36 citations · 120 across the 25 of their papers we have counts for
6 papers · 1 filter
An Exact Generate - Transform Decomposition of Small-LLM Team Scaling Across Orchestration Architectures
Blaz Bertalanic, Carolina Fortuna
Replacing one LLM agent with a collaborating team can raise accuracy, but whether scaling the team helps, and which architecture to scale, is unclear. Sweeping eight agent orchestr…
Multi-agent Scaling Across Disjunctive and Compensatory Tasks
Carolina Fortuna, Blaz Bertalanic
Multi-agent LLM systems are often expected to improve as team size increases, yet the scaling behavior may depend on task structure. Our central contribution is to introduce Steine…
Where Should Agents Live? Energy-Memory Characterization of Agentic AI for the Edge-Cloud Continuum
Carolina Fortuna, Vid Hanžel, Tim Strnad +1
As telecommunication networks evolve toward autonomous 5G-Advanced and 6G operations, agentic artificial intelligence (AI) workflows, where large language models (LLMs) execute mul…
Monotonic Kolmogorov-Arnold Networks: A Theoretical and Empirical Study of Monotonicity as an Inductive Bias
Mikhail Krasnov, Blaž Bertalanič, Carolina Fortuna
Monotonicity has been a long-running architectural inductive bias for neural networks, motivated by tabular, scientific, and economic settings where outputs are known to respond mo…
The Ringelmann Effect in Multi-Agent LLM Systems: A Scaling Law for Effective Team Size
Blaž Bertalanič, Carolina Fortuna
Inference-time multi-agent LLM scaling lacks a shared unit: counting nominal agents conflates cost with independent evidence. We derive a two-parameter scaling law $R(N) = N_\text{…
The Cost of Consensus: Isolated Self-Correction Prevails Over Unguided Homogeneous Multi-Agent Debate
Blaž Bertalanič, Carolina Fortuna
Multi-agent debate, where teams of LLMs iteratively exchange rationales and vote on answers, is widely deployed under the assumption that peer review filters hallucinations. Yet th…