ProcTHOR

Headlines · 2

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.

   新智元 · 🔥 0
2. Oct 13
   WEDGE uses an agent's failure modes to generate targeted navigation training worlds

   A preprint submitted to arXiv on 14 October 2026 by Yue Zhang and colleagues introduces WEDGE, a framework for weakness-guided navigation learning in which a pretrained agent's failure modes guide the generation of training environments at each adaptation round. WEDGE has an LLM propose executable weakness features from contrasting successful and failed rollouts, validates them on a diagnostic set, selects the strongest validated weakness by success-rate gap, and maps it to the environmental factors it then varies to build environments in the difficulty regimes where the policy struggles. Across three navigation agents with different adaptation mechanisms, the authors report distinct weakness profiles and different beneficial training environments, with weakness-conditioned adaptation consistently beating matched random and hard-example alternatives on ProcTHOR, and gains for Uni-NaVid and Gemini also extending to HM3D after ProcTHOR adaptation.

   Hugging Face · Papers · 🔥 0

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