RAILS is presented, a retrieval-augmented incremental LLM clusterer that turns clustering into a simple loop over a growing label pool and scales through document batching with bounded concurrency.
Abstract
Using a Large Language Model (LLM) as the clusterer at production scale is hard: prompts cannot hold the entire label space, and per-document serial processing does not deliver the throughput real workloads require. We present RAILS, a retrieval-augmented incremental LLM clusterer that turns clustering into a simple loop over a growing label pool and scales through document batching with bounded concurrency. On six public benchmarks RAILS exceeds the strongest prior LLM-clustering method on average, lifting accuracy from 51.2% to 59.3%, NMI from 67.2% to 74.8%, and ARI from 45.4% to 54.7%. We further report production-deployment evidence from a SaaS ticket-topic-discovery pipeline, where RAILS has replaced a traditional HDBSCAN stage with higher clustering quality, transparent prompt-driven control, and stateful incremental operation.
Labeling large text corpora with LLM teachers has become a practical route to training data at scale. At millions of items, hand-labeling every batch is not feasible, and two questions dominate: what label quality a teacher buys per dollar, and how to keep a fleet of GPU workers busy under skewed, failure-prone workloa...
Serving offline large language model (LLM) inference workloads (e.g., log summarization and bulk translation) can consume up to 30% of GPUs in production. Despite this significant share, the characteristics of offline inference remain largely understudied. In this paper, we start by analyzing 1.5 million tasks comprisi...
Le-Ping Yang, Xue Li, Kun Qian et al.· Proceedings of the ACM SIGOP...· 0 citations
This paper measures and analytically derive an optimal size for in-prompt document batching to effectively amortize this overhead of LLM calls over public APIs, cutting end-to-end semantic operator latency by up to 14 × with no meaningful accuracy loss.
LLMVisor is presented, a roofline-guided latency attribution model that captures the memory-bound and compute-bound phases via a concise piecewise-linear form over features proportional to FLOPs and memory I/O traffic and runs efficiently at microsecond scale.
Shuowei Jin, Xue-Shen Liu, Jiaxin Shan et al.· 3 citations
LMTracer is presented, a fine-grained and real-time performance profiling framework for production LLM services that embed profiling logic into the execution through graph-embedded probing and streaming buffered profiling data to CPUs on demand to keep the execution of user kernels uninterrupted.
Wei Liu, Yong-Chao He, Bo-Han Zhao et al.· Proceedings of the ACM SIGOP...· 0 citations
Operational logs create a need for private, resource-efficient incident analysis, but aggregate detection scores can conceal severe prediction bias. We present TriCalRAG, a reproducible benchmark for log-anomaly detection with generated root-cause and remediation outputs across BGL, HDFS, Thunderbird, and OpenStack. Th...
Rohit Patel, S. K. Mohanty, Jeenal Chaudhary· 1 citation
Exploring how generative AI could make machine vision more accessible to businesses. The post GenEye in a Box: Making Machine Vision Something You Can Just Ask For appeared first on GPT-Lab.
MIT News · Artificial Intelligence· news.mit.eduOct 7, 2026
Students in MIT’s Concourse program delve deeply into the human condition, debate challenging questions, and learn to develop judgment about issues that can’t be quantified.
Training AI agents with reinforcement learning can be challenging because their tools, context, and decision-making are managed by complex frameworks. Agent Lightning connects existing agents to RL training, making it easier to improve them without rebuilding them. The post Agent Lightning v1.0: A 3,500-Line Lightweight Agentic RL Framework for Training Agents with Real Harnesses appeared first on Microsoft Research.
MIT News · Artificial Intelligence· news.mit.eduOct 6, 2026
