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arxiv: 2606.12748 · v1 · pith:H3RFQ7P2new · submitted 2026-06-10 · 💻 cs.CL

Agent-based models for the evolution of morphological alternation patterns

Aravinth Kulanthaivelu , Richard Sproat This is my paper

Pith reviewed 2026-06-27 09:26 UTC · model grok-4.3

classification 💻 cs.CL
keywords agent-based simulationmorphological alternationlanguage evolutionsocial networkslarge language modelshistorical linguistics
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The pith

Multi-agent simulations show scale-free networks and Bernoulli adoption produce more plausible morphologies

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

The paper develops multi-agent simulations in which alternate word forms spread through populations via phonological changes or lexical alternatives, leading to entrenched stem and inflectional alternations. Agents adopt novel forms with varying probabilities and can extend them to related paradigm slots, using realistic lexicons and phonological rules on populations of tens to hundreds of agents. To assess realism, the authors introduce the AI Historical Linguist, an LLM system that simulates debates between two historical linguists to score real, disguised, and simulated morphologies. Results indicate that scale-free social networks combined with random Bernoulli adoption yield morphologies judged more plausible than those from other topologies or policies. The work also runs case studies on attested historical changes to explore alternative outcomes.

Core claim

In these simulations, scale-free social networks and random Bernoulli adoption of forms generate morphologies that the AI Historical Linguist rates as more plausible than those arising under other network structures or adoption rules, when benchmarked against real language morphologies and disguised controls.

What carries the argument

Multi-agent simulation supporting naturalistic lexical forms, phonological rules, multiple network topologies, diffusion patterns, and adoption policies, evaluated by the AI Historical Linguist LLM debate system.

If this is right

  • Scale-free networks favor more plausible morphologies than other topologies.
  • Random Bernoulli adoption favors more plausible morphologies than other policies.
  • The simulation framework can reproduce specific attested historical alternations.
  • Altering network or adoption parameters allows testing of counterfactual historical scenarios.

Where Pith is reading between the lines

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

  • Social network structure may influence which irregular forms persist over time in actual languages.
  • LLM debate evaluation could be applied to test plausibility of other simulated language changes.
  • Random adoption policies might better capture diffusion processes observed in real populations.

Load-bearing premise

The AI Historical Linguist system gives a valid and unbiased measure of morphological plausibility through simulated linguist debates.

What would settle it

If the AI Historical Linguist assigns equivalent or lower plausibility scores to morphologies from scale-free networks with Bernoulli adoption than to those from other conditions when compared against real morphologies.

Figures

Figures reproduced from arXiv: 2606.12748 by Aravinth Kulanthaivelu, Richard Sproat.

Figure 1
Figure 1. Figure 1: Schematic of within-step adoption. During one communication round, an agent hears forms from neighboring agents and may replace its current entry for the relevant lexeme-slot cell. In (a) the main adoption mechanism is Bernoulli adoption, optionally augmented with mild biasing terms such as novelty weighting, while (b) showcases the stem-as-target setting, which replaces not only the specific slot but all … view at source ↗
Figure 2
Figure 2. Figure 2: One simulation time step. Optional exogenous events are applied first, after which agents communicate on the graph and adopt heard forms. Compression is then applied synchronously, and the resulting state is recorded for later analysis. Each time step proceeds as follows: 1. Optional exogenous events are applied. These may alter the graph or change the forms held by a subset of agents, allowing us to model… view at source ↗
Figure 3
Figure 3. Figure 3: Rate of suppletion, number of stem alternation signatures, and number of paradigms for the distinct experiment configurations at the terminal state. June 10, 2026 17/51 [PITH_FULL_IMAGE:figures/full_fig_p017_3.png] view at source ↗
Figure 4
Figure 4. Figure 4: Signature-count summaries for the population-splitting intervention. The fixed population is divided into non-interacting components, changing the global interaction structure rather than only the local transmission parameters [PITH_FULL_IMAGE:figures/full_fig_p018_4.png] view at source ↗
Figure 5
Figure 5. Figure 5: Signature-count summaries for the cascade-targeting intervention. Cascades are applied to the whole population or to subsets selected at random or by connectivity degree. June 10, 2026 18/51 [PITH_FULL_IMAGE:figures/full_fig_p018_5.png] view at source ↗
Figure 6
Figure 6. Figure 6: Heatmap displaying the mean normalized impact relative to the baseline of each factor for various summary statistics. June 10, 2026 19/51 [PITH_FULL_IMAGE:figures/full_fig_p019_6.png] view at source ↗
Figure 7
Figure 7. Figure 7: Sample prompt for the AI Historical Linguist with an artificially evolved language. paradigms having only one example shown. • Patterns of stem alternation: The stem alternations present serious issues: – There’s a consistent pattern of accent shift (single to double accent marks: ’ to ”) in positions 4-5 across most paradigms, which is phonologically plausible – However, the extreme suppletion is highly p… view at source ↗
Figure 8
Figure 8. Figure 8: Top panel: Bar plot showing the mean scores assigned by the assessor and critic over ten runs of the AI Historical Linguist. Here the assessor is Gemini-2.5-Pro and the critic is Claude-Sonnet-4-20250514. The morphologies evaluated are for Estonian, Finnish, French, Hungarian, Italian and Latin in both real and disguised versions; and six sample artificial languages from the set evaluated more fully in the… view at source ↗
Figure 9
Figure 9. Figure 9: Relationship between fitted rank-frequency slope and AIHL plausibility for the main AIHL-scored experiment sweep. Each point represents an experimental condition, colored by the parameter family varied relative to the baseline. The horizontal axis gives the fitted power-law exponent for the terminal stem-alternation signature distribution, while the vertical axis gives the mean AIHL plausibility score. The… view at source ↗
Figure 10
Figure 10. Figure 10: AIHL plausibility scores by experimental condition. Bars show mean final score across AIHL evaluations, error bars show standard deviation, and points show individual runs. Higher scores indicate greater judged morphological plausibility. June 10, 2026 29/51 [PITH_FULL_IMAGE:figures/full_fig_p029_10.png] view at source ↗
Figure 11
Figure 11. Figure 11: AIHL plausibility scores by experimental condition, ordered by decreasing AIHL score. Bars show mean final score across AIHL evaluations, error bars show standard deviation, and points show individual runs. Higher scores indicate greater judged morphological plausibility. June 10, 2026 30/51 [PITH_FULL_IMAGE:figures/full_fig_p030_11.png] view at source ↗
Figure 12
Figure 12. Figure 12: AIHL plausibility scores for individual components in the population-split simulations. Bars show mean final score across AIHL evaluations, error bars show standard deviation, and points show individual runs. June 10, 2026 31/51 [PITH_FULL_IMAGE:figures/full_fig_p031_12.png] view at source ↗
Figure 13
Figure 13. Figure 13: AIHL plausibility scores for cascade-targeting conditions. Bars show mean final score across AIHL evaluations, error bars show standard deviation, and points show individual runs. June 10, 2026 32/51 [PITH_FULL_IMAGE:figures/full_fig_p032_13.png] view at source ↗
Figure 14
Figure 14. Figure 14: Numbers of lexemes in a given category and language that show suppletive stem alternations from the Surrey Suppletion Database [225]. ‘receive’. So systems with ten or more lexemes in a given category showing suppletion are not unheard of, though relatively rare. Honorific systems are particularly ripe for lexical substitutions. Javanese, for example, is famous for its multiple speech levels. Alongside th… view at source ↗
Figure 15
Figure 15. Figure 15: The three adoption methods applied to a common inbox for a single focal cell. Bernoulli treats each received token as an independent coin flip and may leave the cell unchanged; degree-scaled biases adoption toward high-degree senders; cumulative aggregates per-form counts across the inbox and deterministically adopts the most heard form. June 10, 2026 2/37 [PITH_FULL_IMAGE:figures/full_fig_p053_15.png] view at source ↗
Figure 16
Figure 16. Figure 16: The three adoption-target modes applied to the same heard token. The default mode updates only the heard lexeme-slot cell; stem-as-target additionally propagates the heard stem across every slot of the target lexeme’s paradigm; affix-as-target propagates the heard affix to every cell in the lexicon that previously carried the listener’s old affix. June 10, 2026 3/37 [PITH_FULL_IMAGE:figures/full_fig_p054… view at source ↗
Figure 17
Figure 17. Figure 17: The three compression policies applied to a shared population of seven stem-alternation signatures. No compression leaves the signatures untouched; class consensus snaps minority signatures within an alternant-set class to the modal pattern, preserving the alternant count; global drift snaps each signature to a dominant nearby one and can drop alternants altogether. June 10, 2026 5/37 [PITH_FULL_IMAGE:fi… view at source ↗
Figure 18
Figure 18. Figure 18: Decision-tree compression illustrated on a Korean nominative example. A classifier is fit to (environment, affix) pairs in the agent’s lexicon and applied back, learning that vowel-final stems take -ka and consonant-final stems take -i, and rewriting the outlier accordingly. June 10, 2026 6/37 [PITH_FULL_IMAGE:figures/full_fig_p057_18.png] view at source ↗
Figure 19
Figure 19. Figure 19: Representative social graph topologies used in the model: complete, Erdős–Rényi, scale-free, and log-normal. The complete graph maximizes connectivity; the Erdős–Rényi graph provides a homogeneous random baseline; the scale-free graph introduces hubs through preferential attachment; and the log-normal graph produces heterogeneous degree structure without the same hub dominance. Social graphs The social st… view at source ↗
Figure 20
Figure 20. Figure 20: Example of a split and merge operation applied successively to a single population. Under a split instance the population is split into a set of connected components, the number of which is specified by the user. Under a merge operation disjoint components are merged by adding edges at random between the components. June 10, 2026 10/37 [PITH_FULL_IMAGE:figures/full_fig_p061_20.png] view at source ↗
Figure 21
Figure 21. Figure 21: Birth/death turnover under the two persistence regimes. In the persistent regime each learner inherits its mentor’s social position and the graph topology is preserved across generations; in the non-persistent regime the edges within each connected component are re-sampled, shown here by new solid edges in the post-turnover graph and the previous edges drawn as faded dashed lines. June 10, 2026 11/37 [PI… view at source ↗
Figure 22
Figure 22. Figure 22: Signature histogram figures for the rule-based and LLM-based segmentation outputs. June 10, 2026 18/37 [PITH_FULL_IMAGE:figures/full_fig_p069_22.png] view at source ↗
Figure 23
Figure 23. Figure 23: A run of the simulation of the change from the nominative marker -i to -ka after vowels in early Modern Korean. The population has 50 agents, exchanging 400 utterances at each tick. The probability of adoption is set to 0.1, and the probability of the initial change from -i to -ka after nouns ending in /i/ is also 0.1. The novelty parameter is 0.6. shows decision trees at two stages, the first from shortl… view at source ↗
Figure 24
Figure 24. Figure 24: Decision trees for an agent at step 420 (left) and 820 (right) for the simulation presented in [PITH_FULL_IMAGE:figures/full_fig_p072_24.png] view at source ↗
Figure 25
Figure 25. Figure 25: A run of the simulation of the change from the nominative marker -i to -ka after vowels in early Modern Korean. The population has 50 agents, exchanging 400 utterances at each tick. The probability of adoption is set to 0.1, and the probability of the initial change from -i to -ka after nouns ending in /i/ is also 0.1. Here the novelty parameter is 0.2, and rather than spreading the system collapses. June… view at source ↗
Figure 26
Figure 26. Figure 26: Evolution of forms of ambulāre ‘go’, barrıre ‘trumpet’, disquırıre ‘discourse’, farcıre ‘force’, flōrıre ‘flower’. Each row corresponds to the evolution of the prevalence of a particular pattern for the verb. The left panel represents the period prior to the dialect split, and the right two panels represent the ‘French’ and ‘Italian’ branches respectively. The curve corresponding to the historically corre… view at source ↗
Figure 27
Figure 27. Figure 27: Continuation of [PITH_FULL_IMAGE:figures/full_fig_p079_27.png] view at source ↗
Figure 28
Figure 28. Figure 28: Continuation of [PITH_FULL_IMAGE:figures/full_fig_p080_28.png] view at source ↗
Figure 29
Figure 29. Figure 29: Evolution of Lenition from Proto-Celtic to the two Insular Celtic branches for the case where the examples are balanced to roughly represent the proportion of vowel and consonant stems in Proto-Celtic. The number of agents is 50. Probability of adoption is 0.2, novelty is 0.6, and probability of mutation is 0.2. Mutation rules were introduced to the 20% of agents with the highest degrees of connectivity. … view at source ↗
Figure 30
Figure 30. Figure 30: Evolution of Lenition from Proto-Celtic to the two Insular Celtic branches for the case where the examples are biased towards having as many consonant stems as vowel stems. The same settings were used as in [PITH_FULL_IMAGE:figures/full_fig_p086_30.png] view at source ↗
Figure 31
Figure 31. Figure 31: Decision tree for one of the Goidelic-speaking agents at tick 1000 for the balanced high-degree case. For masculine-singular and feminine-plural nouns, the tree predicts no lenition, whereas the other two classes induce lenition. June 10, 2026 36/37 [PITH_FULL_IMAGE:figures/full_fig_p087_31.png] view at source ↗
read the original abstract

Why is the past of English "go" the apparently unrelated "went"? Such alternations are frequent in languages. They neither aid communication nor learnability, yet they can be persistent, surviving over centuries or millennia. We present a multi-agent simulation of the emergence of morphological stem and inflection alternations. Alternate forms arise by phonological changes or, as with "go/went", from lexical alternatives associated with a subset of the population. When an agent 'hears' another agent use a novel form for a slot in the paradigm of a word (say, the past tense of go), they will with some probability adopt that form, possibly spreading its use to other slots in the paradigm that shared the same original form. Thus alternative forms can spread through the population and become entrenched as stem or inflectional marker alternants. Unlike many previous computational studies, our system allows for naturalistic lexical forms, realistic phonological rules, lexicons with hundreds or thousands of entries, and agent populations in the tens or hundreds. It supports several network topologies, diffusion patterns and agent adoption policies. One issue with such simulations is evaluation: how realistic is the resulting morphology compared to those of real languages? We introduce the AI Historical Linguist, a novel Large Language Model-driven system that models a debate between two historical linguists. We use this to compare a set of real language morphologies, disguised morphologies, and experimentally evolved morphologies. The results suggest that among the factors that favor more plausible morphologies are scale-free social networks and random Bernoulli adoption of forms. We also present three case studies modeling attested historical changes, allowing us to test what might have happened if history had been different. All code and data are released.

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

1 major / 1 minor

Summary. The paper presents a multi-agent simulation of morphological alternation emergence, incorporating phonological changes and lexical alternatives that spread via adoption probabilities across agent populations and network topologies. It introduces the AI Historical Linguist—an LLM system simulating debates between historical linguists—to evaluate plausibility of simulated morphologies against real and disguised language data. Results indicate scale-free networks and Bernoulli adoption yield higher plausibility scores; three case studies of attested changes are included, with code and data released.

Significance. If the evaluation holds, the work provides a scalable framework for testing social and diffusion factors in morphological evolution, with strengths in handling realistic lexicons, phonology, and large populations. The open release of code and data supports reproducibility, and the counterfactual case studies offer a falsifiable modeling approach. The LLM-based judge is a novel but unvalidated component.

major comments (1)
  1. [Evaluation section / abstract] The central claim that scale-free networks and Bernoulli adoption produce more plausible morphologies (abstract) depends entirely on plausibility rankings from the AI Historical Linguist. No validation is reported—such as correlation with human expert ratings, inter-annotator agreement, prompt ablations, or checks for training-data leakage—which is load-bearing because systematic LLM biases (e.g., favoring attested alternation types) could invert the reported ranking of network topologies and adoption rules.
minor comments (1)
  1. [Abstract] The abstract refers to 'disguised morphologies' without defining the disguise procedure or how they control for surface similarity, which would clarify the evaluation baseline.

Simulated Author's Rebuttal

1 responses · 0 unresolved

We thank the referee for the constructive feedback, particularly on the need to validate the AI Historical Linguist. We agree this is essential for supporting the central claims and will revise accordingly.

read point-by-point responses

  1. Referee: [Evaluation section / abstract] The central claim that scale-free networks and Bernoulli adoption produce more plausible morphologies (abstract) depends entirely on plausibility rankings from the AI Historical Linguist. No validation is reported—such as correlation with human expert ratings, inter-annotator agreement, prompt ablations, or checks for training-data leakage—which is load-bearing because systematic LLM biases (e.g., favoring attested alternation types) could invert the reported ranking of network topologies and adoption rules.

    Authors: We agree that the absence of validation for the AI Historical Linguist represents a genuine limitation, as unexamined biases could indeed affect the topology and adoption rule rankings. In revision we will add prompt ablations testing debate format variations, training-data leakage checks via held-out languages and rare alternation patterns, and inter-annotator agreement metrics across repeated LLM debates. We will also include a small-scale human expert rating study on a subset of outputs to provide initial correlation evidence. These steps directly address the load-bearing concern without overclaiming the current results. revision: yes

Circularity Check

0 steps flagged

No significant circularity; derivation self-contained against external benchmarks

full rationale

The paper simulates morphological evolution via agent-based rules on networks and adoption policies, then evaluates outputs by comparing LLM-assigned plausibility scores against real-language morphologies and disguised controls. No quoted equations, parameters, or steps reduce the reported ranking of network topologies or adoption rules to a fit, self-definition, or self-citation chain. The AI Historical Linguist is introduced as an external judge rather than derived from the simulation results themselves. Evaluation therefore rests on independent real-language data rather than internal construction.

Axiom & Free-Parameter Ledger

1 free parameters · 1 axioms · 0 invented entities

Ledger constructed from abstract only; full implementation details unavailable.

free parameters (1)
  • adoption probability
    Agents adopt novel forms 'with some probability'; this parameter controls diffusion but no specific value or fitting procedure is given in the abstract.
axioms (1)
  • domain assumption When an agent hears a novel form for a paradigm slot, it may adopt it and spread the form to other slots sharing the original stem.
    This mechanism is presented as the core process allowing alternations to entrench.

pith-pipeline@v0.9.1-grok · 5839 in / 1251 out tokens · 26929 ms · 2026-06-27T09:26:21.943421+00:00 · methodology

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Reference graph

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