5 citations · 6 across the 12 of their papers we have counts for
12 papers
MM-IssueLoc: A Controlled Benchmark for Evaluating Visual Evidence in Multimodal Repository-Level Issue Localization
Shaoxiong Zhan, Shi Hu, Boyu Feng +7
Real repository issues routinely include visual evidence such as screenshots, error dialogs, rendered UI states, and logs, yet repository-level issue localization is evaluated most…
Model-Native Computing Architecture: Envisioning Future System Architecture Through the Lens of Computer Architecture
Hai Lin, Hoilam Pao, Shaoxiong Zhan +1
Large language models are undergoing a transition from model technology to system technology. Engineering challenges like cache reuse, context capacity, agent scheduling, and permi…
When Hard Negatives Hurt: Bridging the Generative-Discriminative Gap in Hard Negative Synthesis for Retrieval
Zhicheng Zhang, Jiwei Tang, Kuicai Dong +9
Hard negative mining has become the dominant strategy for training retrievers, yet it faces intrinsic limitations: negatives are bounded by corpus availability, selected by retriev…
SemantiCache: Efficient KV Cache Compression via Semantic Chunking and Clustered Merging
Shunlong Wu, Hai Lin, Shaoshen Chen +5
Existing KV cache compression methods generally operate on discrete tokens or non-semantic chunks. However, such approaches often lead to semantic fragmentation, where linguistical…
3ViewSense: Spatial and Mental Perspective Reasoning from Orthographic Views in Vision-Language Models
Shaoxiong Zhan, Yanlin Lai, Zheng Liu +6
Current Large Language Models have achieved Olympiad-level logic, yet Vision-Language Models paradoxically falter on elementary spatial tasks like block counting. This capability m…
Auxiliary-Hyperparameter-Free Sampling: Entropy Equilibrium for Text Generation
Xiaodong Cai, Hai Lin, Shaoxiong Zhan +5
Token sampling strategies critically influence text generation quality in large language models (LLMs). However, existing methods introduce additional hyperparameters, requiring ex…
FEANEL: A Benchmark for Fine-Grained Error Analysis in K-12 English Writing
Jingheng Ye, Shen Wang, Jiaqi Chen +9
Large Language Models (LLMs) have transformed artificial intelligence, offering profound opportunities for educational applications. However, their ability to provide fine-grained…
LexSemBridge: Fine-Grained Dense Representation Enhancement through Token-Aware Embedding Augmentation
Shaoxiong Zhan, Hai Lin, Hongming Tan +6
As queries in retrieval-augmented generation (RAG) pipelines powered by large language models (LLMs) become increasingly complex and diverse, dense retrieval models have demonstrat…
A Hierarchical Framework for Measuring Scientific Paper Innovation via Large Language Models
Hongming Tan, Shaoxiong Zhan, Fengwei Jia +2
Measuring scientific paper innovation is both important and challenging. Existing content-based methods often overlook the full-paper context, fail to capture the full scope of inn…
IRSC: A Zero-shot Evaluation Benchmark for Information Retrieval through Semantic Comprehension in Retrieval-Augmented Generation Scenarios
Hai Lin, Shaoxiong Zhan, Junyou Su +2
In Retrieval-Augmented Generation (RAG) tasks using Large Language Models (LLMs), the quality of retrieved information is critical to the final output. This paper introduces the IR…
Perception Compressor: A Training-Free Prompt Compression Framework in Long Context Scenarios
Jiwei Tang, Jin Xu, Tingwei Lu +4
Large language models (LLMs) demonstrate exceptional capabilities in various scenarios. However, they suffer from much redundant information and are sensitive to the position of ke…
QAEA-DR: A Unified Text Augmentation Framework for Dense Retrieval
Hongming Tan, Shaoxiong Zhan, Hai Lin +2
In dense retrieval, embedding long texts into dense vectors can result in information loss, leading to inaccurate query-text matching. Additionally, low-quality texts with excessiv…