1 citations · 1 across the 6 of their papers we have counts for
12 papers
Stateful Testing for Deep Learning Compilers
Jiatao Ge, Ruixiang Qian, Yibo Hu +5
Modern deep learning compilers compile models lazily and cache artifacts across invocations. Retained compiler state can make a later invocation incorrect even when it is correct i…
Grounding Deep Learning Framework Testing in Data Flow
Jiatao Ge, Yanzhou Mu, Yinglong Zou +3
Data flow underlies deep learning (DL) framework execution, including automatic differentiation and operator-fusion optimization. Bugs involving data flow can cause incorrect resul…
CacheReforge: Bounded Recovery for Stale KV Caches under Evolving Adapters
Yuhang Cao, Yanzhou Mu, Chunrong Fang +1
Large language models rely on KV caching to reduce repeated prefill computation in long context and interactive applications. As lightweight adapters evolve, cached states reflect…
Tactile-based Multimodal Fusion in Embodied Intelligence: A Survey of Vision, Language, and Contact-Driven Paradigms
Zhixiang Cao, Di Tian, Runwei Guan +11
Tactile sensing is a fundamental modality for embodied intelligence, offering unique and direct feedback on contact geometry, material properties, and interaction dynamics that rem…
Train in Vain: Functionality-Preserving Poisoning to Prevent Unauthorized Use of Code Datasets
Yuan Xiao, Jiaming Wang, Yuchen Chen +8
The widespread availability of large-scale code datasets has accelerated the development of code large language models (CodeLLMs), raising concerns about unauthorized dataset usage…
Where Agent Frameworks Fall Short: Examining Functional Challenges and Usability Concerns
Xinxue Zhu, Yanzhou Mu, Xiaoyu Zhang +5
Large language model (LLM) agents are increasingly built on agent frameworks that provide reusable abstractions for workflow orchestration, state management, tool integration, and…
When Autonomous Vehicle Meets V2X Cooperative Perception: How Far Are We?
An Guo, Shuoxiao Zhang, Enyi Tang +7
With the tremendous advancement of deep learning and communication technology, Vehicle-to-Everything (V2X) cooperative perception has the potential to address limitations in sensin…
Deep Learning Framework Testing via Model Mutation: How Far Are We?
Yanzhou Mu, Rong Wang, Juan Zhai +7
Deep Learning (DL) frameworks are a fundamental component of DL development. Therefore, the detection of DL framework defects is important and challenging. As one of the most widel…
Improving Deep Learning Framework Testing with Model-Level Metamorphic Testing
Yanzhou Mu, Juan Zhai, Chunrong Fang +6
Deep learning (DL) frameworks are essential to DL-based software systems, and framework bugs may lead to substantial disasters, thus requiring effective testing. Researchers adopt…
Deep Learning Framework Testing via Heuristic Guidance Based on Multiple Model Measurements
Yinglong Zou, Juan Zhai, Chunrong Fang +3
Deep learning frameworks serve as the foundation for developing and deploying deep learning applications. To enhance the quality of deep learning frameworks, researchers have propo…
Generate Realistic Test Scenes for V2X Communication Systems
An Guo, Xinyu Gao, Chunrong Fang +6
Accurately perceiving complex driving environments is essential for ensuring the safe operation of autonomous vehicles. With the tremendous progress in deep learning and communicat…
Understanding LLM-Centric Challenges for Deep Learning Frameworks: An Empirical Analysis
Yanzhou Mu, Rong Wang, Juan Zhai +7
Large language models (LLMs) have driven significant progress across a wide range of real-world applications. Realizing such models requires substantial system-level support. Deep…