MOTOR-Bench supplies a real-world video dataset for structured mental state understanding in learning settings, while MOTOR-MAS improves zero-shot prediction of behavior, cognition, and emotion labels over single models and other multi-agent systems.
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4 Pith papers cite this work, alongside 24 external citations. Polarity classification is still indexing.
4
Pith papers citing it
24
external citations · external index
citation-role summary
background 1
method 1
citation-polarity summary
years
2026 4verdicts
UNVERDICTED 4representative citing papers
Machine translation preserves embedding similarity structure for ten languages but distorts it for four in the Manifesto Corpus, via a new non-inferiority testing framework.
Hierarchical CNN-LSTM plus vision transformer detects tremor from raw time-domain kinematic data across nine body parts with average F1 of 0.765 and attention-based explanations.
ReaORE is a progressive open relation extraction method that applies coarse-to-fine reasoning to improve generalization to unseen relations over clustering or direct LLM generation.
citing papers explorer
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MOTOR-Bench: A Real-world Dataset and Multi-agent Framework for Zero-shot Human Mental State Understanding
MOTOR-Bench supplies a real-world video dataset for structured mental state understanding in learning settings, while MOTOR-MAS improves zero-shot prediction of behavior, cognition, and emotion labels over single models and other multi-agent systems.
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Is Textual Similarity Invariant under Machine Translation? Evidence Based on the Political Manifesto Corpus
Machine translation preserves embedding similarity structure for ten languages but distorts it for four in the Manifesto Corpus, via a new non-inferiority testing framework.
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An explainable hierarchical self attention-based approach for tremor detection in the time domain
Hierarchical CNN-LSTM plus vision transformer detects tremor from raw time-domain kinematic data across nine body parts with average F1 of 0.765 and attention-based explanations.
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ReaORE: Reasoning-Guided Progressive Open Relation Extraction Empowered by Large Reasoning Models
ReaORE is a progressive open relation extraction method that applies coarse-to-fine reasoning to improve generalization to unseen relations over clustering or direct LLM generation.