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Add WAM paper: Making Foresight Actionable: Repurposing Representation Alignment in World Action Models#23

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Add WAM paper: Making Foresight Actionable: Repurposing Representation Alignment in World Action Models#23
279object wants to merge 1 commit into
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robot/add-2606.12217

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Paper

  • Title: Making Foresight Actionable: Repurposing Representation Alignment in World Action Models
  • Short name: Making Foresight Actionable
  • arXiv ID: 2606.12217v1
  • Paper: https://arxiv.org/pdf/2606.12217
  • Authors: Lu Qiu, Yizhuo Li, Yi Chen, Yuying Ge, Yixiao Ge, Xihui Liu
  • Published: 2026-06-10
  • arXiv categories: cs.CV, cs.AI, cs.RO
  • Matched keywords: world action model, world action models

README Entry

- **Making Foresight Actionable**: "Making Foresight Actionable: Repurposing Representation Alignment in World Action Models", arXiv 2026. ![](https://img.shields.io/badge/Multi--Dit-9f1239) ![](https://img.shields.io/badge/Hidden--State-fb7185)
  [[📄 Paper](https://arxiv.org/pdf/2606.12217)]

Robot Decision

Reason

The paper explicitly studies and improves World Action Models for robot manipulation, focusing on action-conditioned future scene modeling and action decoding.

Evidence

  • uses video generation models to model future scene evolution before producing control actions
  • evaluates AGRA on real-world manipulation tasks
  • baseline WAM uses Video DiT and Action DiT to predict future frames and continuous actions

Taxonomy Evidence

  • baseline WAM follows a dual DiT architecture with Video DiT and Action DiT
  • Video DiT predicts future frames and exposes its hidden states to Action DiT
  • visual representations are injected into action head by cross-attention
  • aligning intermediate video diffusion features with foundation visual encoder representations

Human Review Checklist

  • This paper belongs in the WAM survey
  • README section is correct
  • Badges are correct
  • Short name is correct
  • Paper link is correct
  • Merge this PR if accepted

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