DSIPA is a zero-shot black-box detector that uses sentiment distribution consistency and preservation metrics to identify LLM text, reporting up to 49.89% F1 gains over baselines across domains and models.
arXiv preprint arXiv:2404.16038 (2024)
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DeepFleet develops and compares four foundation model architectures for multi-agent robot fleet coordination using warehouse data, finding robot-centric and graph-floor models most promising for prediction and scaling.
The paper surveys the evolution of video trailer generation from extractive heuristics to generative AI methods and proposes a new taxonomy for future systems based on autoregressive and foundation models.
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DSIPA: Detecting LLM-Generated Texts via Sentiment-Invariant Patterns Divergence Analysis
DSIPA is a zero-shot black-box detector that uses sentiment distribution consistency and preservation metrics to identify LLM text, reporting up to 49.89% F1 gains over baselines across domains and models.
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DeepFleet: Multi-Agent Foundation Models for Mobile Robots
DeepFleet develops and compares four foundation model architectures for multi-agent robot fleet coordination using warehouse data, finding robot-centric and graph-floor models most promising for prediction and scaling.
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Generative AI for Video Trailer Synthesis: From Extractive Heuristics to Autoregressive Creativity
The paper surveys the evolution of video trailer generation from extractive heuristics to generative AI methods and proposes a new taxonomy for future systems based on autoregressive and foundation models.