Tootfinder

GALA: Can Graph-Augmented Large Language Model Agentic Workflows Elevate Root Cause Analysis?
Yifang Tian, Yaming Liu, Zichun Chong, Zihang Huang, Hans-Arno Jacobsen
https://arxiv.org/abs/2508.12472

GALA: Can Graph-Augmented Large Language Model Agentic Workflows Elevate Root Cause Analysis?
Root cause analysis (RCA) in microservice systems is challenging, requiring on-call engineers to rapidly diagnose failures across heterogeneous telemetry such as metrics, logs, and traces. Traditional RCA methods often focus on single modalities or merely rank suspect services, falling short of providing actionable diagnostic insights with remediation guidance. This paper introduces GALA, a novel multi-modal framework that combines statistical causal inference with LLM-driven iterative reasonin…

GenTune: Toward Traceable Prompts to Improve Controllability of Image Refinement in Environment Design
Wen-Fan Wang, Ting-Ying Lee, Chien-Ting Lu, Che-Wei Hsu, Nil Ponsa Campany, Yu Chen, Mike Y. Chen, Bing-Yu Chen
https://arxiv.org/abs/2508.15227

GenTune: Toward Traceable Prompts to Improve Controllability of Image Refinement in Environment Design
Environment designers in the entertainment industry create imaginative 2D and 3D scenes for games, films, and television, requiring both fine-grained control of specific details and consistent global coherence. Designers have increasingly integrated generative AI into their workflows, often relying on large language models (LLMs) to expand user prompts for text-to-image generation, then iteratively refining those prompts and applying inpainting. However, our formative study with 10 designers su…

Tensor Invariant Data-Assisted Control and Dynamic Decomposition of Multibody Systems
Mostafa Eslami, Maryam Babazadeh
https://arxiv.org/abs/2509.11688 https://

Tensor Invariant Data-Assisted Control and Dynamic Decomposition of Multibody Systems
The control of robotic systems in complex, shared collaborative workspaces presents significant challenges in achieving robust performance and safety when learning from experienced or simulated data is employed in the pipeline. A primary bottleneck is the reliance on coordinate-dependent models, which leads to profound data inefficiency by failing to generalize physical interactions across different frames of reference. This forces learning algorithms to rediscover fundamental physical principl…