Hannah Lerner

Yiwen Zhu, Joyce Cahoon, Anna Pavlenko et al.

Complex operational workflows coordinating personnel, tools, and information are central to enterprise operations, yet end-to-end automation remains challenging due to extensive requirements for human inputs and the inability to adapt over time. We present GraphMind, an end-to-end system that constructs, executes, and evolves action-centric workflow graphs without human effort. The system operates in three phases. First, a scalable offline pipeline extracts structured workflow graphs from large volumes of human resolution traces, capturing problems, actions, and their causal relationships. Second, an online multi-agent traversal engine navigates the graph to dynamically construct and execute workflows, combining graph-guided retrieval with LLM-driven reasoning at each step. Third, Adaptive Traversal Reinforcement (ATR) reinforces successful traversal paths and decays stale elements. This closed-loop mechanism enables the graph to self-optimize and adapt to shifting operational conditions. GraphMind has been deployed across four production cloud database services for incident investigation. Evaluated on production data, the system substantially outperforms a Trace-RAG baseline in mitigation reach, groundedness, and diagnostic throughput, scoring 4.95/5 in blind expert review. The ATR layer provides further gains across all metrics, demonstrating that workflow graphs can learn and improve from execution-derived feedback.

Yiwen Zhu, Mathieu Demarne, Kai Deng et al.

Software engineers frequently grapple with the challenge of accessing disparate documentation and telemetry data, including TroubleShooting Guides (TSGs), incident reports, code repositories, and various internal tools developed by multiple stakeholders. While on-call duties are inevitable, incident resolution becomes even more daunting due to the obscurity of legacy sources and the pressures of strict time constraints. To enhance the efficiency of on-call engineers (OCEs) and streamline their daily workflows, we introduced DECO-a comprehensive framework for developing, deploying, and managing enterprise-grade copilots tailored to improve productivity in engineering routines. This paper details the design and implementation of the DECO framework, emphasizing its innovative NL2SearchQuery functionality and a lightweight agentic framework. These features support efficient and customized retrieval-augmented-generation (RAG) algorithms that not only extract relevant information from diverse sources but also select the most pertinent skills in response to user queries. This enables the addressing of complex technical questions and provides seamless, automated access to internal resources. Additionally, DECO incorporates a robust mechanism for converting unstructured incident logs into user-friendly, structured guides, effectively bridging the documentation gap. Since its launch in September 2023, DECO has demonstrated its effectiveness through widespread adoption, enabling tens of thousands of interactions and engaging hundreds of monthly active users (MAU) across dozens of organizations within the company.

Dhruvil Gala, Mohammad Omar Khursheed, Hannah Lerner et al.

Popular media reflects and reinforces societal biases through the use of tropes, which are narrative elements, such as archetypal characters and plot arcs, that occur frequently across media. In this paper, we specifically investigate gender bias within a large collection of tropes. To enable our study, we crawl tvtropes.org, an online user-created repository that contains 30K tropes associated with 1.9M examples of their occurrences across film, television, and literature. We automatically score the "genderedness" of each trope in our TVTROPES dataset, which enables an analysis of (1) highly-gendered topics within tropes, (2) the relationship between gender bias and popular reception, and (3) how the gender of a work's creator correlates with the types of tropes that they use.