most citedERNIE 5.0 Technical Report

2 citations · 2 across the 5 of their papers we have counts for

collaborators

5 papers

cs.SE2026

BulkPR-Bench: Benchmarking Queue-Level Governance of Interacting Pull Requests

Zetong Xiong, Qiao Zhao, Jun Zhang +20

Coding-agent benchmarks increasingly cover long-horizon, end-to-end, and interactive development, but typically retain one requested outcome or a fixed change sequence. Sequential…

cs.SE2026

SWE-Future: Forecast-Conditioned Data Synthesis for Future-Oriented Software Engineering Agents

Qiao Zhao, JianYing Qu, Jun Zhang +3

Realistic coding-agent benchmarks often replay public GitHub issues and pull requests, making them vulnerable to overlap with model pretraining, fine-tuning, synthetic-data generat…

cs.CL2026

Do Language Models Track Entities Across State Changes?

Zilu Tang, Qiao Zhao, Gabriel Franco +4

Entity tracking (ET), the ability to keep track of states, is a fundamental skill that underlies complex reasoning. An increasing amount of work investigates how transformer langua…

cs.SE2026

Code-QA-Bench: Separating Code Reasoning from Documentation Memorization in Repository-Level QA

Jun Zhang, JianYing Qu, Hanwen Du +3

We present Code-QA-Bench, a fully automated framework for synthesizing repository-level code understanding benchmarks that separates genuine code comprehension from documentation r…

cs.CL20262 cited

ERNIE 5.0 Technical Report

Haifeng Wang, Hua Wu, Tian Wu +432

In this report, we introduce ERNIE 5.0, a natively autoregressive foundation model desinged for unified multimodal understanding and generation across text, image, video, and audio…