EmbodiedMemory-Bench

Headlines · 1

1. Oct 18
   Zhejiang University proposes EMem-Bench for embodied agent memory

   Zhejiang University's OmniAI team introduced EmbodiedMemory-Bench (EMem-Bench), a benchmark with four task types—passive observation, dynamic tracking, interaction failure and experience generalization—and 2,554 executable samples testing whether embodied agents can use past observations, state changes and action feedback in later tasks. The team also proposed an external memory system, Embodied-Memorizer (EMem), and fine-tuned Qwen3-VL-8B into EMem-8B; in the authors' evaluation, EMem raised average success from 35.3% to 58.9% with a GPT-5.4-mini backbone. Under the paper's full-context setting, Gemini-3-Flash led at 64.2% average success and GPT-5.4 reached 56.8%. The report says the benchmark drew nearly 30,000 Hugging Face downloads in about three weeks, with results mainly from simulation.

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