17 papers
Empirical Computation: Prompting versus Programming
Eric Tang, Jing Liu, Marcel Böhme
Large Language Models (LLM) can solve *any* computational problem *without* an algorithm in a runtime *independent* of the computational complexity of that problem. Instead of spec…
Evaluating LLM-Based Regression Test Generation
Jing Liu, Seongmin Lee, Eleonora Losiouk +1
Large Language Models (LLMs) have shown tremendous promise in automated software engineering. In this paper, we investigate LLMs for just-in-time regression test generation for pro…
PIGEON: VLM-Driven Object Navigation via Points of Interest Selection
Cheng Peng, Zhenzhe Zhang, Xiaobao Wei +7
Object navigation in unseen indoor environments requires agents to perform semantic search under partial observability. Vision-language models (VLMs) provide strong semantic-spatia…
UAOR: Uncertainty-aware Observation Reinjection for Vision-Language-Action Models
Jiabing Yang, Yixiang Chen, Yuan Xu +14
Vision-Language-Action (VLA) models leverage pretrained Vision-Language Models (VLMs) as backbones to map images and instructions to actions, demonstrating remarkable potential for…
MEnvAgent: Scalable Polyglot Environment Construction for Verifiable Software Engineering
Chuanzhe Guo, Jingjing Wu, Sijun He +10
The evolution of Large Language Model (LLM) agents for software engineering (SWE) is constrained by the scarcity of verifiable datasets, a bottleneck stemming from the complexity o…
Activation-Informed Pareto-Guided Low-Rank Compression for Efficient LLM/VLM
Ryan Solgi, Parsa Madinei, Jiayi Tian +4
Large language models (LLM) and vision-language models (VLM) have achieved state-of-the-art performance, but they impose significant memory and computing challenges in deployment.…