MELT is the first behavioral trace dataset for high-risk memecoin launch detection on Solana, providing 122 features, risk annotations, and ML benchmarks that reduce investment loss when used for selection.
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9 Pith papers cite this work. Polarity classification is still indexing.
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Pith papers citing it
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2026 9representative citing papers
WSTypist is a new RL-based simulation model that reproduces human-like word suggestion strategies, individual differences, and adaptation to design changes in mobile text entry.
IGSTGNN adds incident-context spatial fusion and temporal impact decay modules to model how events alter traffic patterns, achieving state-of-the-art results on a new time-aligned incident-traffic dataset.
SocialLDG models six socio-cognitive tasks with lexical priors from language models and time-evolving task affinities via dynamic graphs, claiming state-of-the-art results on two public human-robot interaction datasets plus scalability without forgetting.
A single transformer model trained offline on expert trajectories from three distinct MARL environments achieves competitive performance against specialized baselines without per-task tuning.
Multi-source transfer learning for building thermal dynamics yields up to 63% lower forecasting errors than single-source models and outperforms time series foundation models when pretrained on 16-32 buildings over one year.
WRF4CIR uses weight-regularized fine-tuning with adversarial perturbations to mitigate overfitting in composed image retrieval and narrows the generalization gap on benchmarks.
citing papers explorer
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MELT: A Behavioral Trace Dataset for High-Risk Memecoin Launch Detection
MELT is the first behavioral trace dataset for high-risk memecoin launch detection on Solana, providing 122 features, risk annotations, and ML benchmarks that reduce investment loss when used for selection.
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Simulating Word Suggestion Usage in Mobile Typing to Guide Intelligent Text Entry Design
WSTypist is a new RL-based simulation model that reproduces human-like word suggestion strategies, individual differences, and adaptation to design changes in mobile text entry.
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Incident-Guided Spatiotemporal Traffic Forecasting
IGSTGNN adds incident-context spatial fusion and temporal impact decay modules to model how events alter traffic patterns, achieving state-of-the-art results on a new time-aligned incident-traffic dataset.
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Teaching Robots to Interpret Social Interactions through Lexically-guided Dynamic Graph Learning
SocialLDG models six socio-cognitive tasks with lexical priors from language models and time-evolving task affinities via dynamic graphs, claiming state-of-the-art results on two public human-robot interaction datasets plus scalability without forgetting.
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MARL-GPT: Foundation Model for Multi-Agent Reinforcement Learning
A single transformer model trained offline on expert trajectories from three distinct MARL environments achieves competitive performance against specialized baselines without per-task tuning.
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Thermal-GEMs: Generalized Models for Building Thermal Dynamics
Multi-source transfer learning for building thermal dynamics yields up to 63% lower forecasting errors than single-source models and outperforms time series foundation models when pretrained on 16-32 buildings over one year.
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WRF4CIR: Weight-Regularized Fine-Tuning Network for Composed Image Retrieval
WRF4CIR uses weight-regularized fine-tuning with adversarial perturbations to mitigate overfitting in composed image retrieval and narrows the generalization gap on benchmarks.
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