“Where Does This Strange Smell Come from?”: Enabling Conversational Interfaces for Artificial Olfaction (2025)

Abstract: Existing Artificial Olfaction (AO) primarily serves two tasks: Odor Classification (OC) and Odor Source Localization (OSL). Both tasks w.r.t. indoor event detection scenarios are studied either using a single electronic nose (e-nose) mounted on the ceiling or mobile robot(s) equipped with e-noses. However, they are not compatible with smart home scenarios due to diverse obstacles (e.g., chairs and tables) and the need for natural interaction. In this paper, we explore the feasibility and usability of a Conversational Interfaces for Artificial Olfaction (CIAO) system using Large Language Models (LLMs) in Smart Home. We made the first olfaction-oriented corpus for LLM evaluation, as well as an olfaction dataset via a self-developed olfactory sensory network. We train the dedicated models for OSL and OC using the dataset and integrate them into a tool within the MCP (Model Context Protocol) server. Five commercial LLMs are used as MCP clients for experiments and validation. Our experimental results indicate that our CIAO system is technically feasible and applicable. Besides, we observe that ChatGPT-4o relatively outperforms in terms of both answer quality and overall LLM usability in pervasive IoT scenarios. Qwen-Plus, in contrast, appears to be a promising solution for robot-compatible applications. To our knowledge, this work is the first effort to bring forward conversational interfaces for AO, enabling multi-turn conversations with contexts beyond one-off question answering. Our codes and partial corpus are available at https://github.com/HokyeeJau/CIAO.

## introduction existing artificial olfaction (ao) primarily serves two tasks: odor classification (oc) and odor source localization (osl). oc refers to the process of identifying and categorizing different odors, * corresponding authors. while osl is the task of determining the location of a chemical source of interest in the surrounding environment. both provide recognition results by analyzing the plume released by the source @xcite @xcite . facilitating both tasks can significantly subserve various aspects of human life, with extensive applications in safety and hazard detection, health, and environmental monitoring, just to name a few. researchers to this end commonly develop electronic noses (e-noses) to mimic the animal sense of smell. an e-nose system generally consists of an array of gas sensors and a pattern recognition system (usually based on machine learning or neural networks); they together reveal surrounding odor profiles for odor-sensitive applications @xcite @xcite . indoor applications of e-noses, especially in smart home, encompass a range of use cases such as lifelogging @xcite @xcite @xcite and air quality monitoring @xcite @xcite . when deployed, researchers and vendors typically create different software to vividly and systematically present the recognition results, such as mobile phone applications @xcite @xcite and web-based dashboards @xcite . apparently, the presentation of interpreted odor profiles is largely limited to simple data analysis and visualization. existing systems rarely provide a natural interaction experience or a brief introduction to relevant olfactory knowledge. recent studies primarily apply e-noses in robots, particularly on mobile robots and unmanned aerial vehicles (uavs). these platforms perceive odor stimuli features as spatial gradients in the environment, which enables reactive planning and navigation @xcite ; nana-data management and audit system mcp server mcp client dashboard smart phones laptops host computers edge devices figure 1 : overview of our scenario. we assume that the e-nose components are installed as functional components in home decor or household appliances. powered by a well-developed iot system and llm-based agents, the smart housekeeper system can cooperate them to provide a strong conversational system for multi-end users. @xcite @xcite . robots or uavs can also collaborate to trace odor sources @xcite @xcite . however, in smart home scenarios, diverse obstacles (e.g., chairs, tables) and confined space challenge those robots to detect, plan, and move. ceiling e-noses, additionally, are unable to accurately identify the locations of odor sources. a large-scale yet multi-end portable detection system offers a more compatible solution for smart home environments. the studies on llm (large language model) agents in smart homes recently emerged due to the rising tool use capabilities of llms. we have witnessed several promising solutions such as sage @xcite , homeassistant @xcite , and greenifttt @xcite . those studies focus on device controllability, llm performance, and usability, using computational and statistical methods. however, none of them provisions a study w.r.t. olfactory event detection and a comprehensive corpus for systematically evaluating their methods. to address the abovementioned concerns, we herein take initial steps towards a model context protocol (mcp)-regulated conversational interfaces for artificial olfaction (ciao) system for detecting indoor olfactory events in smart home. figure 1 illustrates an overview of our scenario, where a multi-end olfactory sensory network is built by multiple e-noses, which are equidistantly distributed within a delimited area. to train dedicated models for the oc and osl tasks, we alternatively use and localize different samples at different positions to collect the olfaction dataset using our self-developed e-noses. each sensory (time series) data point inside is assigned with two labels: the odor type and the absolute position of the odor source. the trained oc and osl models are integrated into a data api provided by the iot system. a unified tool deployed on an mcp server is designed to invoke that data api to collect data from relevant devices and provide the majorityvoted odor type and its position. five prevalent commercial llm models are selected according to their different knowledge backgrounds (training corpora) and used as mcp clients for validation and investigation. as a result, a self-developed mcp-regulated ciao system is thereby constructed. we further construct the first olfaction-oriented corpus by humans and llms to evaluate the llm usability. this corpus is devised for not only smart home scenarios, but also the olfactory embodied intelligence and other multimodal tasks. we assess the system usability by collecting the system latency, and evaluate the tool use capabilities by analyzing the statistics of tool calls at multiple given situations. an expert evaluation involving 15 ai researchers from three countries is furthermore carried out to evaluate the answer quality and the llm usability. the emotional feedback capabilities and multi-round conversations of llms are additionally discussed by case studies. we observe that chatgpt-4o empirically outperforms w.r.t. both answer quality and overall llm usability in iot so far. qwen-plus, conversely, is a promising robot-compatible solution for olfactory embodied intelligence. ## conversational interfaces for artificial olfaction we designed a feasible solution to bridge the iot system, mcp-regulated services and the fron- ## evaluation of ciao this section aims to evaluate the capabilities of the five widely-used commercial llms (introduced in section 3.2) employed in our ciao system. we first introduce our olfaction-oriented corpus to assess those llms in section 4.1. we validate the technical feasibility of the mcp-regulated ciao system by evaluating tool use capabilities of the llms in section 4.2. the system latency using each llm is also assessed to show its practical usability in section 4.3. we additionally conduct an expert evaluation to assess the llm usability in section 4.4. a divergent discussion on model empathic capabilities and multi-round conversations is included in section 4.5. the evaluation metrics are detailed in appendix e. ## conclusions and future work this study acts as the first effort to bring forward conversational interfaces for ao tasks including odor classification and localization, enabling multiturn conversation with contexts beyond a one-off question answering. we assume the target is for olfactory event detection in a smart home. we assume the target is for olfactory event detection in a smart home. we constructed the olfactory sensor dataset and olfaction-related question corpus for realizing and evaluating our mcp-regulated ciao system. the results of our extensive experiments indicate that our ciao system is technically feasible in smart home scenarios when supported by advanced llm-based agents. among the evaluated models, the chatgpt series, particularly chatgpt-4o, demonstrates superior performance in terms of tool use, contextual understanding, and answer quality. these findings highlight the potential of chatgpt-4o in the tool-based llm agent systems. qwen-plus, however, exhibits promising traits, especially for applications involving bipedal robots designed to mimic human behaviors. in future work, we will build an olfactionspecific llm for our ciao system, enable more visual aids such as visual reality, customize prompts for different answering styles, and enhance llm capability on handling uncertain recognition.