6 papers
Credit Where Credit Is Due: Contrastive Attribution Topology Search for Multi-Agent Systems
Fabian Tritthart, Tom Zehle
An LLM multi-agent system (MAS) is only as good as the agents it chooses to trust: its accuracy and cost hinge on its topology, meaning which agents are included and how they commu…
Design and Tuning of Multi-Agent Systems Using Bayesian Evolutionary Optimization
Dominik Jehle, Tom Zehle
Designing effective multi-agent systems (MAS) remains a challenging and resource-intensive task. We propose a Bayesian Evolutionary Optimization framework for automatically discove…
MO-CAPO: Multi-Objective Cost-Aware Prompt Optimization
Jan Büssing, Moritz Schlager, Timo Heiß +2
Large language models (LLMs) achieve strong performance across a wide range of tasks but are highly sensitive to prompt design, motivating the need for automatic prompt optimizatio…
CANTANTE: Optimizing Agentic Systems via Contrastive Credit Attribution
Tom Zehle
LLM-based multi-agent systems have demonstrated strong performance across complex real-world tasks, such as software engineering, predictive modeling, and retrieval-augmented gener…
promptolution: A Unified, Modular Framework for Prompt Optimization
Tom Zehle, Timo Heiß, Moritz Schlager +2
Prompt optimization has become crucial for enhancing the performance of large language models (LLMs) across a broad range of tasks. Although many research papers demonstrate its ef…
CAPO: Cost-Aware Prompt Optimization
Tom Zehle, Moritz Schlager, Timo Heiß +1
Large language models (LLMs) have revolutionized natural language processing by solving a wide range of tasks simply guided by a prompt. Yet their performance is highly sensitive t…