---
title: "ConSensus: Multi-Agent Collaboration for Multimodal Sensing"
description: "LLMs are increasingly asked to interpret sensor streams such as motion, biosignals and environmental readings. 41 references."
authors: ["Hyungjun Yoon", "Mohammad Malekzadeh", "Sung-Ju Lee", "Fahim Kawsar", "Lorena Qendro"]
year: 2026
venue: "Findings of the Association for Computational Linguistics: ACL 2026"
source: "https://aclanthology.org/2026.findings-acl.1649/"
doi: "10.18653/v1/2026.findings-acl.1649"
licence: "CC BY 4.0 (ACL Anthology)"
licence_url: "https://creativecommons.org/licenses/by/4.0/"
summary_licence: "Elamur Cosmic License"
canonical: "https://research.elamur.ai/free/en/papers/yoon-2026-consensus-multi-agent-collaboration-multimodal/"
---
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# ConSensus: Multi-Agent Collaboration for Multimodal Sensing

**Authors:** Hyungjun Yoon, Mohammad Malekzadeh, [Sung-Ju Lee](https://md.research.elamur.ai/free/en/authors/sung-ju-lee/), Fahim Kawsar and Lorena Qendro · **Year:** 2026 · **Venue:** [Findings of the Association for Computational Linguistics: ACL 2026](https://md.research.elamur.ai/free/en/venues/findings/) ([Findings of the Association for Computational Linguistics 2026](https://md.research.elamur.ai/free/en/venues/findings/2026/)) · **Publisher:** [Association for Computational Linguistics](https://md.research.elamur.ai/free/en/publishers/association-for-computational-linguistics/) · **Licence:** [CC BY 4.0 (ACL Anthology)](https://creativecommons.org/licenses/by/4.0/)

**Links:** [Publisher's page](https://aclanthology.org/2026.findings-acl.1649/) · [PDF](https://aclanthology.org/2026.findings-acl.1649.pdf) · [DOI 10.18653/v1/2026.findings-acl.1649](https://doi.org/10.18653/v1/2026.findings-acl.1649) · [OpenAlex](https://openalex.org/W7166836509)

**Citations:** 0 citations (OpenAlex, 2026-10-14)

## Summary
LLMs are increasingly asked to interpret sensor streams such as motion, biosignals and environmental readings. However, putting many heterogeneous modalities into one prompt works poorly. The authors observe three failure patterns:
- A single LLM tends to ignore evidence from some sensors.
- An LLM judge merging per-sensor opinions over-trusts modalities it assumes matter most, such as ECG.
- Plain majority voting breaks down when sensors are noisy or absent.

ConSensus is a training-free framework designed around these weaknesses. Each sensor modality gets a dedicated LLM agent that reasons over hand-crafted features. The agents' answers are then combined in a single round by three fusion agents:
- a semantic agent that reasons across the per-sensor explanations,
- a statistical agent tied to the majority vote,
- a hybrid coordinator that weighs both views.

On five multimodal sensing benchmarks, the method raises accuracy by 7.1% on average over a single agent. It is competitive with iterative multi-agent debate at a small fraction of the token cost. This makes LLM-based sensing both more robust and cheaper to run.

## Key points
- Each modality input is represented by standard hand-crafted features (e.g., mean, std). The formulation can also accept raw signals, images or learned embeddings.
- Motivating examples use WESAD (affective state: baseline, stress or amusement) with gpt-oss-20B, including a setting where 30% of modalities are missing.
- Average accuracy improves by 7.1% over the single-agent baseline across five benchmarks.
- ConSensus matches or beats state-of-the-art multi-agent debate baselines while cutting average fusion token cost by 12.7×, thanks to its single-round protocol.
- Limitations: only classification tasks, experiments on dataset subsets, and no fine-tuning or RAG. Code is released on GitHub (Nokia).

*Our summary of the paper, in our own words — not its text. The work belongs to its authors; read it at the links above.*

## Institutions

- [Korea Advanced Institute of Science and Technology](https://md.research.elamur.ai/free/en/universities/korea-advanced-institute-of-science-and-technology/) — University, Daejeon, South Korea · 56 works · 3,269 citations
- [University of Glasgow](https://md.research.elamur.ai/free/en/universities/university-of-glasgow/) — University, Glasgow, United Kingdom · 14 works · 746 citations

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## Cite

Cite as: Hyungjun Yoon, Mohammad Malekzadeh, Sung-Ju Lee, Fahim Kawsar and Lorena Qendro. 2026. ConSensus: Multi-Agent Collaboration for Multimodal Sensing. Findings of the Association for Computational Linguistics: ACL 2026.

The paper itself, not this page. BibTeX from the library's record of the work.

```bibtex
@inproceedings{yoon2026consensus,
  title = {{ConSensus: Multi-Agent Collaboration for Multimodal Sensing}},
  author = {Hyungjun Yoon, Mohammad Malekzadeh, Sung-Ju Lee, Fahim Kawsar and Lorena Qendro},
  booktitle = {Findings of the Association for Computational Linguistics: ACL 2026},
  year = {2026},
  doi = {10.18653/v1/2026.findings-acl.1649},
  url = {https://doi.org/10.18653/v1/2026.findings-acl.1649}
}
```

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- [↑ back to papers of 2026](https://md.research.elamur.ai/free/en/papers/2026/page/27/)

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40. Yuwei Zhang, Kumar Ayush, Siyuan Qiao, A. Ali Heydari, Girish Narayanswamy, Maxwell A Xu, Ahmed Metwally, Jinhua Xu, Jake Garrison, Xuhai Xu, Tim Althoff, Yun Liu, Pushmeet Kohli, Jiening Zhan, Mark Malhotra, Shwetak Patel, Cecilia Mascolo, Xin Liu, Daniel McDuff, and Yuzhe Yang. 2025b. SensorLM: Le… [find](https://scholar.google.com/scholar?q=Yuwei+Zhang%2C+Kumar+Ayush%2C+Siyuan+Qiao%2C+A%2E+Ali+Heydari%2C+Girish+Narayanswamy%2C+Maxwell+A+Xu%2C+Ahmed+Metwally%2C+Jinhua+Xu%2C+Jake+Garrison%2C+Xuhai+Xu%2C+Tim+Althoff%2C+Yun+Liu%2C+Pushmeet+Kohli%2C+Jiening+Zhan%2C+Mark+M)
41. Dataset Task \\\#Classes Sensor modalities WESAD Affective state recognition ACC ECG EMG EDA TEMP RESP ( Chest ) ACC PPG EDA TEMP ( Wrist ) SleepEDF Sleep stage classification EEG ( Fpz-Cz ) EEG ( Pz-Oz ) EOG EMG RESP ActionSense Kitchen activity recognition ACC ANG EMG ( Left arm ), ACC ANG EMG ( Rig… [find](https://scholar.google.com/scholar?q=Dataset+Task+%5C%5C%5C%23Classes+Sensor+modalities+WESAD+Affective+state+recognition+ACC+ECG+EMG+EDA+TEMP+RESP+%28+Chest+%29+ACC+PPG+EDA+TEMP+%28+Wrist+%29+SleepEDF+Sleep+stage+classification+EEG+%28+Fpz%2DCz+%29+EEG+%28+Pz%2D)

*Updated 2 November 2026*