Review possible WAM paper: QuantWAMs: Calibrating at the Right Granularity for World Action Models

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Paper

• Title: QuantWAMs: Calibrating at the Right Granularity for World Action Models
• Short name: QuantWAMs
• arXiv ID: 2607.28405v1
• Paper: https://arxiv.org/pdf/2607.28405
• Authors: Jiacheng Zhou, Jinfan Lv, Ruixuan Li, Longtai Zhang, Yan Wang, Wenqiang Zhang, Lizhe Qi
• Published: 2026-07-30
• arXiv categories: cs.AI, cs.LG
• Matched keywords: WAM, world action model, world action models

README Entry

- **QuantWAMs**: "QuantWAMs: Calibrating at the Right Granularity for World Action Models", arXiv 2026. ![](https://img.shields.io/badge/Needs--Taxonomy--Review-64748b)
  [[📄 Paper](https://arxiv.org/pdf/2607.28405)]

Robot Decision

• Decision: pending
• Confidence: unknown
• Taxonomy: None
• Taxonomy confidence: None
• README heading: ## World Action Model
• Badges: https://img.shields.io/badge/Needs--Taxonomy--Review-64748b

Reason

LLM WAM detection failed: LLM request failed: HTTP 522:

Evidence

• WAM
• world action model
• world action models

Needs taxonomy review: fallback README heading/badge was used because taxonomy.toml has no README mapping for this classification.

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