papers

Publications (11)

physics.ins-det2026

Final assessment of radioactive impurities in the JUNO detector

Thomas Adam, Fengpeng An, Costas Andreopoulos +569

The Jiangmen Underground Neutrino Observatory (JUNO) collaboration has completed the construction of the 20,000-ton liquid scintillator detector and the associated muon veto detect…

cs.OS2025

EVICPRESS: Joint KV-Cache Compression and Eviction for Efficient LLM Serving

Shaoting Feng, Yuhan Liu, Hanchen Li +11

Reusing KV cache is essential for high efficiency of Large Language Model (LLM) inference systems. With more LLM users, the KV cache footprint can easily exceed GPU memory capacity…

hep-ex2026

A Low-energy Threshold and Multi-messenger Trigger System for the JUNO Experiment

Thomas Adam, Fengpeng An, Costas Andreopoulos +563

The paper presents a new multi‑messenger trigger system for the JUNO liquid‑scintillator detector that lowers the energy threshold to about 110 keV and adds real‑time monitoring of…

#low-energy trigger#multi-messenger detection#neutrino observatory#supernova neutrino bursts
cs.LG2025

METIS: Fast Quality-Aware RAG Systems with Configuration Adaptation

Siddhant Ray, Rui Pan, Zhuohan Gu +5

RAG (Retrieval Augmented Generation) allows LLMs (large language models) to generate better responses with external knowledge, but using more external knowledge often improves gene…

cs.AR2026

VeriCache: Turning Lossy KV Cache into Lossless LLM Inference

Jiayi Yao, Samuel Shen, Kuntai Du +7

The large size of the KV cache has become a major bottleneck for serving LLMs with increasing context lengths. In response, many KV cache compression methods, such as token droppin…

hep-ex2025

Initial performance results of the JUNO detector

Angel Abusleme, Thomas Adam, Kai Adamowicz +1131

The Jiangmen Underground Neutrino Observatory (JUNO) started physics data taking on 26 August 2025. JUNO consists of a 20-kton liquid scintillator central detector, surrounded by a…

cs.OS2026

AdaptCache: KV Cache Native Storage Hierarchy for Low-Delay and High-Quality Language Model Serving

Shaoting Feng, Hanchen Li, Kuntai Du +8

Large language model (LLM) applications often reuse previously processed context, such as chat history and documents, which introduces significant redundant computation. Existing L…

cs.MA2025

DroidSpeak: KV Cache Sharing for Cross-LLM Communication and Multi-LLM Serving

Yuhan Liu, Yuyang Huang, Jiayi Yao +9

Compound AI systems, such as agentic systems, are an emerging trend in large-scale enterprise settings, with multiple LLMs specialized for different users, tasks, and/or roles work…

hep-ex2025

Prospects for geoneutrino detection with JUNO

Thomas Adam, Shakeel Ahmad, Rizwan Ahmed +625

Geoneutrinos, which are antineutrinos emitted during the decay of long-lived radioactive elements inside Earth, serve as a unique tool for studying the composition and heat budget…

cs.LG2025

LMCache: An Efficient KV Cache Layer for Enterprise-Scale LLM Inference

Yuhan Liu, Yihua Cheng, Jiayi Yao +8

KV cache has traditionally been stored in GPU memory to accelerate the decoding phase of large language model (LLM) inference. However, it is increasingly necessary to move KV cach…

hep-ex2025

First measurement of reactor neutrino oscillations at JUNO

Angel Abusleme, Thomas Adam, Kai Adamowicz +1131

Neutrino oscillations, a quantum effect manifesting at macroscopic scales, are governed by lepton flavor mixing angles and neutrino mass-squared differences that are fundamental pa…