most citedOpenSeeker: Democratizing Frontier Search Agents by Fully Open-Sourcing Training Data

1 citations · 1 across the 3 of their papers we have counts for

collaborators

6 papers

cs.AI2026

ABSeeker: Training Long-Horizon Search Agents via Answer-Backtracked Credit Assignment

Yijun Lu, Rui Ye, Jiajun Wang +4

Long-horizon search agents must make multiple sequential actions (steps) to search, retrieve, verify, and integrate evidence to reach a final answer. However, existing methods for…

cs.AI2026

LongSeeker: Elastic Context Orchestration for Long-Horizon Search Agents

Yijun Lu, Rui Ye, Yuwen Du +3

Long-horizon search agents must manage a rapidly growing working context as they reason, call tools, and observe information. Naively accumulating all intermediate content can over…

cs.AI20261 cited

OpenSeeker: Democratizing Frontier Search Agents by Fully Open-Sourcing Training Data

Yuwen Du, Rui Ye, Shuo Tang +4

Deep search capabilities have become an indispensable competency for frontier Large Language Model (LLM) agents, yet the development of high-performance search agents remains domin…

cs.HC2026

See What I See: An Attention-Guiding eHMI Approach for Autonomous Vehicles

Jialong Li, Zhenyu Mao, Zhiyao Wang +4

As autonomous vehicles are gradually being deployed in the real world, external Human-Machine Interfaces (eHMIs) are expected to serve as a critical solution for enhancing vehicle-…

cs.AI2025

Knowledge Graph-enhanced Large Language Model for Incremental Game PlayTesting

Enhong Mu, Jinyu Cai, Yijun Lu +3

The rapid iteration and frequent updates of modern video games pose significant challenges to the efficiency and specificity of testing. Although automated playtesting methods base…

cs.AI2025

RSafe: Incentivizing proactive reasoning to build robust and adaptive LLM safeguards

Jingnan Zheng, Xiangtian Ji, Yijun Lu +6

Large Language Models (LLMs) continue to exhibit vulnerabilities despite deliberate safety alignment efforts, posing significant risks to users and society. To safeguard against th…