14 papers
Monotonic Kolmogorov-Arnold Networks: A Theoretical and Empirical Study of Monotonicity as an Inductive Bias
Mikhail Krasnov, Blaž BertalaniÄ, 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 BertalaniÄ, 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 BertalaniÄ, 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…
An Indoor Radio Mapping Dataset Combining 3D Point Clouds and RSSI
Ljupcho Milosheski, Kuon Akiyama, Blaž BertalaniÄ +2
The growing number of smart devices supporting bandwidth-intensive and latency-sensitive applications, such as real-time video analytics, smart sensing, Extended Reality (XR), etc.…
A Configuration-First Framework for Reproducible, Low-Code Machine Learning: a Localization Use Case
Tim Strnad, Blaž BertalaniÄ, Blaž Bertalanič +1
As machine learning underpins more critical applications, the value of a reported result depends on whether it can be compared and repeated. In practice, this remains difficult: re…
A Network Science Approach to Granular Time Series Segmentation
Ivana KesiÄ, Carolina Fortuna, Mihael MohorÄiÄ +1
Time series segmentation (TSS) is one of the time series (TS) analysis techniques, that has received considerably less attention compared to other TS related tasks. In recent years…