3 papers
cs.AI2026
When Does LLM Orchestration Pay Off? A Controlled Evaluation of Accuracy, Cost, and Task Difficulty
Nicolas Leins, Nico Pelleriti, Jana Gonnermann-Müller +1
LLM orchestration is often assumed to improve reasoning by allocating additional inference-time computation, yet its gains may not justify its cost. Existing comparisons also frequ…
cs.RO2026
Beyond Static Instruction: A Multi-agent AI Framework for Adaptive Augmented Reality Robot Training
Nicolas Leins, Jana Gonnermann-Müller, Malte Teichmann +1
Augmented Reality (AR) offers powerful visualization capabilities for industrial robot training, yet current interfaces remain predominantly static, failing to account for learners…
cs.AI2026
Within-Model vs Between-Prompt Variability in Large Language Models for Creative Tasks
Jennifer Haase, Jana Gonnermann-Müller, Paul H. P. Hanel +4
How much of LLM output variance is explained by prompts versus model choice versus stochasticity through sampling? We answer this by evaluating 12 LLMs on 10 creativity prompts wit…