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arxiv: 2605.04608 · v1 · submitted 2026-05-06 · 💻 cs.AI

Recognition: 3 theorem links

· Lean Theorem

SensingAgents: A Multi-Agent Collaborative Framework for Robust IMU Activity Recognition

Naiyu Zheng , Tianlong Yu , Haochen Yin , Xiaoyi Fan , Xiping Hu , Zhimeng Yin
Authors on Pith no claims yet

Pith reviewed 2026-05-08 17:53 UTC · model grok-4.3

classification 💻 cs.AI
keywords multi-agent systemsIMU activity recognitionlarge language modelszero-shot learninghuman activity recognitionsensor conflict resolutionubiquitous sensing
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The pith

Multi-agent LLM framework reaches 79.5% zero-shot accuracy on IMU activity recognition by debating sensor conflicts

A machine-rendered reading of the paper's core claim, the machinery that carries it, and where it could break.

The paper proposes SensingAgents, a multi-agent system that uses large language models to recognize human activities from IMU sensor readings. It divides work among Analyst Agents that examine data from specific body positions such as arm or pocket, Advocate Agents that debate to settle disagreements between those readings, and a Decision Agent that accounts for possible sensor problems. This structure is meant to avoid the heavy need for labeled training data and the opaque reasoning common in deep learning models for activity recognition. A sympathetic reader would care because reliable zero-shot performance on noisy or conflicting sensors could support more practical mobile health and smart environment applications.

Core claim

SensingAgents organizes LLM-powered agents into specialized roles: a group of Analyst Agents for position-specific sensor analysis (arm, wrist, belt, pocket), a pair of Advocate Agents that resolves sensor conflicts through dynamic and static dialectical debates, and a Decision Agent that ensures reliability under sensor drift or failure. Evaluation on the Shoaib dataset demonstrates that SensingAgents significantly outperforms state-of-the-art single-agent and multi-agent LLM models, achieving an accuracy of 79.5% in a zero setting--29% higher than existing agent models and 9.4% higher than deep learning baselines--particularly in complex scenarios where multi-sensor data is conflicting or

What carries the argument

The SensingAgents multi-agent collaborative framework with Analyst Agents for position-specific IMU analysis, Advocate Agents using dialectical debate for conflict resolution, and a Decision Agent for final reliability checks.

If this is right

  • Achieves 79.5% accuracy in zero-shot settings on the Shoaib dataset.
  • Outperforms single-agent and multi-agent LLM models by 29% and deep learning baselines by 9.4%.
  • Handles conflicting or noisy multi-sensor data more effectively than prior single-model approaches.
  • Supplies transparent reasoning steps for activity predictions through agent debates.

Where Pith is reading between the lines

These are editorial extensions of the paper, not claims the author makes directly.

  • The debate mechanism for resolving sensor disagreements could be tested on other multi-modal fusion tasks beyond IMU data.
  • Logging the agents' analysis and debate steps might enable user-facing explanations in mobile health applications.

Load-bearing premise

That LLM agents prompted with specialized roles can perform accurate position-specific sensor analysis and resolve conflicts through dialectical debate without hallucinations, bias, or need for fine-tuning or external verification.

What would settle it

A controlled test on the Shoaib dataset with artificially added noise to one sensor position where SensingAgents accuracy falls below deep learning baselines would falsify the claim of superior robustness.

Figures

Figures reproduced from arXiv: 2605.04608 by Haochen Yin, Naiyu Zheng, Tianlong Yu, Xiaoyi Fan, Xiping Hu, Zhimeng Yin.

Figure 1
Figure 1. Figure 1: The IMU data is normally collected at several body view at source ↗
Figure 2
Figure 2. Figure 2: A traditional single agent leads to cognitive overload view at source ↗
Figure 3
Figure 3. Figure 3: Workflow of SensingAgents. this direction. These works show that LLMs can perform zero-shot or few-shot activity recognition by aligning pre-processed sensor data (e.g., symbolic representations or summaries) with natural language descriptions of activities. For example, an LLM prompted with a wrist accelerometer summary like "high frequency, moderate amplitude oscillations" can infer the activity. Recent … view at source ↗
Figure 4
Figure 4. Figure 4: Even though jogging and walking are rhythmic view at source ↗
Figure 6
Figure 6. Figure 6: A round debate example of dynamic and static ad view at source ↗
Figure 7
Figure 7. Figure 7: Overall classification performance across agent view at source ↗
Figure 9
Figure 9. Figure 9: Average token consumption per sample, broken down by pipeline stage and token type (input vs. output). DeepSeek -chat GPT-5 -mini Claude Sonnet-4.6 GLM-4.7 MiniMax -2.7 0 20 40 60 80 23s 28s 55s 72s 83s Best Latency Other Backends Fastest: DeepSeek-chat (23s) view at source ↗
read the original abstract

Human Activity Recognition (HAR) using Inertial Measurement Unit (IMU) sensors is a cornerstone of mobile health, smart environments, and human-computer interaction. However, current deep learning-based HAR models often struggle with heavy reliance on labeled data, position-specific ambiguity, and a lack of transparent reasoning. Inspired by the advanced agents framework, which emulates a collaborative agent using Large Language Models (LLMs), we propose SensingAgents, a novel multi-agent system for robust IMU activity recognition. SensingAgents organizes LLM-powered agents into specialized roles: a group of Analyst Agents for position-specific sensor analysis (arm, wrist, belt, pocket), a pair of Advocate Agents that resolves sensor conflicts through dynamic and static dialectical debates, and a Decision Agent that ensures reliability under sensor drift or failure. Evaluation on the Shoaib dataset demonstrates that SensingAgents significantly outperforms state-of-the-art single-agent and multi-agent LLM models, achieving an accuracy of 79.5% in a zero setting--29% higher than existing agent models and 9.4% higher than deep learning baselines--particularly in complex scenarios where multi-sensor data is conflicting or noisy. Our work highlights the potential of multi-agent collaborative reasoning for advancing the robustness and interpretability of ubiquitous sensing systems.

Editorial analysis

A structured set of objections, weighed in public.

Desk editor's note, referee report, simulated authors' rebuttal, and a circularity audit. Tearing a paper down is the easy half of reading it; the pith above is the substance, this is the friction.

Referee Report

3 major / 2 minor

Summary. The paper proposes SensingAgents, a multi-agent LLM framework for IMU-based human activity recognition. Analyst Agents perform position-specific analysis on sensors at arm, wrist, belt, and pocket locations; Advocate Agents resolve conflicts via dynamic and static dialectical debates; and a Decision Agent ensures reliability under drift or failure. On the Shoaib dataset in a zero-shot setting, the system reports 79.5% accuracy, outperforming other agent models by 29% and deep learning baselines by 9.4%, with emphasis on robustness to conflicting or noisy multi-sensor inputs.

Significance. If the results hold under rigorous verification, the work offers a label-efficient, interpretable alternative to supervised deep learning for HAR by leveraging collaborative LLM reasoning for sensor fusion and conflict resolution. The role specialization and debate mechanism represent a novel application of multi-agent systems to ubiquitous sensing, potentially improving robustness in real-world conditions with sensor ambiguity or failure. The empirical gains, if reproducible, could motivate further exploration of agent-based approaches in mobile health and HCI.

major comments (3)
  1. [Abstract/Evaluation] Abstract and Evaluation section: The central claim of 79.5% zero-shot accuracy (with 29% and 9.4% gains) is presented without any description of IMU signal preprocessing, conversion of time-series data into LLM prompts, dataset splits, number of activities/classes, evaluation runs, or statistical significance tests. This absence makes it impossible to verify whether the reported outperformance reflects genuine robustness or artifacts of the experimental protocol.
  2. [Agent Architecture] Advocate Agents and Decision Agent descriptions: The assumption that dialectical debate between Advocate Agents can reliably resolve position-specific conflicts and noise without introducing hallucinations or ungrounded reasoning is load-bearing for the robustness claim, yet no verification mechanism, grounding step, or error analysis for debate outputs is provided. LLMs lack native signal-processing capabilities, so the absence of external checks or ablation on hallucination rates undermines the assertion that the multi-agent setup outperforms baselines in complex scenarios.
  3. [Evaluation] Comparison to deep learning baselines: The 9.4% improvement over DL baselines in a zero-shot setting requires explicit clarification of whether the DL models were trained on the Shoaib dataset or also evaluated zero-shot; without this, the cross-paradigm comparison cannot be interpreted as evidence of superior robustness.
minor comments (2)
  1. Clarify the exact meaning of 'zero setting' (presumably zero-shot) and provide the full list of activity classes and sensor positions used in the Shoaib evaluation.
  2. Include a diagram or pseudocode illustrating the information flow between Analyst, Advocate, and Decision Agents to aid reader comprehension of the collaborative process.

Simulated Author's Rebuttal

3 responses · 0 unresolved

We thank the referee for the constructive and detailed feedback. The comments highlight important areas for improving clarity, reproducibility, and rigor in our presentation of SensingAgents. We address each major comment below with clarifications and have revised the manuscript to incorporate additional details and analyses where needed.

read point-by-point responses

  1. Referee: [Abstract/Evaluation] Abstract and Evaluation section: The central claim of 79.5% zero-shot accuracy (with 29% and 9.4% gains) is presented without any description of IMU signal preprocessing, conversion of time-series data into LLM prompts, dataset splits, number of activities/classes, evaluation runs, or statistical significance tests. This absence makes it impossible to verify whether the reported outperformance reflects genuine robustness or artifacts of the experimental protocol.

    Authors: We agree that the original manuscript omitted key experimental details necessary for verification. In the revised version, we have added a new 'Experimental Protocol' subsection in the Evaluation section. This describes IMU preprocessing (band-pass filtering at 0.5-20 Hz, z-score normalization, and segmentation into 2-second windows with 50% overlap), the prompt engineering approach (extracting statistical and frequency features from each position-specific sensor stream and converting them into structured textual summaries for the LLM), the Shoaib dataset (6 activity classes, 4 body positions, zero-shot protocol with no labeled training data or fine-tuning), 5 independent runs with different random seeds for prompt sampling, and statistical significance via paired t-tests on accuracy differences. These additions enable full assessment of the reported results. revision: yes

  2. Referee: [Agent Architecture] Advocate Agents and Decision Agent descriptions: The assumption that dialectical debate between Advocate Agents can reliably resolve position-specific conflicts and noise without introducing hallucinations or ungrounded reasoning is load-bearing for the robustness claim, yet no verification mechanism, grounding step, or error analysis for debate outputs is provided. LLMs lack native signal-processing capabilities, so the absence of external checks or ablation on hallucination rates undermines the assertion that the multi-agent setup outperforms baselines in complex scenarios.

    Authors: This concern about potential hallucinations in the debate process is well-taken, as LLMs operate on textual representations rather than raw signals. The original design grounds the process by having Analyst Agents produce feature-based summaries directly from the IMU data, with the Decision Agent performing a final cross-check against all position inputs for consistency. To address the request for verification, the revised manuscript adds an error analysis subsection that samples debate outputs and compares them against the underlying analyst summaries and raw feature values to quantify unsupported claims. We also include an ablation study removing the debate mechanism to measure its impact on robustness in noisy cases. These additions provide the requested checks while acknowledging the inherent limitations of LLM reasoning. revision: yes

  3. Referee: [Evaluation] Comparison to deep learning baselines: The 9.4% improvement over DL baselines in a zero-shot setting requires explicit clarification of whether the DL models were trained on the Shoaib dataset or also evaluated zero-shot; without this, the cross-paradigm comparison cannot be interpreted as evidence of superior robustness.

    Authors: We appreciate the opportunity to clarify this point. The deep learning baselines (standard CNN, LSTM, and Transformer models from the HAR literature) were trained in a fully supervised manner on labeled portions of the Shoaib dataset using conventional train-test splits. SensingAgents, by contrast, performs zero-shot inference with no access to any training labels or fine-tuning. The reported 9.4% gain therefore illustrates the label efficiency and robustness of the multi-agent approach relative to supervised DL methods, rather than a matched zero-shot comparison between paradigms. We have updated the Evaluation section to state this distinction explicitly and added discussion on the practical advantages for settings where labeled IMU data is scarce or unavailable. revision: yes

Circularity Check

0 steps flagged

No circularity: purely empirical multi-agent framework evaluated on external dataset

full rationale

The paper introduces SensingAgents as a multi-agent LLM system with Analyst, Advocate, and Decision agents for IMU-based activity recognition. Its central claim rests on an empirical evaluation reporting 79.5% accuracy on the Shoaib dataset, outperforming baselines. No equations, derivations, fitted parameters, or predictions appear in the provided text. No self-citations are invoked as load-bearing premises for any result. The work is self-contained against an external benchmark dataset, with no reduction of outputs to inputs by construction. This matches the default non-circular case for empirical system papers.

Axiom & Free-Parameter Ledger

0 free parameters · 1 axioms · 3 invented entities

The framework rests on the untested premise that current LLMs can reliably execute the assigned analytical and debating roles when given only natural-language prompts; no free parameters are stated, but the entire performance depends on this domain assumption about LLM capability.

axioms (1)
  • domain assumption LLMs can perform reliable position-specific sensor analysis and dialectical conflict resolution when assigned specialized roles via prompting
    This is the load-bearing premise that allows the multi-agent system to function without training data or fine-tuning.
invented entities (3)
  • Analyst Agents (arm, wrist, belt, pocket) no independent evidence
    purpose: To produce position-specific interpretations of IMU data
    New role introduced by the paper; no independent evidence supplied beyond the claimed accuracy gain.
  • Advocate Agents no independent evidence
    purpose: To resolve sensor conflicts via dynamic and static debates
    New role introduced by the paper; no independent evidence supplied beyond the claimed accuracy gain.
  • Decision Agent no independent evidence
    purpose: To produce final activity label under sensor drift or failure
    New role introduced by the paper; no independent evidence supplied beyond the claimed accuracy gain.

pith-pipeline@v0.9.0 · 5531 in / 1469 out tokens · 57229 ms · 2026-05-08T17:53:02.425838+00:00 · methodology

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Reference graph

Works this paper leans on

33 extracted references · 5 canonical work pages · 1 internal anchor

  1. [1]

    Yasmin Abdelaal, Michaël Aupetit, Abdelkader Baggag, and Dena Al-Thani. 2024. Exploring the applications of explainability in wearable data analytics: Systematic literature review.Journal of Medical Internet Research26 (2024), e53863

    2024

  2. [2]

    Aisha Alansari and Hamzah Luqman. 2025. Large language models hallucination: A comprehensive survey.arXiv preprint arXiv:2510.06265(2025)

    work page arXiv 2025

  3. [3]

    Tuo An, Yunjiao Zhou, Han Zou, and Jianfei Yang. 2026. IoT-LLM: A framework for enhancing large language model reasoning from real-world sensor data. Patterns7, 1 (2026)

    2026

  4. [4]

    Anthropic. 2026. Claude Code. https://github.com/anthropics/claude-code

    2026

  5. [5]

    Sheikh Asif Imran Shouborno, Mohammad Nur Hossain Khan, Subrata Biswas, and Bashima Islam. 2025. LLaSA: A Sensor-Aware LLM for Natural Language Reasoning of Human Activity from IMU Data. InCompanion of the 2025 ACM International Joint Conference on Pervasive and Ubiquitous Computing. 893–899

    2025

  6. [6]

    Cho-Chun Chiu, Tuan Nguyen, Ting He, Shiqiang Wang, Beom-Su Kim, and Ki Il Kim. 2024. Active learning for wban-based health monitoring. InProceedings of the Twenty-fifth International Symposium on Theory, Algorithmic Foundations, and Protocol Design for Mobile Networks and Mobile Computing. 231–240

    2024

  7. [7]

    Ilker Demirel, Karan Thakkar, Benjamin Elizalde, Miquel Espi Marques, Aditya Sarathy, Yang Bai, Umamahesh Srinivas, Jiajie Xu, Shirley Ren, and Jaya Narain

  8. [8]

    arXiv preprint arXiv:2509.10729(2025)

    Using LLMs for Late Multimodal Sensor Fusion for Activity Recognition. arXiv preprint arXiv:2509.10729(2025)

    work page arXiv 2025

  9. [9]

    Emilio Ferrara. 2024. Large language models for wearable sensor-based human activity recognition, health monitoring, and behavioral modeling: a survey of early trends, datasets, and challenges.Sensors24, 15 (2024), 5045

    2024

  10. [10]

    Hearst, Susan T Dumais, Edgar Osuna, John Platt, and Bernhard Scholkopf

    Marti A. Hearst, Susan T Dumais, Edgar Osuna, John Platt, and Bernhard Scholkopf. 1998. Support vector machines.IEEE Intelligent Systems and their applications13, 4 (1998), 18–28

    1998

  11. [11]

    Zhiqing Hong, Yiwei Song, Zelong Li, Anlan Yu, Shuxin Zhong, Yi Ding, Tian He, and Desheng Zhang. 2025. LLM4HAR: Generalizable On-device Human Activity Recognition with Pretrained LLMs. InProceedings of the 31st ACM SIGKDD Conference on Knowledge Discovery and Data Mining. 4511–4521

    2025

  12. [12]

    Jeya Vikranth Jeyakumar, Ankur Sarker, Luis Antonio Garcia, and Mani Sri- vastava. 2023. X-char: A concept-based explainable complex human activity recognition model.Proceedings of the ACM on interactive, mobile, wearable and ubiquitous technologies7, 1 (2023), 1–28

    2023

  13. [13]

    Sijie Ji, Xinzhe Zheng, and Chenshu Wu. 2024. Hargpt: Are llms zero-shot human activity recognizers?. In2024 IEEE International Workshop on Foundation Models for Cyber-Physical Systems & Internet of Things (FMSys). IEEE, 38–43

    2024

  14. [14]

    Minwoo Kim, Jaechan Cho, Seongjoo Lee, and Yunho Jung. 2019. IMU sensor- based hand gesture recognition for human-machine interfaces.Sensors19, 18 (2019), 3827

    2019

  15. [15]

    Ha Le, Akshat Choube, Vedant Das Swain, Varun Mishra, and Stephen Intille. 2025. A Multi-Agent LLM Network for Suggesting and Correcting Human Activity and Posture Annotations. InCompanion of the 2025 ACM International Joint Conference on Pervasive and Ubiquitous Computing. 877–884

    2025

  16. [16]

    Zechen Li, Baiyu Chen, Hao Xue, and Flora D Salim. 2025. Zara: Zero-shot motion time-series analysis via knowledge and retrieval driven llm agents.arXiv preprint arXiv:2508.04038(2025)

    work page internal anchor Pith review Pith/arXiv arXiv 2025

  17. [17]

    Zechen Li, Shohreh Deldari, Linyao Chen, Hao Xue, and Flora D Salim. 2025. Sensorllm: Aligning large language models with motion sensors for human activity recognition. InProceedings of the 2025 Conference on Empirical Methods in Natural Language Processing. 354–379

    2025

  18. [18]

    Andrea Mannini and Angelo Maria Sabatini. 2010. Machine learning methods for classifying human physical activity from on-body accelerometers.Sensors10, 2 (2010), 1154–1175

    2010

  19. [19]

    OpenClaw. 2025. OpenClaw. https://openclaw.ai/

    2025

  20. [20]

    Francisco Javier Ordóñez and Daniel Roggen. 2016. Deep convolutional and lstm recurrent neural networks for multimodal wearable activity recognition.Sensors 16, 1 (2016), 115

    2016

  21. [21]

    Muhammad Shoaib, Stephan Bosch, Ozlem Durmaz Incel, Hans Scholten, and Paul JM Havinga. 2014. Fusion of smartphone motion sensors for physical activity recognition.Sensors14, 6 (2014), 10146–10176

    2014

  22. [22]

    Qin Tang, Jing Liang, and Fangqi Zhu. 2023. A comparative review on multi-modal sensors fusion based on deep learning.Signal Processing213 (2023), 109165

    2023

  23. [23]

    Jason Wei, Xuezhi Wang, Dale Schuurmans, Maarten Bosma, Fei Xia, Ed Chi, Quoc V Le, Denny Zhou, et al. 2022. Chain-of-thought prompting elicits reasoning in large language models.Advances in neural information processing systems35 (2022), 24824–24837

    2022

  24. [24]

    Huatao Xu, Liying Han, Qirui Yang, Mo Li, and Mani Srivastava. 2024. Penetra- tive ai: Making llms comprehend the physical world. InProceedings of the 25th International Workshop on Mobile Computing Systems and Applications. 1–7

    2024

  25. [25]

    Huatao Xu, Pengfei Zhou, Rui Tan, Mo Li, and Guobin Shen. 2021. Limu-bert: Unleashing the potential of unlabeled data for imu sensing applications. In Proceedings of the 19th ACM Conference on Embedded Networked Sensor Systems. 220–233

    2021

  26. [26]

    Hua Yan, Heng Tan, Yi Ding, Pengfei Zhou, Vinod Namboodiri, and Yu Yang. 2025. Large Language Model-guided Semantic Alignment for Human Activity Recog- nition.Proceedings of the ACM on Interactive, Mobile, Wearable and Ubiquitous Technologies9, 4 (2025), 1–25

    2025

  27. [27]

    Yifan Yan, Shuai Yang, Xiuzhen Guo, Xiangguang Wang, Wei Chow, Yuanchao Shu, and Shibo He. 2025. mmExpert: Integrating Large Language Models for Comprehensive mmWave Data Synthesis and Understanding. InProceedings of the Twenty-sixth International Symposium on Theory, Algorithmic Foundations, and Protocol Design for Mobile Networks and Mobile Computing. 1...

    2025

  28. [28]

    Hyungjun Yoon, Mohammad Malekzadeh, Sung-Ju Lee, Fahim Kawsar, and Lorena Qendro. 2026. ConSensus: Multi-Agent Collaboration for Multimodal Sensing.arXiv preprint arXiv:2601.06453(2026)

    work page arXiv 2026

  29. [29]

    Fusang Zhang, Jie Xiong, Zhaoxin Chang, Junqi Ma, and Daqing Zhang. 2022. Mobi2Sense: empowering wireless sensing with mobility. InProceedings of the 28th Annual International Conference on Mobile Computing And Networking. 268– 281

    2022

  30. [30]

    Yilin Zhao, Yihong Chen, Rui Tang, and Wenjie Zhao. 2025. MultiagentsCR: A Multi-Agent Collaborative Reasoning Framework Based on LLM for Human Activity Recognition. In2025 10th International Conference on Information Science, Computer Technology and Transportation (ISCTT). IEEE, 90–95

    2025

  31. [31]

    Huiyu Zhou and Huosheng Hu. 2007. Inertial sensors for motion detection of human upper limbs.Sensor Review27, 2 (2007), 151–158

    2007

  32. [32]

    Yang, and Qun Jin

    Xiaokang Zhou, Wei Liang, Kevin I-Kai Wang, Hao Wang, Laurence T. Yang, and Qun Jin. 2020. Deep-Learning-Enhanced Human Activity Recognition for Internet of Healthcare Things.IEEE Internet of Things Journal7, 7 (2020), 6429–

    2020

  33. [33]

    doi:10.1109/JIOT.2020.2985082

    work page doi:10.1109/jiot.2020.2985082 2020