3 citations · 4 across the 15 of their papers we have counts for
7 papers · 1 filter
Self-Spec Verifiable Code Generation
Jiaru Qian, Yihong Dong, Yongmin Li +3
Large language models (LLMs) may generate unreliable code on corner cases missed by testing, while formal verification can provide machine-checkable guarantees. Recently, researche…
PRAXIS: Graph-Grounded Tacit Knowledge for Domain Code Generation
Xue Jiang, Tianyu Zhang, Lingwei Wu +7
LLM agents have achieved strong performance on general software engineering tasks, yet struggle with domain-specific code generation. We identify the root cause as the agent's lack…
Multi-User Large Language Model Agents
Shu Yang, Shenzhe Zhu, Hao Zhu +5
Large language models (LLMs) and LLM-based agents are increasingly deployed as assistants in planning and decision making, yet most existing systems are implicitly optimized for a…
On Safety Risks in Experience-Driven Self-Evolving Agents
Weixiang Zhao, Yichen Zhang, Yingshuo Wang +8
Experience-driven self-evolution has emerged as a promising paradigm for improving the autonomy of large language model agents, yet its reliance on self-curated experience introduc…
Structural Evaluation Metrics for SVG Generation via Leave-One-Out Analysis
Haonan Zhu, Adrienne Deganutti, Elad Hirsch +1
SVG generation is typically evaluated by comparing rendered outputs to reference images, which captures visual similarity but not the structural properties that make SVG editable,…
Do Transformers Have the Ability for Periodicity Generalization?
Huanyu Liu, Ge Li, Yihong Dong +7
Large language models (LLMs) based on the Transformer have demonstrated strong performance across diverse tasks. However, current models still exhibit substantial limitations in ou…
KOCO-BENCH: Can Large Language Models Leverage Domain Knowledge in Software Development?
Xue Jiang, Ge Li, Jiaru Qian +12
Large language models (LLMs) excel at general programming but struggle with domain-specific software development, necessitating domain specialization methods for LLMs to learn and…