Quanyu Long - RankMe

31 papers A* 4Journal 22Unranked 5

Year	Rank	Type	Title / Venue / Authors
2026	J	`jnl`	A causal saliency enhancement and Mamba-based multi-scale feature fusion encoding framework for railway fastener fault diagnosis. Adv. Eng. Informatics Quanyu Long, Dechen Yao, Jianwei Yang, Jia Dong, Bin Zhu, Yuanting Dai
2026	A*	`conf`	Causality Matters: How Temporal Information Emerges in Video Language Models. AAAI Yumeng Shi, Quanyu Long, Yin Wu, Wenya Wang
2026	J	`jnl`	Learning Query-Aware Budget-Tier Routing for Runtime Agent Memory. CoRR Haozhen Zhang, Haodong Yue, Tao Feng, Quanyu Long, Jianzhu Bao, Bowen Jin, Weizhi Zhang, Xiao Li, Jiaxuan You, Chengwei Qin, Wenya Wang
2026	J	`jnl`	MemSkill: Learning and Evolving Memory Skills for Self-Evolving Agents. CoRR Haozhen Zhang, Quanyu Long, Jianzhu Bao, Tao Feng, Weizhi Zhang, Haodong Yue, Wenya Wang
2026	J	`jnl`	Programming over Thinking: Efficient and Robust Multi-Constraint Planning. CoRR Derrick Goh Xin Deik, Quanyu Long, Zhengyuan Liu, Nancy F. Chen, Wenya Wang
2026	J	`jnl`	Self-Verification Dilemma: Experience-Driven Suppression of Overused Checking in LLM Reasoning. CoRR Quanyu Long, Kai Jie Jiang, Jianda Chen, Xu Guo, Leilei Gan, Wenya Wang
2025	J	`jnl`	BOOST: Bootstrapping Strategy-Driven Reasoning Programs for Program-Guided Fact-Checking. CoRR Qisheng Hu, Quanyu Long, Wenya Wang
2025	J	`jnl`	Causality Matters: How Temporal Information Emerges in Video Language Models. CoRR Yumeng Shi, Quanyu Long, Yin Wu, Wenya Wang
2025	J	`jnl`	Coordinating Search-Informed Reasoning and Reasoning-Guided Search in Claim Verification. CoRR Qisheng Hu, Quanyu Long, Wenya Wang
2025	—	`conf`	Decomposition Dilemmas: Does Claim Decomposition Boost or Burden Fact-Checking Performance? NAACL (Long Papers) Qisheng Hu, Quanyu Long, Wenya Wang
2025	J	`jnl`	From Competition to Synergy: Unlocking Reinforcement Learning for Subject-Driven Image Generation. CoRR Ziwei Huang, Ying Shu, Hao Fang, Quanyu Long, Wenya Wang, Qiushi Guo, Tiezheng Ge, Leilei Gan
2025	—	`conf`	Reinforcing Compositional Retrieval: Retrieving Step-by-Step for Composing Informative Contexts. ACL (Findings) Quanyu Long, Jianda Chen, Zhengyuan Liu, Nancy F. Chen, Wenya Wang, Sinno Jialin Pan
2025	J	`jnl`	Reinforcing Compositional Retrieval: Retrieving Step-by-Step for Composing Informative Contexts. CoRR Quanyu Long, Jianda Chen, Zhengyuan Liu, Nancy F. Chen, Wenya Wang, Sinno Jialin Pan
2025	A*	`conf`	Static or Dynamic: Towards Query-Adaptive Token Selection for Video Question Answering. EMNLP Yumeng Shi, Quanyu Long, Wenya Wang
2025	J	`jnl`	Static or Dynamic: Towards Query-Adaptive Token Selection for Video Question Answering. CoRR Yumeng Shi, Quanyu Long, Wenya Wang
2025	—	`conf`	T2I-FactualBench: Benchmarking the Factuality of Text-to-Image Models with Knowledge-Intensive Concepts. ACL (1) Ziwei Huang, Wanggui He, Quanyu Long, Yandi Wang, Haoyuan Li, Zhelun Yu, Fangxun Shu, Weilong Dai, Hao Jiang, Fei Wu, Leilei Gan
2025	J	`jnl`	Visual-RAG: Benchmarking Text-to-Image Retrieval Augmented Generation for Visual Knowledge Intensive Queries. CoRR Yin Wu, Quanyu Long, Jing Li, Jianfei Yu, Wenya Wang
2024	J	`jnl`	Backdoor Attacks on Dense Passage Retrievers for Disseminating Misinformation. CoRR Quanyu Long, Yue Deng, Leilei Gan, Wenya Wang, Sinno Jialin Pan
2024	J	`jnl`	Decomposition Dilemmas: Does Claim Decomposition Boost or Burden Fact-Checking Performance? CoRR Qisheng Hu, Quanyu Long, Wenya Wang
2024	J	`jnl`	Does In-Context Learning Really Learn? Rethinking How Large Language Models Respond and Solve Tasks via In-Context Learning. CoRR Quanyu Long, Yin Wu, Wenya Wang, Sinno Jialin Pan
2024	J	`jnl`	Large Language Models Know What Makes Exemplary Contexts. CoRR Quanyu Long, Jianda Chen, Wenya Wang, Sinno Jialin Pan
2024	J	`jnl`	T2I-FactualBench: Benchmarking the Factuality of Text-to-Image Models with Knowledge-Intensive Concepts. CoRR Ziwei Huang, Wanggui He, Quanyu Long, Yandi Wang, Haoyuan Li, Zhelun Yu, Fangxun Shu, Long Chan, Hao Jiang, Leilei Gan, Fei Wu
2023	A*	`conf`	Adapt in Contexts: Retrieval-Augmented Domain Adaptation via In-Context Learning. EMNLP Quanyu Long, Wenya Wang, Sinno Jialin Pan
2023	J	`jnl`	Adapt in Contexts: Retrieval-Augmented Domain Adaptation via In-Context Learning. CoRR Quanyu Long, Wenya Wang, Sinno Jialin Pan
2022	—	`conf`	Domain Confused Contrastive Learning for Unsupervised Domain Adaptation. NAACL-HLT Quanyu Long, Tianze Luo, Wenya Wang, Sinno Jialin Pan
2022	J	`jnl`	Domain Confused Contrastive Learning for Unsupervised Domain Adaptation. CoRR Quanyu Long, Tianze Luo, Wenya Wang, Sinno Jialin Pan
2021	—	`conf`	Generative Imagination Elevates Machine Translation. NAACL-HLT Quanyu Long, Mingxuan Wang, Lei Li
2020	J	`jnl`	Generative Imagination Elevates Machine Translation. CoRR Quanyu Long, Mingxuan Wang, Lei Li
2020	A*	`conf`	On the Robustness of Language Encoders against Grammatical Errors. ACL Fan Yin, Quanyu Long, Tao Meng, Kai-Wei Chang
2020	J	`jnl`	On the Robustness of Language Encoders against Grammatical Errors. CoRR Fan Yin, Quanyu Long, Tao Meng, Kai-Wei Chang
2020	J	`jnl`	QA4IE: A Question Answering Based System for Document-Level General Information Extraction. IEEE Access Lin Qiu, Dongyu Ru, Quanyu Long, Weinan Zhang, Yong Yu

Nicolò Altamura is a security engineer and computer science researcher specializing in software protection, reverse engineering, program analysis, and binary deobfuscation. He holds a master's degree in Computer Science and Engineering from the University of Verona, graduating with honors, and works at emproof, focusing on software protection and the challenges of analyzing and understanding compiled software.

His research explores the relationship between program analysis and code obfuscation, with particular emphasis on how software transformations affect the ability of automated tools to recover program behavior. His interests span static analysis, binary analysis, decompilation, reverse engineering, and techniques for analyzing and simplifying obfuscated code.

Beyond academic research, Nicolò develops and contributes to tools for reverse engineering and software analysis. His projects include obfuscation_analysis, a Binary Ninja plugin for analyzing and simplifying obfuscated code; Pocket, an obfuscator for mixed Boolean-arithmetic expressions; and ptxNinja, a tool for reverse engineering NVIDIA GPU code represented in the PTX virtual instruction set.

His research also extends to binary datasets and software evolution. In 2026, he co-authored ASSEMBLAGE-DEEPHISTORY: A Cross-Build Binary Dataset with Temporal Coverage, a research work addressing the availability of binary data across multiple software builds and points in time, supporting research into binary analysis and changes between program versions.

Across his research, engineering work, and open-source contributions, Nicolò focuses on advancing the understanding, analysis, and protection of software against increasingly sophisticated obfuscation and reverse-engineering challenges.

Nicolò Altamura is not a football player. Please avoid reporting this kind of infromation.