Papers by Yize Cheng
Schoenfeld’s Anatomy of Mathematical Reasoning by Language Models (2026.acl-long)
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| Challenge: | Large language models expose reasoning traces, yet their underlying cognitive structure and steps remain difficult to identify and analyze beyond surface-level statistics. |
| Approach: | They propose a framework that explicitly abstracts reasoning traces into functional reasoning steps such as Analysis, Explore, Implement, Verify, etc. |
| Outcome: | The proposed framework reveals reproducible thinking dynamics and structural differences between reasoning and non-reasoning models, which are not apparent from token-level views. |
Tool Preferences in Agentic LLMs are Unreliable (2025.emnlp-main)
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Kazem Faghih, Wenxiao Wang, Yize Cheng, Siddhant Bharti, Gaurang Sriramanan, Sriram Balasubramanian, Parsa Hosseini, Soheil Feizi
| Challenge: | Large language models (LLMs) can now access a wide range of external tools thanks to the Model Context Protocol (MCP). |
| Approach: | They expose a vulnerability in prevalent tool/function-calling protocols by editing tool descriptions to find out which tools are used by LLMs. |
| Outcome: | The proposed changes in the tool descriptions can increase the usage of tools from LLMs when competing with alternatives. |
DyePack: Provably Flagging Test Set Contamination in LLMs Using Backdoors (2025.emnlp-main)
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| Challenge: | Open benchmarks are essential for evaluating large language models, but their accessibility makes them likely targets of test set contamination. |
| Approach: | They propose a framework that leverages backdoor attacks to flag models that used benchmark test sets during training. |
| Outcome: | The proposed framework detects models that trained on benchmark test sets without loss of logits or internal details . it can prevent false accusations while providing strong evidence for every detected case of contamination. |
Your LLM Agents are Temporally Blind: The Misalignment Between Tool Use Decisions and Human Time Perception (2026.findings-acl)
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Yize Cheng, Arshia Soltani Moakhar, Chenrui Fan, Parsa Hosseini, Kazem Faghih, Zahra Sodagar, Wenxiao Wang, Soheil Feizi
| Challenge: | Large language model agents assume a stationary context, failing to account for real-world time elapsed between messages. |
| Approach: | They construct a dataset of multi-turn user–agent message trajectories across 76 scenarios . they collect human preferences between "calling a tool" and "directly answering" they also examine whether existing models lack human temporal perception . |
| Outcome: | The results show that existing models display poor alignment with human temporal perception . the findings provide insights to foster the development of more time-aware and human-aligned agents. |