MARIO -0.5 B : A Multi-Agent Lightweight Model for Real-Time Open Information Extraction in Low-Resource Settings (2025)

Abstract: Large language models (LLMs) have shown remarkable capabilities in open information extraction. However, their substantial resource requirements often restrict their deployment in resource-constrained industrial settings, particularly on edge devices. The high computational demands also lead to increased latency, making them difficult to apply in real-time applications. In this paper, we introduce MARIO-0.5B, an ultra-lightweight model trained on instruction-based samples in Chinese, English, Korean, and Russian. We also present a novel multi-agent framework, SMOIE, which integrates schema mining, information extraction, reasoning, and decision-making to effectively support MARIO-0.5B.The experimental results show that our framework outperforms large-scale models with up to 70B parameters, reducing computational resources by 140x and delivering 11x faster response times. Moreover, it operates efficiently in CPU-only environments, which makes it well-suited for widespread industrial deployment.

## introduction open information extraction (oie) is the task of extracting triples @xcite from given text without a fixed schema @xcite . large language models (llms) have shown success in extracting triples from simple sentences using content-based learning approaches @xcite . however, llms often fail to capture all relevant triples @xcite , they still struggle with more complex sentences that contain multiple triples or numerous entities and relations. another challenge is the scale of the parameters. advanced models such as chatgpt @xcite , @xcite , and * corresponding authors @xcite require substantial computational resources during training and inference, which are typically run on highperformance servers. and heavy computations often result in response delays. there is a growing demand for models that can operate efficiently in low-resource environments like cpus to enable deployment on edge devices. however, the substantial resource requirements and response latency of llms hinder their use in information extraction. most current research focuses on large-scale models like chatgpt, while smaller, lightweight llms and agent frameworks have yet to be explored. inspired by @xcite , which stated that instructions containing label information can stimulate model capabilities, we are interested in this research: whether ultra-small, lightweight models can achieve the performance of their larger counterparts in resource-constrained environments by incorporating potential label information into instructions through collective effects. in this paper, we introduce mario-0.5b, an ultra-lightweight model trained on instructionfollowing samples in multiple languages, including chinese, english, korean, and russian. we also present a novel multi-agent framework, smoie, which integrates schema mining, information extraction, reasoning, and decision-making to effectively support mario-0.5b. the framework aims to progressively enrich label information required for oie via efficient group decision-making, thereby enhancing the reasoning capabilities of the mario-0.5b model. the evaluation results show that the multi-agent framework we proposed achieves a significant 6.9% improvement in the f1 score, outperforms largescale models with up to 70b parameters and delivers performance comparable to @xcite . additionally, it reduces computational resources by 140 times and delivers 11 times faster response times, which enables efficient operation in a cpu-only environment. ## related work early methods for the extraction of entities and relations relied on separate models for each task, leadingo cascading potential errors throughout the process @xcite . the rise of deep learning introduced new end-to-end models that could handle both tasks simultaneously, sharing information and optimizing performance together @xcite @xcite . recently, llms have attracted attention due to their impressive performance across various nlp tasks. with the right prompts, llms can match the performance of specially trained models in extraction tasks, even without prior examples or with only a few examples @xcite @xcite . however, these studies have some blind spots: they neither evaluate the models' capability in handling complex sentences with multiple entities and relations, nor explore effective methods for achieving high performance under resource constraints. ## multi agents for oie we present a novel multi-agent framework for extracting triples from texts. the agent framework uses mario-0.5b as the base model. as shown in figure 2 , this framework employs the agent communication protocols to enhance system stability. these protocols enable several coordinated processes: schema mining, triple extraction, reasoning, and behavioral correction. ## conclusion in this paper, we explore an effective step-bystep deepening group decision-making approach to address the issues of insufficient knowledge and resource constraints in open information extraction. we introduce a small-scale 0.5b llm and present an innovative multi-agent framework to support and enhance the model. our proposed agent framework clarifies label information in instructions through schema mining, reasoning, and decision-making, thereby stimulating the reasoning abilities of small-scale models. experimental results show the effectiveness of our approach in enhancing information extraction performance while significantly reducing dependence on computational resources, offering substantial benefits in resource-constrained scenarios.