As AI research increasingly moves beyond systems optimized for static tasks, a central challenge is building agents that learn continuously through experience-driven lifelong learning (ELL). In this paper, we introduce StuLife, a synthetic benchmark designed to evaluate selected operational capabilities relevant to ELL. Particularly, StuLife simulates a student’s college journey, from enrollment to academic and personal development, across three core phases and ten detailed sub-scenarios. StuLife is designed around four key design principles: From Isolated Tasks to Stateful Interaction, From Passive to Proactive, From Context to Memory, and From Imitation to Learning. In this dynamic environment, agents must acquire and distill practical skills and maintain persistent memory to make decisions based on evolving state variables (e.g., resource availability and time), and are also expected to initiate actions without external prompting. To this end, StuLife provides an evaluation protocol with dedicated metrics (e.g., StuGPA) for these capabilities. Our evaluation reveals that even the highest-StuGPA model, GPT-5, scores only 17.9/100, highlighting large gaps in long-term retention and proactive task initiation. Beyond evaluating LLMs on StuLife, we also explore the role of context engineering in improving agent performance on lifelong learning tasks.

Conventional Retrieval-Augmented Generation (RAG) systems often struggle with complex multi-hop queries over long documents due to their single-pass retrieval. We introduce MM-Doc-R1, a novel framework that employs an agentic, vision-aware workflow to address long document visual question answering through iterative information discovery and synthesis. To incentivize the information seeking capabilities of our agents, we propose Similarity-based Policy Optimization (SPO), addressing baseline estimation bias in existing multi-turn reinforcement learning (RL) algorithms like GRPO. Our core insight is that in multi-turn RL, the more semantically similar two trajectories are, the more accurate their shared baseline estimation becomes. Leveraging this, SPO calculates a more precise baseline by similarity-weighted averaging of rewards across multiple trajectories, unlike GRPO which inappropriately applies the initial state’s baseline to all intermediate states. This provides a more stable and accurate learning signal for our agents, leading to superior training performance that surpasses GRPO. Our experiments on the MMLongbench-Doc benchmark show that MM-Doc-R1 outperforms previous baselines by 10.4%. Furthermore, SPO demonstrates superior performance over GRPO, boosting results by 5.0% with Qwen3-8B and 6.1% with Qwen3-4B. These results highlight the effectiveness of our integrated framework and novel training algorithm in advancing the state-of-the-art for complex, long-document visual question answering.