Papers by Xiaoxue Ren
ExecVerify: White-Box RL with Verifiable Stepwise Rewards for Code Execution Reasoning (2026.acl-long)
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| Challenge: | Existing methods for code execution reasoning are limited by the difficulty of the training data. |
| Approach: | They propose a model that uses reinforcement learning to reward correct answers from execution traces. |
| Outcome: | The proposed model improves pass@1 by up to 5.9% on code generation tasks over strong baselines. |
Rethinking LLM Watermark Detection in Black-Box Settings: A Non-Intrusive Third-Party Framework (2026.findings-acl)
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| Challenge: | Existing secret-key schemes tightly couple detection with injection . this dependency creates a fundamental barrier for real-world governance . |
| Approach: | et al. introduce a black-box framework for non-intrusive, third-party watermark verification . they propose a proxy model to amplify watermark-relevant signals and complementary relative measurements . |
| Outcome: | a new framework decouples detection from injection and assesses alignment of query text with watermark distributions. |
CoRE: A Fine-Grained Code Reasoning Benchmark Beyond Output Prediction (2026.findings-acl)
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Jun Gao, Yun Peng, Qian Qiao, Changhai Zhou, Yuhua Zhou, Shiyang Zhang, Shichao Weng, Zhenchang Xing, Xiaoxue Ren
| Challenge: | Existing code reasoning benchmarks evaluate final output correctness under a single implementation. |
| Approach: | They propose a Code Reasoning benchmark that evaluates code reasoning through implementation invariance and process transparency. |
| Outcome: | The proposed benchmarks lack implementation invariance and process transparency . they observe superficial execution where models arrive at correct outputs without reasoning . |
The Bidirectional Process Reward Model (2026.acl-long)
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| Challenge: | Process reward models (PRMs) assign fine-grained scores to intermediate reasoning steps within a solution trajectory. |
| Approach: | They propose a bidirectional evaluation paradigm that integrates a parallel evaluation stream alongside the L2R evaluation scheme and a gating mechanism to fuse the reward scores. |
| Outcome: | The proposed model surpasses unidirectional baselines in multiple benchmarks, LLM objectives and sampling policies. |
The Dawn After the Dark: An Empirical Study on Factuality Hallucination in Large Language Models (2024.acl-long)
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| Challenge: | a growing number of researchers are studying the hallucination issue in large language models. |
| Approach: | They propose a hallucination detection benchmark and a method to detect hallucines in LLMs. |
| Outcome: | The proposed method detects hallucinations and mitigates them using different training stages. |
JumpCoder: Go Beyond Autoregressive Coder via Online Modification (2024.acl-long)
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| Challenge: | Existing code large language models lack reversibility and autoregressive sequential generation is incapable of correcting previous missing statements as humans do. |
| Approach: | They propose a model-agnostic framework that enables human-like online modification and non-sequential generation to augment code large language models. |
| Outcome: | The proposed framework enables human-like modification and non-sequential generation to augment code large language models. |