Loom框架通过嵌入空间重加权技术，在保持高准确率的同时大幅提升推理速度，适合工业场景的根因分析任务。
Loom是一个生成式共识框架，用于解决工业环境中NLP系统整合嘈杂、冲突文本假设的挑战。该框架将模块化启发式方法生成的开放形式假设投影到连续嵌入空间，并通过迭代基于质心的重加权算法解决冲突信号。在OpenRCA基准测试中，Loom在准确率-效率帕累托前沿上表现出色，仅需单次LLM调用即可实现约26倍的速度提升。
Loom: Weaving Diagnostic Strands into Free-Text Consensus via Embedding-Space Reweighting
Aggregating noisy, conflicting textual hypotheses into a reliable consensus is a fundamental challenge when deploying NLP systems in real-world industrial settings. While monolithic Large Language Model (LLM) agents offer unbounded expressivity for tasks like Root Cause Analysis (RCA), they suffer from context limits, compounding hallucinations, and prohibitive inference latency. Traditional weak supervision offers statistical rigor but is mathematically restricted to discrete classes. We present Loom, a generative consensus framework deployed for real-world RCA that bridges these paradigms. Loom aggregates open-form hypotheses emitted by modular heuristics (diagnostic templates dynamically populated with episode-specific entities, times, and metrics) by projecting them into a continuous embedding space, and resolves conflicting signals with an iterative centroid-based reweighting algorithm. The resulting consensus weights ground a single lightweight LLM synthesis step. Evaluated on the OpenRCA benchmark, Loom occupies the accuracy--efficiency Pareto frontier: it matches a state-of-the-art autonomous agent on Bank and Market-2 and trails on Market-1 and Telecom, while using a single LLM call per incident on all four datasets ($\sim$26$\times$ faster; $\sim$33$\times$ with an 8B-parameter synthesizer). We discuss our deployment experience, highlighting lessons learned regarding the trade-offs between agentic depth and inference latency, negative results in redundancy detection, and how deterministic consensus fosters trust among Subject Matter Experts~(SMEs).