SensingAgents is a multi-agent LLM framework that reaches 79.5% zero-shot accuracy on IMU activity recognition by using position-specific analysts, debating advocates, and a final decision agent, beating prior agent and deep-learning baselines.
Hearst, Susan T Dumais, Edgar Osuna, John Platt, and Bernhard Scholkopf
2 Pith papers cite this work. Polarity classification is still indexing.
2
Pith papers citing it
years
2026 2verdicts
UNVERDICTED 2representative citing papers
The sum of verifier warnings adds no useful predictive power for code comprehensibility beyond syntactic and developer features.
citing papers explorer
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SensingAgents: A Multi-Agent Collaborative Framework for Robust IMU Activity Recognition
SensingAgents is a multi-agent LLM framework that reaches 79.5% zero-shot accuracy on IMU activity recognition by using position-specific analysts, debating advocates, and a final decision agent, beating prior agent and deep-learning baselines.
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Verifier Warnings Do Not Improve Comprehensibility Prediction
The sum of verifier warnings adds no useful predictive power for code comprehensibility beyond syntactic and developer features.