Papers by Xuhong Wang
CP-BCS: Binary Code Summarization Guided by Control Flow Graph and Pseudo Code (2023.emnlp-main)
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| Challenge: | Current work on understanding assembly code is oriented towards generating function names, which involve numerous abbreviations that make them confusing. |
| Approach: | They propose a control flow graph and pseudo code guided binary code summarization framework to learn the comprehensive binary function execution behavior and logic semantics. |
| Outcome: | The proposed framework improves the efficiency of reverse engineering on 3 different binary optimization levels for 3 different computer architectures. |
Decoupled Reasoning with Implicit Fact Tokens (DRIFT): A Dual-Model Framework for Efficient Long-Context Inference (2026.findings-acl)
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| Challenge: | Existing solutions to integrate extensive, dynamic knowledge into Large Language Models (LLMs) are constrained by finite context windows, retriever noise, or the risk of catastrophic forgetting. |
| Approach: | They propose a dual-model architecture that explicitly decouples knowledge extraction from the reasoning process by compressing document chunks into implicit fact tokens conditioned on the query. |
| Outcome: | The proposed architecture significantly outperforms strong baselines among comparably sized models on long-context tasks while maintaining inference accuracy. |
Deliberative Searcher: Improving LLM Reliability via Reinforcement Learning with Constraints (2026.acl-long)
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| Challenge: | Large language models with search capabilities often exhibit miscalibrated confidence, causing incorrect answers with high certainty. |
| Approach: | They propose a reasoning-primary framework that integrates search operations into chain-of-thought generation while maintaining explicit confidence calibration. |
| Outcome: | The proposed framework improves accuracy and reliability of large language models with search capabilities. |
Tram: A Token-level Retrieval-augmented Mechanism for Source Code Summarization (2024.findings-naacl)
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| Challenge: | Existing methods to generate source code summaries are coarse-grained and noise-filled . however, they do not capture contextual code semantics and are often outdated in continuous software iteration. |
| Approach: | They propose a fine-grained Token-level retrieval-augmented mechanism on the decoder side to enhance performance of neural models. |
| Outcome: | The proposed method produces more low-frequency tokens and is interpretable. |
From Coarse to Fine: Benchmarking and Reward Modeling for Writing-Centric Generation Tasks (2026.findings-acl)
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| Challenge: | Existing evaluation benchmarks for writing reward models are coarse-grained. |
| Approach: | They propose a benchmark and a fine-grained training framework to evaluate writing reward models. |
| Outcome: | The proposed model improves on various writing benchmarks and exhibits strong generalization. |
NaviMaster: Learning a Unified Policy for GUI and Embodied Navigation Tasks (2026.acl-long)
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| Challenge: | Recent advances in Graphical User Interface (GUI) and embodied navigation have driven progress, yet these domains have largely evolved in isolation, with disparate datasets and training paradigms. |
| Approach: | They propose a visual-target trajectory collection pipeline that generates trajectories for GUI and embodied tasks using a single formulation. |
| Outcome: | The proposed agent outperforms state-of-the-art agents in GUI navigation, spatial affordance prediction, and embodied navigation. |
ToolGate: Contract-Grounded and Verified Tool Execution for LLMs (2026.findings-acl)
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Yanming Liu, Xinyue Peng, Jiannan Cao, Xinyi Wang, Songhang Deng, Jintao Chen, Jianwei Yin, Xuhong Zhang
| Challenge: | Existing frameworks for tool-augmented LLMs rely heavily on natural language reasoning to determine when tools can be invoked and whether their results should be trusted. |
| Approach: | They propose a forward execution framework that provides logical safety guarantees and verifiable state evolution for LLM tool calling. |
| Outcome: | The proposed framework improves the reliability and verifiability of tool-augmented LLM systems while maintaining competitive performance on multi-step reasoning tasks. |
Beyond Correctness: Confidence-Aware Reward Modeling for Enhancing Large Language Model Reasoning (2025.emnlp-main)
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| Challenge: | Recent advances in large language models have shifted the post-training paradigm from instruction tuning and human preference alignment to reinforcement learning (RL) based on rule-based evaluations of answer correctness, these models often receive rewards for speculative answers without generating coherent reasoning chains. |
| Approach: | They propose a confidence-based reward model tailored for enhancing STEM reasoning capabilities. |
| Outcome: | The proposed model outperforms state-of-the-art open-source reward models across diverse STEM benchmarks. |
SecCoder: Towards Generalizable and Robust Secure Code Generation (2024.emnlp-main)
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| Challenge: | Existing secure code generation methods have limited generalizability to unseen test cases and poor robustness against the attacked model, leading to safety failures in code generation. |
| Approach: | They propose a generalizable and robust secure code generation method SecCoder by using in-context learning and the safe demonstration. |
| Outcome: | The proposed method achieves a significant security improvement of 7.20% on unseen test cases and better robustness against the attacked model. |