13 citations · 21 across the 10 of their papers we have counts for
11 papers
TideRL: Boosting Agentic RL Goodput with Readiness-Aware Scheduling
Yanyu Ren, Xizheng Wang, Xiao Liu +8
Reinforcement learning (RL) for large language models is moving toward multi-turn agentic workloads, where rollout tasks repeatedly pause for external environments, resume with gro…
An Empirical Study of Coordination Mode as the First-Class Citizen in From-Scratch Multi-Agent Coding
Yanyu Ren, Yunfeng Bai, Xizheng Wang +2
Multi-agent vibe coding promises to accelerate software development, yet existing benchmarks rely on synthetic environments that ignore practical time and monetary costs, conflate…
Binary Decompilation LLM with Feedback-Driven Multi-Turn Refinement
Peipei Liu, Jian Sun, Mingzhe Xing +5
Binary decompilation is fundamental to security tasks such as vulnerability discovery, malware inspection, and executable-only program understanding. Recent LLM-based decompilation…
EntWorld: A Holistic Environment and Benchmark for Verifiable Enterprise GUI Agents
Ying Mo, Yu Bai, Dapeng Sun +4
Recent advances in Multimodal Large Language Models (MLLMs) have enabled agents to operate in open-ended web and operating system environments. However, existing benchmarks predomi…
DMA: Online RAG Alignment with Human Feedback
Yu Bai, Yukai Miao, Dawei Wang +9
Retrieval-augmented generation (RAG) systems often rely on static retrieval, limiting adaptation to evolving intent and content drift. We introduce Dynamic Memory Alignment (DMA),…
The Digital Cybersecurity Expert: How Far Have We Come?
Dawei Wang, Geng Zhou, Xianglong Li +5
The increasing deployment of large language models (LLMs) in the cybersecurity domain underscores the need for effective model selection and evaluation. However, traditional evalua…
The CodeInverter Suite: Structure- and Data-Aware Binary Decompilation with Efficient LLMs
Peipei Liu, Jian Sun, Rongkang Sun +7
Binary decompilation plays a vital role in various cybersecurity and software engineering tasks. Recently, end-to-end decompilation methods powered by large language models (LLMs)…
ProphetFuzz: Fully Automated Prediction and Fuzzing of High-Risk Option Combinations with Only Documentation via Large Language Model
Dawei Wang, Geng Zhou, Li Chen +2
Vulnerabilities related to option combinations pose a significant challenge in software security testing due to their vast search space. Previous research primarily addressed this…
Pistis-RAG: Enhancing Retrieval-Augmented Generation with Human Feedback
Yu Bai, Yukai Miao, Li Chen +6
RAG systems face limitations when semantic relevance alone does not guarantee improved generation quality. This issue becomes particularly evident due to the sensitivity of large l…
An Empirical Study of NetOps Capability of Pre-Trained Large Language Models
Yukai Miao, Yu Bai, Li Chen +10
Nowadays, the versatile capabilities of Pre-trained Large Language Models (LLMs) have attracted much attention from the industry. However, some vertical domains are more interested…
TODM: Train Once Deploy Many Efficient Supernet-Based RNN-T Compression For On-device ASR Models
Yuan Shangguan, Haichuan Yang, Danni Li +11
Automatic Speech Recognition (ASR) models need to be optimized for specific hardware before they can be deployed on devices. This can be done by tuning the model's hyperparameters…