Proposes equation-grounded taxonomy (unexpected AIS activity, route deviation, close approach) and LLM-guided synthesis pipeline to generate timestamp-labeled anomalies for evaluating maritime detection models.
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3 Pith papers cite this work, alongside 4 external citations. Polarity classification is still indexing.
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QDTraj uses Quality-Diversity algorithms with sparse rewards to produce at least five times more diverse high-performing trajectories for articulated object manipulation than compared methods, validated across 30 objects with hundreds of trajectories per task.
Adding controlled noise and irrelevant persona contexts across training and testing stages for strong LLMs yields better reasoning and efficiency than high-quality data alone, reaching 76.7% on AIME24/25 with Qwen2.5-32B.
citing papers explorer
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Redefining Maritime Anomaly Detection via Equation-Grounded Synthetic Anomalies
Proposes equation-grounded taxonomy (unexpected AIS activity, route deviation, close approach) and LLM-guided synthesis pipeline to generate timestamp-labeled anomalies for evaluating maritime detection models.
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QDTraj: Exploration of Diverse Trajectory Primitives for Articulated Objects Robotic Manipulation
QDTraj uses Quality-Diversity algorithms with sparse rewards to produce at least five times more diverse high-performing trajectories for articulated object manipulation than compared methods, validated across 30 objects with hundreds of trajectories per task.
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Input-Time Scaling: Adding Noise and Irrelevance into Less-Is-More Drastically Improves Reasoning Performance and Efficiency
Adding controlled noise and irrelevant persona contexts across training and testing stages for strong LLMs yields better reasoning and efficiency than high-quality data alone, reaching 76.7% on AIME24/25 with Qwen2.5-32B.