Long-horizon agents operate over extended sequences of reasoning and actions, but this inevitably accumulates context noise, resulting in excessive computational cost and information overload. Existing approaches commonly rely on fixed, rule-based summarization strategies (e.g., summarizing every few steps), which are inflexible, lack generalization, and often introduce irreversible information loss. We propose Self-Sum, a framework that empowers agents to autonomously decide when and what to summarize by modeling summarization as a first-class internal cognitive action, unified with external environmental actions within a multi-turn decision-making process. Specifically, we introduce a two-stage training recipe consisting of (i) a cold-start supervised fine-tuning stage that bootstraps summarization behavior, and (ii) a lightweight, summarization-aware reinforcement learning stage that refines summarization timing and content while discouraging unnecessary summaries. Experiments on multiple long-horizon benchmarks show that Self-Sum consistently outperforms no-summarization and rule-based baselines, with particularly strong gains in generalization. Analysis further reveals that Self-Sum learns to summarize sparsely at meaningful moments and preserves task-relevant information, highlighting the importance of jointly learning when and what to summarize for robust long-horizon agent behavior.

Large language models (LLMs) have shown significant potential for robotics applications, particularly task planning, by harnessing their language comprehension and text generation capabilities. However, in applications such as household robotics, a critical gap remains in the personalization of these models to household preferences. For example, an LLM planner may find it challenging to perform tasks that require personalization, such as deciding where to place mugs in a kitchen based on specific household preferences. We introduce LLM-Personalize, a novel framework designed to personalize LLM planners for household robotics. LLM-Personalize uses an LLM planner to perform iterative planning in multi-room, partially-observable household environments, utilizing a scene graph built dynamically from local observations. To personalize the LLM planner towards user preferences, our optimization pipeline integrates imitation learning and reinforced Self-Training. We evaluate LLM-Personalize on Housekeep, a challenging simulated real-world 3D benchmark for household rearrangements, demonstrating a more than 30 percent increase in success rate over existing LLM planners, showcasing significantly improved alignment with human preferences.