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cs.CL2026

Dual-Pool Token-Budget Routing for Cost-Efficient and Reliable LLM Serving

Xunzhuo Liu, Bowei He, Xue Liu +3

Production vLLM fleets typically provision each instance for the worst-case context length, leading to substantial KV-cache over-allocation and under-utilized concurrency. In pract…

cs.CL2026

Knowledge Access Beats Model Size: Memory Augmented Routing for Persistent AI Agents

Xunzhuo Liu, Bowei He, Xue Liu +3

Production AI agents frequently receive user-specific queries that are highly repetitive, with up to 47\% being semantically similar to prior interactions, yet each query is typica…

cs.CL2026

Adaptive Vision-Language Model Routing for Computer Use Agents

Xunzhuo Liu, Bowei He, Xue Liu +3

Computer Use Agents (CUAs) translate natural-language instructions into Graphical User Interface (GUI) actions such as clicks, keystrokes, and scrolls by relying on a Vision-Langua…

cs.CL2026

98 Faster LLM Routing Without a Dedicated GPU: Flash Attention, Prompt Compression, and Near-Streaming for the vLLM Semantic Router

Xunzhuo Liu, Bowei He, Xue Liu +3

System-level routers that intercept LLM requests for safety classification, domain routing, and PII detection must be both fast and operationally lightweight: they should add minim…

cs.CL2025

Building Safe GenAI Applications: An End-to-End Overview of Red Teaming for Large Language Models

Alberto Purpura, Sahil Wadhwa, Jesse Zymet +5

The rapid growth of Large Language Models (LLMs) presents significant privacy, security, and ethical concerns. While much research has proposed methods for defending LLM systems ag…

cs.CL2025

Refining Input Guardrails: Enhancing LLM-as-a-Judge Efficiency Through Chain-of-Thought Fine-Tuning and Alignment

Melissa Kazemi Rad, Huy Nghiem, Andy Luo +3

Large Language Models (LLMs) have demonstrated powerful capabilities that render them valuable in different applications, including conversational AI products. It is paramount to e…