We introduce OceanGym, the first comprehensive benchmark for underwater embodied agents, designed to advance AI in one of the most demanding real-world environments. OceanGym features eight realistic task domains and a unified agent framework driven by Multimodal Large Language Models (MLLMs) that integrates perception, memory, and sequential decision-making. Agents must follow natural language instructions, interpret optical and sonar inputs, autonomously explore complex environments, and achieve long-horizon goals. Extensive experiments reveal a significant performance gap between state-of-the-art MLLM-driven agents and human experts, underscoring persistent challenges in perception, planning, and adaptation underwater. By offering a high-fidelity, rigorously designed platform, OceanGym establishes a testbed for robust embodied AI and represents a foundational step toward sim-to-real transfer on Autonomous Underwater Vehicles (AUVs), marking progress toward agents capable of operating in one of Earth’s last unexplored frontiers.

Large Language Models (LLMs) have demonstrated strong performance on general tasks, while often struggling to adapt to specialized domains without high-quality domain-specific data. Existing LLM-based data curation methods primarily rely on human-designed workflows, leaving it unexamined whether LLMs can autonomously execute an end-to-end data engineering pipeline for model specialization. We formalize Autonomous Agentic Data Engineering, a novel task designed to evaluate LLMs as autonomous data engineers that drive model specialization through end-to-end data curation. We frame data as an optimizable component and assign an autonomous agent with planning, generating, and optimizing training data across multiple domains, guided by post-training performance improvement. Experiments show that autonomous LLM data engineers yield substantial gains, as GPT-5.2 constructs a training curriculum that improves a student model by 57.29%, entirely through iterative, agent-driven data adaptation. By illuminating both potential and bottlenecks, our study establishes autonomous data engineering as a measurable capability and charts a path toward agent-driven model specialization.

Large Language Models (LLMs) have achieved considerable performance across various agentic planning tasks. However, traditional approaches adopt a “flood irrigation” methodology that indiscriminately injects gold trajectories, external feedback, and domain knowledge into agent models. This practice overlooks the fundamental human cognitive principle of self-awareness - the ability to dynamically assess situational demands and strategically employ resources during decision-making. We propose Agentic Knowledgeable Self-awareness to address this gap, a novel paradigm enabling LLM-based agents to autonomously regulate knowledge utilization. Specifically, we propose KnowSelf, a data-centric approach that applies agents with knowledgeable self-awareness like humans. Concretely, we devise a heuristic situation judgement criterion to mark special tokens on the agent’s self-explored trajectories for collecting training data. Through a two-stage training process, the agent model can switch between different situations by generating specific special tokens, achieving optimal planning effects with minimal costs. Our experiments demonstrate that can outperform various strong baselines on different tasks and models with minimal use of external knowledge.