Papers by Zhenjiang Mao
Thesis Proposal: When Does an Agent Know It Is Lost? Confidence Trajectory Analysis for Tool-Using LLMs (2026.acl-srw)
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| Challenge: | Existing uncertainty quantification methods treat each step in isolation, ignoring how confidence evolves and compounds across a full task trajectory. |
| Approach: | They propose a framework for trajectory-level confidence analysis in the tool-use agent setting. |
| Outcome: | The proposed framework will expose early warning signals for agent failure and offer interpretable diagnostic tools for understanding when and why LLM agents lose confidence. |
Confidence over Time: Confidence Calibration with Temporal Logic for Large Language Model Reasoning (2026.findings-acl)
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Zhenjiang Mao, Anirudhh Venkat, Artem Bisliouk, Sindhura Kumbakonam Subramanian, Akshat Kothiyal, Saithej Singhu, Ivan Ruchkin
| Challenge: | Existing confidence estimation methods reduce reasoning process to a single scalar score, ignoring how confidence evolves throughout generation. |
| Approach: | They propose to characterize the stepwise confidence signal using Signal Temporal Logic (STL) based on a discriminative STL mining procedure, they find temporal formulas that distinguish correct and incorrect responses. |
| Outcome: | The proposed method can distinguish between correct and incorrect reasoning signals. |