Large reasoning models improve difficult problem solving by spending many tokens on internal reasoning. This paradigm is natural for single-turn tasks, but poorly matched to language agents: an agent may take tens or hundreds of environment steps, making deep thinking before every action costly and sometimes harmful. We study agentic deep thinking: when should an agent invoke long-form private deliberation, and how should it be exposed inside a ReAct-style loop? We propose a selective deep-thinking framework that separates the short externalized Thought used to choose the next action from a longer private memo used to reorganize future behavior. Without model post-training, we instantiate this framework with Periodic Thinking, Sel.-Gate, and Sel.-Action. Across three benchmarks and six model families, selective deep thinking improves agents, but the best interface depends on backbone capability: strong proprietary models benefit most from deliberation as a callable action, while smaller open-source models are more robust with a separate gate. We then use GRPO to teach a less capable backbone when to deliberate, turning the weakest zero-shot interface into the strongest learned policy and improving transfer to unseen agentic tasks. These results show that selective deep thinking can improve both the effectiveness and efficiency of long-horizon language agents.

Cross-Document Multi-entity question answering (MEQA) demands the integration of scattered information across documents to resolve complex queries involving entities, relationships, and contextual dependencies. Although Large Language Models (LLMs) and Retrieval-augmented Generation (RAG) systems show promise, their performance on cross-document MEQA remains underexplored due to the absence of tailored benchmarks. To address this gap, we introduce MEBench, a scalable multi-document, multi-entity benchmark designed to systematically evaluate LLMs’ capacity to retrieve, consolidate, and reason over scattered and dense information. Our benchmark comprises 4,780 questions which are systematically categorized into three primary categories: Comparative Reasoning, Statistical Reasoning and Relational Reasoning, further divided into eight distinct types, ensuring broad coverage of real-world multi-entity reasoning scenarios. Our experiments on state-of-the-art LLMs reveal critical limitations: even advanced models achieve only 59% accuracy on MEBench. Our benchmark emphasizes the importance of completeness and factual precision of information extraction in MEQA tasks, using Entity-Attributed F1 (EA-F1) metric for granular evaluation of entity-level correctness and attribution validity. MEBench not only highlights systemic weaknesses in current LLM frameworks but also provides a foundation for advancing robust, entity-aware QA architectures.