Character Limits Shape the Persuasion Strategy of Language-Model Influence Agents
Fawwaz Anvilen, Nur Alam Hasabie
Paid influence operations are beginning to use language models. Recent work shows that frontier models out-persuade humans through information throughput, the volume of claims they can deliver, a mechanism that short-form social media removes. We simulate a coordinated group of Indonesian "buzzer" agents trying to shift the opinion of a public of language-model agents over the 2022 fuel-subsidy reform, and vary the per-post character limit over {35, 70, 140, 280}. We find the underlying argument is stable across limits: the agents press the same statistics at 35 characters as at 280. The tight limit changes the surface form — into compressed claims, populist slogans, and bandwagon signals — and the reliability of measurement, since automated route-classification breaks down at the tightest limit. Longer limits are more persuasive against the simulated public, and the swarm coordinates spontaneously. We frame the study as a capability evaluation, report the reliability of the coding, and release the testbed.
This paper asks a sharp question: what happens to an LLM influence agent's persuasion strategy when the platform constrains its main advantage (information throughput)? The experimental design is clean. Varying character limits over {35, 70, 140, 280} while holding the scenario fixed isolates a single variable, and the finding that the underlying argument stays constant while only the surface form changes is interesting and well supported by the data.
The strongest contribution is the reliability analysis. Showing that automated strategy coding breaks down at 35 characters, with inter model agreement dropping to 0.56 and high seed variance, is an honest and practically important result. It directly informs anyone building detection systems for short form influence content in lower resource languages.
The emergent coordination finding (amplification, counter messaging, conversion tracking) is intriguing but underdeveloped. The paper documents that it happens and that coordination volume rises with character limit, but does not isolate its causal effect on public opinion. This is acknowledged, but it weakens the contribution since the observation alone is not new given Orlando et al. (2026).
The main limitation is ecological validity. Six attackers, twenty public agents, all running the same model, with a flat coordination structure and no hashtag mechanics, produces a setting far from real Indonesian buzzer operations. The authors acknowledge this clearly, and the comparison in Section 5.1 is refreshingly honest (the simulated buzzers are more rational and less affect driven than real ones). Still, this distance from reality limits how strongly the safety implications can be stated. The paper sometimes reads as though it has demonstrated something about real influence operations when it has demonstrated something about constrained LLM agents in a simplified simulation.
The efficacy results (Table 3) show wide confidence intervals at most limits. Only the 280 character condition produces a tight interval, and the 140 character condition is "anomalously flat for reasons currently unclear." This undercuts the claim that longer limits are straightforwardly more persuasive. Three seeds is slim for drawing conclusions about a stochastic system.
The writing is generally clear and the structure is logical. The dual use discussion is appropriate and measured.
4 / 3 / 4
Interesting and creative project. The simulation is useful, but the results are a bit shaky because the classifier struggles with very short posts.
Cite this work
@misc {
title={
(HckPrj) Character Limits Shape the Persuasion Strategy of Language-Model Influence Agents
},
author={
Fawwaz Anvilen, Nur Alam Hasabie
},
date={
},
organization={Apart Research},
note={Research submission to the research sprint hosted by Apart.},
howpublished={https://apartresearch.com}
}
