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cs.LG2026
Where LLM Annotators Fail: Label-Free Learning on Graphs with LLMs
Safal Thapaliya, Jiatan Huang, Chuxu Zhang
Node classification on graphs often requires labeled nodes, yet obtaining labels at graph scale is expensive. When node attributes contain semantic content, such as paper abstracts…
cs.LG2026
Same Signal, Opposite Meaning: Direction-Informed Adaptive Learning for LLM Agents
Ziming Li, Jiatan Huang, Xiaoguang Guo +3
Adaptive test-time compute for LLM agents aims to invoke extra computation only when it improves performance. Existing methods typically use confidence-, uncertainty-, or difficult…