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自进化WAM来了!清华AIR联手域变换提出具身In-Context Causal Learning

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Merged summary

TL;DR - Tsinghua AIR and startup Yubianhuan introduced Zeva, an embodied world-action model that learns from action outcomes and human demonstrations at deployment time without updating its weights. Its causal memory raised cumulative success from 26% to 73% across repeated attempts on a simulated benchmark and improved real-world robotic lab tasks.

  • Zeva encodes visual state, actions, and resulting state changes into causal interaction signals that capture action-effect relationships.
  • Dual-timescale memory maintains short-term execution context while retaining reusable evidence from failures, corrections, and successes across attempts.
  • Retrieved evidence is injected as a causal prompt into the frozen Cosmos3 action generator, enabling zero-gradient adaptation and one-shot learning from human demonstrations.
  • On ChemLab-Evo, repeated attempts improved success from 65% to 100% for picking up test tubes, 25% to 70% for placing beakers, and 30% to 80% for pouring water.

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自进化WAM来了!清华AIR联手域变换提出具身In-Context Causal Learning

量子位 思邈 2026-09-01
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Providers: Hugging Face · N/A OpenAlex · N/A Publisher · N/A Semantic Scholar · N/A X · N/A Fetched 2026-09-26 14:17:18.214781 UTC

TL;DR - Tsinghua AIR and startup Yubianhuan introduced Zeva, an embodied world-action model that learns from action outcomes and human demonstrations at deployment time without updating its weights. Its causal memory raised cumulative success from 26% to 73% across repeated attempts on a simulated benchmark and improved real-world robotic lab tasks.

  • Zeva encodes visual state, actions, and resulting state changes into causal interaction signals that capture action-effect relationships.
  • Dual-timescale memory maintains short-term execution context while retaining reusable evidence from failures, corrections, and successes across attempts.
  • Retrieved evidence is injected as a causal prompt into the frozen Cosmos3 action generator, enabling zero-gradient adaptation and one-shot learning from human demonstrations.
  • On ChemLab-Evo, repeated attempts improved success from 65% to 100% for picking up test tubes, 25% to 70% for placing beakers, and 30% to 80% for pouring water.
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