7 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
The paper presents MSEval, a benchmark that evaluates how multi‑agent coding systems build real‑world software, measuring functional success, latency, and token cost while varying…
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 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)…