LLM agents are increasingly expected to perform long-horizon deep search: iteratively decomposing complex information needs, gathering evidence from external sources, and maintaining a reliable research state under multiple constraints. The extended nature of these tasks makes it difficult to attribute outcomes to individual decisions.
The paper introduces FALCA, a falsification-aware reinforcement learning approach with hierarchical credit assignment. Instead of treating an entire search trajectory as a single learning step, the method assigns credit across levels of decision-making, aiming to better handle the central challenge of long-horizon search.
Since only the abstract is available, no experimental results or comparisons are reported here. Still, the proposed combination of falsification awareness and hierarchical credit assignment offers a concrete direction for making deep search agents more robust in multi-step research tasks.