The work creates a new benchmark for humanizing GUI agent touch dynamics via a MinMax detector-agent model, a mobile touch dataset, and methods showing agents can match human behavior without losing task performance.
Support-vector networks.Machine learning, 20(3):273–297
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AI-generated text detectors achieve high benchmark accuracy by exploiting unstable dataset-specific linguistic features, as evidenced by cross-domain degradation and differing SHAP explanations across corpora.
Negative-capable ridge regression uses controlled negative regularization as anti-shrinkage to increase effective complexity along weak eigendirections and mitigate underfitting in small-data regression.
AML outperforms cross-validated baselines including CNNs on 50-2000 example image datasets and is comparable to XGBoost/LightGBM on tabular data using only training data and no task-dependent hyperparameters.
A TCN plus Attention-LSTM model trained on 2014-2024 Chinese A-share data outperforms static baselines and identifies prolonged undervaluation as the long-term driver and sudden cash-flow increases as the short-term trigger for repurchases.
citing papers explorer
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Turing Test on Screen: A Benchmark for Mobile GUI Agent Humanization
The work creates a new benchmark for humanizing GUI agent touch dynamics via a MinMax detector-agent model, a mobile touch dataset, and methods showing agents can match human behavior without losing task performance.
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Why AI-Generated Text Detection Fails: Evidence from Explainable AI Beyond Benchmark Accuracy
AI-generated text detectors achieve high benchmark accuracy by exploiting unstable dataset-specific linguistic features, as evidenced by cross-domain degradation and differing SHAP explanations across corpora.
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A Ridge Too Far: Correcting Over-Shrinkage via Negative Regularization
Negative-capable ridge regression uses controlled negative regularization as anti-shrinkage to increase effective complexity along weak eigendirections and mitigate underfitting in small-data regression.
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Algebraic Machine Learning for Small-to-Medium Datasets Is Competitive against Strong Standard Baselines
AML outperforms cross-validated baselines including CNNs on 50-2000 example image datasets and is comparable to XGBoost/LightGBM on tabular data using only training data and no task-dependent hyperparameters.
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Dynamic Forecasting and Temporal Feature Evolution of Stock Repurchases in Listed Companies Using Attention-Based Deep Temporal Networks
A TCN plus Attention-LSTM model trained on 2014-2024 Chinese A-share data outperforms static baselines and identifies prolonged undervaluation as the long-term driver and sudden cash-flow increases as the short-term trigger for repurchases.