2 citations · 2 across the 2 of their papers we have counts for
5 papers
ScaleBox: Enabling High-Fidelity and Scalable Code Verification for Large Language Models
Jiasheng Zheng, Xin Zheng, Boxi Cao +8
Code sandboxes have emerged as a critical infrastructure for advancing the coding capabilities of large language models, providing verifiable feedback for both RL training and eval…
Across Programming Language Silos: A Study on Cross-Lingual Retrieval-augmented Code Generation
Qiming Zhu, Jialun Cao, Xuanang Chen +6
Current research on large language models (LLMs) with retrieval-augmented code generation (RACG) has largely focused on single-language settings, leaving their cross-lingual effect…
From Lossy to Verified: A Provenance-Aware Tiered Memory for Agents
Qiming Zhu, Shunian Chen, Rui Yu +2
Long-horizon agents often compress interaction histories into write-time summaries. This creates a fundamental write-before-query barrier: compression decisions are made before the…
MTalk-Bench: Evaluating Speech-to-Speech Models in Multi-Turn Dialogues via Arena-style and Rubrics Protocols
Yuhao Du, Qianwei Huang, Guo Zhu +9
The rapid advancement of speech-to-speech (S2S) large language models (LLMs) has significantly improved real-time spoken interaction. However, current evaluation frameworks remain…
DOMAINEVAL: An Auto-Constructed Benchmark for Multi-Domain Code Generation
Qiming Zhu, Jialun Cao, Yaojie Lu +4
Code benchmarks such as HumanEval are widely adopted to evaluate the capabilities of Large Language Models (LLMs), providing insights into their strengths and weaknesses. However,…