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Cross-axis TIES merging outperforms traditional methods, boosting summarization scores by up to 7 chrF points across multiple languages.
LLM agent reliability metrics hide a wealth of information: modeling execution traces as Markov chains reveals the underlying success-time distribution and quantifies uncertainty, offering a richer understanding of agent behavior.
The privacy-efficiency-trust trilemma is real: current privacy-preserving architectures for IoT and vehicular data sharing can't simultaneously deliver strong privacy, computational efficiency, and decentralized trust.