Low-Latency Semantic Processing with Optimal Prompt-Level Batching

RAILS: Retrieval-Augmented Incremental LLM Clustering at Scale

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.

Maintainable, Low-Latency and High-Quality Query Compilation with MLIR and TPDE

A low-latency MLIR compilation backend based on TPDE that by-passes the LLVM lowering and compilation pipeline is introduced and techniques that reduce MLIR’s intrinsic overhead are proposed.

Real-time NLP moderation on XMPP with GPU-accelerated inference and adaptive batching

This work introduces Adaptive Cross-Domain Batching (ACDB) as a method to dynamically adjust batch sizes based on the state of the queues, characteristics of the messages being processed, and real-time feedback from GPU usage.

Accelerating String-Heavy Queries with LLM Token Tables

Strings are the most common data type in modern database systems, yet they are often treated as an afterthought in high-performance data formats. While numerical data benefits from specialized, lightweight compression schemes, text is typically handled by general-purpose algorithms such as Zstd, LZ4, or Snappy, which r...

O P S ERVE : Opportunistic LLM Inference over Fragmented GPU Capacity in HPC Systems

Replacing Large Language Models with Jev Decision Models for Low-Latency Edge Service Orchestration

Jev's decision-oriented application programming interface (API) is integrated into edge service orchestration to reduce overhead while retaining service completion, and decision-model substitution for latency-bound admission on bounded contracts is supported.