A consistent preprocessing pipeline applied to 14 mind wandering datasets reveals varying detection performance across modalities and models, with open code for future work.
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WM-SAR decomposes sarcasm into LLM-agent components, quantifies literal-normative inconsistency deterministically, and integrates it with intention via logistic regression to outperform prior sarcasm detectors on benchmarks.
Microstate tokenizer from clustered EEG signals provides universal representations that outperform traditional time- and frequency-domain features across sleep staging, emotion recognition, and motor imagery tasks.
citing papers explorer
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Automatic Mind Wandering Detection in Educational Settings: A Systematic Review and Multimodal Benchmarking
A consistent preprocessing pipeline applied to 14 mind wandering datasets reveals varying detection performance across modalities and models, with open code for future work.
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World model inspired sarcasm reasoning with large language model agents
WM-SAR decomposes sarcasm into LLM-agent components, quantifies literal-normative inconsistency deterministically, and integrates it with intention via logistic regression to outperform prior sarcasm detectors on benchmarks.
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Atoms of Thought: Universal EEG Representation Learning with Microstates
Microstate tokenizer from clustered EEG signals provides universal representations that outperform traditional time- and frequency-domain features across sleep staging, emotion recognition, and motor imagery tasks.