2 citations · 2 across the 10 of their papers we have counts for
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cs.AI2026
Test-Time Confidence Adaptation under RL-Induced Trajectory Shift
Zhenzhen Ren, Xiaoqing Zhang, GuoBiao Li +4
Trajectory-based confidence estimation has shown promising performance for large language models (LLMs) by leveraging rich signals from their reasoning processes. However, we find…
cs.AI2026
ToolWeaver: Weaving Collaborative Semantics for Scalable Tool Use in Large Language Models
Bowen Fang, Wen Ye, Yunyue Su +8
Prevalent retrieval-based tool-use pipelines struggle with a dual semantic challenge: their retrievers often employ encoders that fail to capture complex semantics, while the Large…
cs.AI2023★ 2 cited
Confucius: Iterative Tool Learning from Introspection Feedback by Easy-to-Difficult Curriculum
Shen Gao, Zhengliang Shi, Minghang Zhu +6
Augmenting large language models (LLMs) with external tools has emerged as a promising approach to extending the capability of LLMs. Although some works employ open-source LLMs for…