1 citations · 1 across the 10 of their papers we have counts for
10 papers
RealClawBench: Live OpenClaw Benchmarks from Real Developer-Agent Sessions
Zongwei Lv, Yaoming Li, Zhewen Tan +7
Agent benchmarks should reflect what users actually ask deployed agents to do, yet existing benchmarks often miss key realism properties of real developer-agent sessions. We introd…
Harness-Bench: Evaluating Agent Harnesses across Models and Realistic Workflows
Yilun Yao, Xinyu Tan, Chao-Hsuan Liu +10
LLM agents are increasingly deployed as executable systems that use tools, modify workspaces, and produce concrete artifacts. In such workflows, performance depends not only on the…
Beyond Parameter Arithmetic: Sparse Complementary Fusion for Distribution-Aware Model Merging
Weihong Lin, Lin Sun, Qilong Shi +6
Model merging has emerged as a promising paradigm for composing the capabilities of large language models by directly operating in weight space, enabling the integration of special…
KVReviver: Reversible KV Cache Compression with Sketch-Based Token Reconstruction
Aomufei Yuan, Zhiming Wang, Ruijie Miao +8
As the context length of current large language models (LLMs) rapidly increases, the memory demand for the Key-Value (KV) cache is becoming a bottleneck for LLM deployment and batc…
Detecting Flow Gaps in Data Streams
Siyuan Dong, Yuxuan Tian, Wenhan Ma +5
Data stream monitoring is a crucial task which has a wide range of applications. The majority of existing research in this area can be broadly classified into two types, monitoring…
ResidualSketch: Enhancing Layer Efficiency and Error Reduction in Hierarchical Heavy Hitter Detection with ResNet Innovations
Xilai Liu, Yuxuan Tian, Xiangyuan Wang +4
In network management, swiftly and accurately identifying traffic anomalies, including Distributed Denial-of-Service (DDoS) attacks and unexpected network disruptions, is essential…
Hidden Sketch: A Space-Efficient Reversible Sketch for Tracking Frequent Items in Data Streams
Zicang Xu, Yuxuan Tian, Yuhan Wu +1
Modern data stream applications demand memory-efficient solutions for accurately tracking frequent items, such as heavy hitters and heavy changers, under strict resource constraint…
KeepKV: Achieving Periodic Lossless KV Cache Compression for Efficient LLM Inference
Yuxuan Tian, Zihan Wang, Yebo Peng +6
Efficient inference of large language models (LLMs) is hindered by an ever-growing key-value (KV) cache, making KV cache compression a critical research direction. Traditional meth…
FairKV: Balancing Per-Head KV Cache for Fast Multi-GPU Inference
Bingzhe Zhao, Ke Cheng, Aomufei Yuan +5
KV cache techniques in Transformer models aim to reduce redundant computations at the expense of substantially increased memory usage, making KV cache compression an important and…
Prediction Is All MoE Needs: Expert Load Distribution Goes from Fluctuating to Stabilizing
Peizhuang Cong, Aomufei Yuan, Shimao Chen +4
MoE facilitates the development of large models by making the computational complexity of the model no longer scale linearly with increasing parameters. The learning sparse gating…