This work recast the affective analysis task as a complex-reasoning problem, which yields one output interface across heterogeneous label spaces and a trajectory over which a verifiable reward can be optimised; to the authors' knowledge, this is the first such treatment covering both sentiment and emotion.
Tianlei Zhu, Zhiwei Liu, Yu-Yan Wang et al.· 0 citations
This work presents the first systematic meta-evaluation of LLM-generated rubrics intrinsically by semantic similarity and extrinsically by score alignment with ground-truth rubrics, and meta-evaluate generated rubrics intrinsically and extrinsically by score alignment with ground-truth rubrics.
Simulations show that over-reliance on a weak AI is especially harmful, and that diversifying AI signals across users can better keep the crowd informative, and conclude with implications for understanding human-AI interaction in information spread and designing misinformation interventions.
Zhuo-Ran Lu, Wei-Long Wang, Yang-Yang Yu et al.· Proceedings of the 2026 ACM...· 0 citations
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