Two recent arXiv papers explore the reasoning capabilities of large language models (LLMs) in complex planning scenarios.
According to arXiv paper 2510.06534v3, titled “Beneficial Reasoning Behaviors in Agentic Search and Effective Post-training to Obtain Them,” researchers investigated what constitutes effective reasoning for agentic search tasks. The paper focuses on “multi-step search to solve complex information-seeking tasks,” which presents “unique challenges” to LLM reasoning capabilities, according to the abstract.
A second paper, arXiv 2601.11354v1, introduces “AstroReason-Bench: Evaluating Unified Agentic Planning across Heterogeneous Space Planning Problems.” The research addresses a gap in existing agent benchmarks. According to the abstract, while “recent advances in agentic Large Language Models (LLMs) have positioned them as generalist planners capable of reasoning and acting across diverse tasks,” current benchmarks “largely focus on symbolic or weakly grounded environments.” The new benchmark appears designed to evaluate LLM performance in more complex, heterogeneous space planning scenarios.
Both papers contribute to understanding how LLMs can be improved for multi-step reasoning and planning tasks, though from different angles—one examining information-seeking behaviors and post-training methods, the other developing evaluation frameworks for space planning problems.