most citedERNIE 5.0 Technical Report

2 citations · 2 across the 4 of their papers we have counts for

collaborators

5 papers

cs.HC2026

OmegaUse-SOP: SOP Engineering for Professional Computer Use from Human Demonstrations

Yixiong Xiao, Lang An, Hucheng Yang +9

Large language models (LLMs) are increasingly evolving from conversational assistants into agents capable of operating external digital environments. Graphical user interface (GUI)…

cs.AI2026

PassNet: Scaling Large Language Models for Graph Compiler Pass Generation

Yiqun Liu, Yingsheng Wu, Ruqi Yang +11

Modern tensor compilers such as TorchInductor deliver substantial speedups on mainstream models, yet face a systematic performance ceiling on long-tail workloads -- our profiling s…

cs.CL2026★ 2 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…

cs.SE2026

MEnvAgent: Scalable Polyglot Environment Construction for Verifiable Software Engineering

Chuanzhe Guo, Jingjing Wu, Sijun He +10

The evolution of Large Language Model (LLM) agents for software engineering (SWE) is constrained by the scarcity of verifiable datasets, a bottleneck stemming from the complexity o…

cs.LG2025

ChessArena: A Chess Testbed for Evaluating Strategic Reasoning Capabilities of Large Language Models

Jincheng Liu, Sijun He, Jingjing Wu +5

Recent large language models (LLMs) have shown strong reasoning capabilities. However, a critical question remains: do these models possess genuine strategic reasoning, or do they…