Jarosław A. Chudziak

Runtime changes in agents' observational capabilities can be systematically managed, ensuring robust belief harmonization even in dynamic environments.

Selective debate using confidence gating not only boosts performance in argument relation classification but also yields interpretable outputs that traditional methods lack.

KG-CFR achieves over 95% resilience against argument degradation in multi-agent debates, redefining stability in adversarial settings.

Multi-agent debate unlocks significantly better argument classification from LLMs, even without fine-tuning, by surfacing hidden ambiguities that single-agent models miss.

Forget limited scope real-world studies: this LLM-powered agent platform lets you simulate entire social media communities to dissect affective polarization with unprecedented control and granularity.