Embodied AI and wearable assistants require agents to retrieve sparse evidence from continuous days-level videos. Existing memory-based methods typically follow a passive recording paradigm based on fixed-window captioning and periodic memory construction, which produces redundant memories and inefficient long-horizon reasoning. Inspired by human memory formation, we propose Active Explore Memory Agent (AEMAgent), which transforms passive recording into active memory formation by deciding when to remember, how to explore events, and when to reflect. These decisions are instantiated as a coherent pipeline: Surprise-guided Event Selection detects salient event changes for selective memory writing, Exploratory Episodic Memory Encoding enriches accepted events through self-questioning, Predictive-Coding-Guided Reflection updates knowledge from expectation-observation mismatches, and Semantic Memory Consolidation distills noisy episodic updates into compact long-term memory. Experiments on multiple benchmarks show that AEMAgent achieves the best overall performance among all compared methods while maintaining lower storage overhead than strong full-memory baselines, demonstrating the effectiveness of selective and efficient memory formation for scalable very-long video understanding.

Recent advances in large language models (LLMs) have enabled increasingly capable web agents, yet training such agents still relies on high-quality interaction trajectories that are difficult to obtain at scale. We identify two key challenges: (1) Infrastructure Overhead, where network instability and website access restrictions limit data collection scalability; and (2) Constrained Exploration, where irreversible state transitions preclude tree-based search and thus limit trajectory diversity. To address these challenges, we introduce WebSynthesis, a framework for scalable trajectory synthesis. WebSynthesis employs an LLM-based World Model to simulate state transitions without network dependencies, and integrates Monte Carlo Tree Search to enable reversible exploration over the simulated state space. Experiments on WebArena, WebVoyager, and Mind2Web-Online demonstrate that agents trained exclusively on synthesized trajectories outperform those trained on real-world data, providing a viable alternative to costly real-world data collection.