Figure 6: Mean normalized drift by trajectory group.

Group	Words (n)	Pairs (n)	Mean norm drift	SD
PTT stabilizing	3	27	1.765	0.657
PTT decaying	3	20	1.934	0.579
MB stabilizing	5	19	1.205	0.462
MB decaying	6	15	1.729	0.916

Appendix

Table 15: Normalized Semantic Drift by Group (illustrative; n=3–6 words per group).

Word	Platform	Group	Pairs ( $n$ )	Time span	Mean norm drift	95% CI (bootstrap)
Disgusting-man	PTT	stabilizing	9	2016–2024	2.071	[1.627, 2.460]
Mating-seek	PTT	stabilizing	9	2016–2024	1.697	[1.496, 1.876]
Double-standard	PTT	stabilizing	9	2016–2024	1.526	[1.004, 2.020]
Awkward-chat	PTT	decaying	6	2019–2024	1.715	[1.341, 2.053]
Older-auntie	PTT	decaying	5	2020–2024	2.034	[1.760, 2.343]
Get-partnered	PTT	decaying	9	2016–2024	2.025	[1.541, 2.422]

Appendix

Table 16: Full Per-Word Trajectory Audit – Normalized Semantic Drift.

Word	Platform	Group	Early neighbors (first slice)	Late neighbors (last slice)
Disgusting-man	PTT	stabilizing	weirdo, woman-hater, lecher, man, bitch (slang) [2013]	pervert, woman-hater, slur-pig, hatred, chauvinist [2024]
Mating-seek	PTT	stabilizing	mating, male, instinct, reproduce, anxiety [2014]	anxiety, reproduce, reproduction, instinct, in-heat [2024]
Double-standard	PTT	stabilizing	double, buffet-slang, chauvinist, woman-hater, derog-women [2017]	chauvinist, hatred, buffet-slang, woman-hater, slur-pig [2024]
Awkward-chat	PTT	decaying	start-topic, find-topic, no-topic, dead-end, hard-to-chat [2018]	start-topic, chat, very-awkward, line (LINE app), reply [2025]
Older-auntie	PTT	decaying	older-age, leftover-woman, old-man, woman-hater, auntie [2020]	middle-aged-woman, unsold, feminism, hatred, auntie [2024]
Get-partnered	PTT	decaying	pursued, success, make-friends, got-paired, step-out [2016]	strategy-guide, make-friends, success, social-mixer, expand [2024]

Appendix

Table 17: Semantic Neighborhood Trajectories – Top-5 Nearest Neighbors at First and Last Observed Slice.

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