Leonardo Ranaldi

AI agents powered by LLMs plan, reason and use tools well, but remain brittle to adapt and evolve because they fail to abstract the mechanisms underlying problem-solving. Reasoning, memory, and decision-making remain fragmented, leaving the systematic reuse of experience for adaptation at scale o...

Debate has emerged as a promising oversight mechanism for Large Language Models (LLMs) amid rising systemic complexity, particularly where models outperform human evaluators. Yet, Debate provides little verifiable evidence for its final judgments, and its scalability remains largely unexplored. T...

LLMs often mistake what sounds true for what is formally valid. This limitation is especially evident in syllogistic reasoning, where plausible arguments can lead models to endorse conclusions that are logically invalid, a phenomenon known as Content Effect (CE).We present Boethius, a schema-guid...

Recent works on large language models (LLMs) have demonstrated the impact of prompting strategies and fine-tuning techniques on their reasoning capabilities. Yet, their effectiveness on clinical natural language inference (CTNLI) remains underexplored. This study presents the first controlled eva...

Large Language Models (LLMs) have been shown to exhibit sycophancy, a tendency to align with user assertions even when they conflict with facts. We frame sycophancy as a sociolinguistic phenomenon, disentangling two distinct drivers of credibility: explicit authority (credentials) and implicit au...