DreamProver and AGEL-Comp: What LLM Agents Need to Reason Better and Generalize Further
DreamProver and AGEL-Comp: What LLM Agents Need to Reason Better and Generalize Further Two recent arXiv papers examine a similar broad problem from different angles: how to make LLM-based agents less brittle when they need to reason across tasks rather than respond one step at a time. DreamProver presents an agentic theorem-proving framework that uses a wake-sleep program induction paradigm to discover reusable lemma libraries for formal proof work. AGEL-Comp introduces a neuro-symbolic architecture for interactive agents that targets failures in compositional generalization through a structured world model, grounding, and skill composition. Both papers are framed as attempts to address limits in current LLM-based agents, but they do so in distinct problem settings and with different design goals. [S1][S2] [S1] [S2] Introduction: the papers and their release context DreamProver, titled "DreamProver: Evolving Transferable Lemma Libraries via a Wake-Sleep Theorem-Proving Agent,...