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Zhenkun Gao

2 accepted papers

2026

Explore with Long-term Memory: A Benchmark and Multimodal LLM-based Reinforcement Learning Framework for Embodied Exploration

CVPR 2026

An ideal embodied agent should possess lifelong learning capabilities to handle long-horizon and complex tasks, enabling continuous operation in general environments. This not only requires the agent to accurately accomplish given tasks but also to leverage long-term episodic memory to optimize deci

Cited by 0SourcecodeScholar
2026

TPRU: Advancing Temporal and Procedural Understanding in Large Multimodal Models

ICLR 2026poster

Multimodal Large Language Models (MLLMs), particularly smaller, deployable variants, exhibit a critical deficiency in understanding temporal and procedural visual data, a bottleneck hindering their application in real-world embodied AI. This gap is largely caused by a systemic failure in training pa…

Cited by 0SourcecodeScholar