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Yu-He Wu

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#artificial intelligence Preprint Sep 2026

Count Evidence, Not Sentences: Tempered Evidence Fusion of LLM Judgments for Long-Text Value Measurement

Tempered Evidence Fusion (TEF) is proposed, a training-free rule that weights each sentence's log-odds by its normalized information gain, as derived from a generalized Bayesian posterior, making the fused score nearly vanish for uncertain sentences while preserving the Bayes-optimal weight of decisive evidence.

Yu-He Wu, Rui Qian, Guang-Yu Wang et al. · 0 citations

BizSage: A Self-Evolving Multi-Agent Framework for Business Research with Efficient Knowledge Retrieval

BizSage is presented, a multi-agent framework combining corpus-level fine-grained retrieval with quality-driven self-evolution that paves the way for reliable research assistance in economics, business, and the broader social sciences.

Yu-He Wu, Guang-Yu Wang, Jia-Xin Liu et al. · 0 citations
#artificial intelligence Preprint Sep 2026

Caught in the Story: Narrative Captivity in Multi-turn LLMs Conversation

People increasingly turn to large language models (LLMs) for everyday advice, making ethically charged interpersonal problems a practical moral-advisory context. Most prior work has studied this context through single-turn judgments or pressure-laden rebuttals, assumptions that poorly match how guidance is sought in re...

Yu-He Wu, Guang-Yu Wang, Yu-Jie Chen et al. · 1 citation

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