Large Language Models (LLMs) exhibit strong reasoning abilities for planning long-horizon, real-world tasks, yet existing agent benchmarks focus on task completion while neglecting time efficiency in parallel and asynchronous operations. To address this, we present ParaCook, a benchmark for time-efficient collaborative planning. Inspired by the Overcooked game, ParaCook provides an environment for various challenging interaction planning of multi-agent systems that are instantiated as cooking tasks, with a simplified action space to isolate the core challenge of strategic parallel planning. Through a comprehensive evaluation of state-of-the-art LLMs, we find that current approaches achieve suboptimal plans, which struggle with parallel actions or coordination. Our analysis also reveals LLMs’ potential on abstract tasks where they can focus on high-level parallel optimization. ParaCook provides a scalable evaluation framework with adjustable complexity, establishing a foundation for developing and assessing time efficiency-aware multi-agent planning.

AI agents promise to turn scattered scientific evidence into AI-ready datasets, yet existing benchmarks examine isolated NLP skills or paper-level summarization and leave open whether agents can assemble complete, evidence-grounded records across multimodal and heterogeneous scientific sources. To address this gap, we introduce ABDataBench, a general evaluation protocol for antibody functional data construction from scientific literature and patents. ABDataBench contains 44 expert-annotated, OCR-processed documents, including 27 papers and 17 patents, yielding 279 antibody-level records under a unified 22-field schema. We further develop ABCurator, a record-centric multi-agent framework that treats each antibody as the unit for planning, evidence aggregation, enrichment, and validation, coupled with a skill-accumulation workflow that distills expert feedback into reusable agent competencies across evaluation rounds. Across proprietary and open-source models, the strongest configurations achieve scores of 78.3 and 76.0, respectively. Analysis shows that current agents achieve high antibody recall but remain limited by over-generation, sequence attribution errors, and incomplete affinity grounding. These results position antibody record construction as a challenging long-horizon information-extraction task and establish ABDataBench as a benchmark framework for reliable AI-driven scientific data generation. The source code and data for ABDataBench is released at:https://github.com/GAIR-NLP/ABDataBench.

Large Language Models (LLMs) with web search capabilities show significant potential for deep research, yet current methods—brittle prompt engineering or RAG-based reinforcement learning in controlled environments—fail to capture real-world complexities. In this paper, we introduce DeepResearcher, the first comprehensive framework for end-to-end training of LLM-based deep research agents through scaling reinforcement learning (RL) in real-world environments with authentic web search interactions. Unlike RAG approaches reliant on fixed corpora, DeepResearcher trains agents to navigate the noisy, dynamic open web. We implement a specialized multi-agent architecture where browsing agents extract relevant information from various webpage structures and overcoming significant technical challenges. Extensive experiments on open-domain research tasks demonstrate that DeepResearcher achieves substantial improvements of up to 28.9 points over prompt engineering-based baselines and up to 7.2 points over RAG-based RL agents. Our qualitative analysis reveals emergent cognitive behaviors from end-to-end RL training, such as planning, cross-validation, self-reflection for research redirection, and maintain honesty when unable to find definitive answers. Our results highlight that end-to-end training in real-world web environments is fundamental for developing robust research capabilities aligned with real-world applications. The source codefor DeepResearcher is released at: https://github.com/GAIR-NLP/DeepResearcher.