QuantClaw dynamically routes precision in agent workflows to cut cost by up to 21.4% and latency by 15.7% while keeping or improving task performance.
arXiv preprint arXiv:2601.09527 , eprint=
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Expert re-annotations of a German ABSA dataset serve as ground truth to evaluate how students, crowdworkers, and LLMs affect inter-annotator agreement and downstream performance on ACSA and TASD tasks using BERT, T5, and LLaMA models.
citing papers explorer
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QuantClaw: Precision Where It Matters for OpenClaw
QuantClaw dynamically routes precision in agent workflows to cut cost by up to 21.4% and latency by 15.7% while keeping or improving task performance.
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Annotation Quality in Aspect-Based Sentiment Analysis: A Case Study Comparing Experts, Students, Crowdworkers, and Large Language Model
Expert re-annotations of a German ABSA dataset serve as ground truth to evaluate how students, crowdworkers, and LLMs affect inter-annotator agreement and downstream performance on ACSA and TASD tasks using BERT, T5, and LLaMA models.