Boris Meinardus — accepted AI-conference papers

2026

Discovering Novel LLM Experts via Task-Capability Coevolution

Andrew Dai, Boris Meinardus, Ciaran Regan, Yingtao Tian, Yujin Tang

ICLR 2026poster

Frontier model developers aim to train models continually to possess emergent, diverse capabilities. To extend capabilities, the current pre-training and post-training paradigm requires manually starting training runs with static datasets or reward functions every time. Addressing this limitation,…

Cited by 0 Source ↗code ↗Scholar ↗

2022

Arena-Bench: A Benchmarking Suite for Obstacle Avoidance Approaches in Highly Dynamic Environments

Linh Kästner, Teham Bhuiyan, Tuan Anh Le, Elias Treis, Johannes Cox, Boris Meinardus +6

RA-L 2022

The ability to autonomously navigate safely, especially within dynamic environments, is paramount for mobile robotics. In recent years, DRL approaches have shown superior performance in dynamic obstacle avoidance. However, these learning-based approaches are often developed in specially designed sim

Cited by 43 Source ↗code ↗Scholar ↗