---
title: "GREP: Grounding Generated Room Evaluation in Principles"
source: "arXiv AI/CL/LG（论文）"
source_tier: "t2"
url: "https://arxiv.org/abs/2611.05802v1"
permalink: "https://feed.astralor.com/items/itm_d79bda4bf9f4fb8c3548f27e"
published: "2026-11-04T01:19:12.000Z"
score: 59
channel: "tech"
tags: ["LLM","AI Agent","OpenAI","GREP","Grounding Generated Room","Evaluation","Principles Recent LLM-centric","Yet"]
---

# GREP: Grounding Generated Room Evaluation in Principles

## 速览

Recent LLM-centric advances can generate attractive furnished rooms by selecting and arranging assets. Yet evaluation remains limited to specific perspectives or predefined questions, failing to reflect overall scene qu…

## 关键点

- Recent LLM-centric advances can generate attractive furnished rooms by selecting and arranging assets.
- Yet evaluation remains limited to specific perspectives or predefined questions, failing to reflect overall scene quality.
- To address this pressing need, we introduce GREP.

## 全文

Recent LLM-centric advances can generate attractive furnished rooms by selecting and arranging assets. Yet evaluation remains limited to specific perspectives or predefined questions, failing to reflect overall scene quality. To address this pressing need, we introduce GREP. Its core is a focus shift from defining specific questions to establishing a three-tier evaluation philosophy: basic physical validity checks whether a scene obeys physical constraints; visual coherence concerns compatibility in object scale, style, and composition; and, at the highest level, human-use logic requires object arrangements to align with everyday human habits. Guided by these principles, an evaluation agent autonomously and adaptively identifies issues from scene context, gathers evidence, and produces scores and reports. This supports scene-specific issue discovery and reliable judgments. Experiments show that (1) GREP achieves 87% agreement with human majority preferences, substantially outperforming baseline evaluators. (2) GREP reports improve scene quality through refinement, whereas score-only and no-feedback baselines show slight declines. (3) GREP suggests that as planning ability improves, recent models such as GPT-5.6 Sol can move from relying on pipelines and harnesses to challenging their guidance and constraints, but still lack the ability to resolve the resulting conflicts. Stronger models are therefore expected not only to plan well, but also to adapt to external guidance and constraints more flexibly. Project page: https://han-808.github.io/GREP/

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