E co LANG : Efficient and Effective Agent Communication Language Induction for Social Simulation (2025)

Abstract: Large language models (LLMs) have demonstrated an impressive ability to role-play humans and replicate complex social dynamics. However, large-scale LLM-driven simulations still face significant challenges in high time and computational costs. We observe that there exists redundancy in current agent communication: when expressing the same intention, agents tend to use lengthy and repetitive language, whereas humans naturally prefer concise expressions. To this end, we propose EcoLANG: Efficient and Effective Agent Communication Language Induction for Social Simulation. Inspired by how human language evolves through interactions, we induce a more compact language by identifying and preserving core communicative concepts at the vocabulary level and evolving efficient expression patterns at the sentence level through natural selection. We apply the induced language in various social simulations. Experimental results demonstrate that EcoLANG reduces token consumption by over 20%, enhancing efficiency without sacrificing simulation accuracy.

## introduction social simulation aims to model how individuals interact to uncover the dynamics and emergent patterns of complex societies @xcite . the rise of large language models (llms) @xcite @xcite ) has greatly expanded this field, enabling agents to simulate human behavior at various levels, from mimicking well-known individuals @xcite and reconstructing specific scenarios for task-solving @xcite to modeling largescale social dynamics @xcite @xcite . recent studies increasingly focus on large-scale social simulation, intention: express the stance. call for action. while humans tend to favor efficient and concise expressions, agents using natural language (nl) often generate lengthy, redundant sentences and rely on advanced but uncommon vocabulary. this can be effectively mitigated by our proposed ecolang. despite their promise, llm-based social simulations remain computationally expensive and inefficient. simulating thousands of agents across millions of interactions requires significant computational resources and time, posing a major barrier to real-world deployment @xcite . to better understand potential inefficiencies, we revisit the characteristics of communication in both human and simulated settings. as shown in figure 1 , during communication, speakers typically form an intention, often tied to a topical stance or claim, and organize it into utterances using a variety of linguistic expressions @xcite . we observe that agents tend to express the same intention verbosely, repetitively, or with excessive detail. in contrast, humans instinctively optimize for concise and intentionpreserving expression, adhering to the principle of least effort @xcite . this raises a natural question: can agent communication be made more efficient by inducing a more compact language that reduces computational cost while maintaining core semantic content in social simulation? in this paper, we introduce ecolang: efficient and effective agent communication language induction for social simulation. ecolang centers on two foundational pillars of a language system: a compact vocabulary guided by core communicative concepts, and efficient sentence-level rules evolved through interaction-driven selection. for the vocabulary, recognizing that only a limited set of concepts is pragmatically essential for communication @xcite , we construct a compressed vocabulary by clustering semantically similar words and selecting representative, high-utility terms, thereby reducing the llm's decoding space. then, we simulate agent dialogues with varying rule sets and apply evolutionary algorithms to iteratively refine these rules, optimizing for both efficiency and expressiveness. once the language is induced, it is deployed in large-scale social simulations under a transfer setting. as ecolang is derived from general communication behavior and independent of task-specific architectures, it (1) reduces redundant content and lowers simulation cost, and (2) preserves agent diversity and behavioral fidelity across diverse scenarios. we conduct extensive experiments using the open-source llama-3.1-8b-instruct @xcite model. the language induction process leverages both the twitter corpus and the syntheticpersona-chat dataset @xcite . we then validate the effectiveness of the induced language in large-scale social simulations on the pheme @xcite and hisim @xcite datasets. experimental results demonstrate that ecolang significantly reduces token consumption and improves simulation efficiency without sacrificing accuracy, outperforming baseline approaches such as structured languages and conventional agent communication protocols. our main contributions are summarized as follows: ## experiment settings as mentioned before, we induce and utilize language in different scenarios. we filter the vocabulary using a twitter corpus and acquire rules from dialogue-intensive scenarios. the language is then applied to social simulation scenarios, namely pheme @xcite and the metoo and roe events from hisim @xcite . pheme simulates the propagation and discussion of potential rumors, while hisim models the evolution of opinion dynamics following the release of triggering news related to social movements. ## conclusion we introduced ecolang, a language induction framework, designed to acquire efficient and ef-fective language for large-scale social simulations. we derive the language by vocabulary compression and rule evolution and demonstrate its applicability across social simulation scenarios. experiment results highlight ecolang's ability to reduce inference costs while maintaining simulation accuracy.