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Jia Li

Tsinghua University

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#artificial intelligence Preprint Oct 2026

SWE-DeceptionBench: Benchmarking Deceptive Behaviors of LLMs Across the Software Development Lifecycle

Large Language Models (LLMs) are increasingly used throughout the software development lifecycle. This growing reliance on LLMs raises concerns about their trustworthiness, with deception posing a particularly important risk. Deceptive behavior takes the form of misleading responses or assistance in fabricating, concea...

Xi-Ting Liu, Lin Yang, Yi-Tong Zhang et al. · 0 citations
Open access Oct 2026

PackMonitor: Towards Zero Package Hallucinations via Decoding-Time Monitoring

As Large Language Models (LLMs) are increasingly integrated into software development workflows, their trustworthiness has become a critical concern. However, in dependency recommendation scenarios, the reliability of LLMs is undermined by widespread package hallucinations, where models often recommend hallucinated pac...

Xi-Ting Liu, Yue-Tong Liu, Yi-Tong Zhang et al. · 0 citations
#natural language process... Preprint Sep 2025

DiffuTester: Accelerating Unit Test Generation for Diffusion LLMs via Mining Structural Pattern

DiffuTester employs a novel structural pattern based decoding approach, which dynamically identifies structural patterns across unit tests through their abstract syntax trees and additionally decodes the corresponding tokens, thereby achieving acceleration without compromising the quality of the output.

Le Yang, Yue-Tong Liu, Yi-Tong Zhang et al. · 3 citations

To See is Not to Master: Teaching LLMs to Use Private Libraries for Code Generation

PriCoder is proposed, an approach that teaches LLMs to invoke private-library APIs through automatically synthesized data, and alternates between two graph operators that improves data diversity by progressively synthesizing more diverse training samples from basic ones.

Yi-Tong Zhang, Cheng-Ze Li, Rui Chen et al. · 7 citations
Preprint Jul 2026

CoACT: Action-Preserving Observation Compression for Coding Agents

CoACT is built on next-action preservation (NAP), which requires a compressed observation to induce the same next action as the raw observation, and uses an action-preservation reward based on NAP to filter out candidates that would change the agent's next action.

Hao Chen, Yuancheng Zhu, Yitong Zhang et al. · 1 citation

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