activity
20242026
collaborators

15 papers

cs.AI2026

Discovering Adaptive Transmission Programs for Collective Innovation

Cédric Colas, Jérémy Perez, Eleni Nisioti +4

Human collective intelligence depends on transmission processes: who shares what with whom, how, and when. While these processes emerge from individual cognition, they can also be…

cs.LG2026

Hard Cases, Bad Labels: Testing Error Exposure and Error Location in Uncertainty Sampling Under Bounded Label Noise

John Myron Uy

Active learning can reduce labeling cost by selecting informative examples, but the most uncertain examples may also be the hardest to label correctly. This study tests whether unc…

cs.CL2026

Conversable Complexity: Agentic LLM Collectives as Interpretable Substrates

Elias Najarro, Ane Espeseth, Eleni Nisioti +2

Complexity and interpretability rarely coincide: systems rich enough for complex behaviours to emerge are usually too opaque to question, while transparent ones are too simple for…

cs.LG2026

Dimensionality Controls When Modularity Helps in Continual Learning

Kathrin Korte, Christian Medeiros Adriano, Joachim Winther Pedersen +2

Compositional learning systems must balance plasticity, the ability to acquire new knowledge, with stability, the preservation of previously learned components, especially when tas…

cs.AI2026

Self-Organising Digital Circuits

Marcello Barylli, Gabriel Béna, Gabriel Béna +3

Fault tolerance in classical computing has traditionally relied on static strategies like hardware redundancy and error-correcting codes. Biological systems, in contrast, exhibit a…

cs.LG2026

When Does Structure Matter in Continual Learning? Dimensionality Controls When Modularity Shapes Representational Geometry

Kathrin Korte, Joachim Winter Pedersen, Eleni Nisioti +1

To preserve previously learned representations, continual learning systems must strike a balance between plasticity, the ability to acquire new knowledge, and stability. This stabi…