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Understanding the Challenges in Iterative Generative Optimization with LLMs research paper by Google DeepMind, 2026

Google DeepMind · Mar 25, 2026 · Foundation models · 8 citations · 27 upvotes · unverified

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What it shows

Generative optimization using large language models faces challenges due to implicit design decisions about artifact modification and learning evidence that significantly impact success across different applications.

UnverifiedHugging Face's summary; not yet checked by hand.

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About this paper
Authors
Allen Nie, Xavier Daull, Zhiyi Kuang and 10 more
arXiv
2603.23994 · PDF
Venue
arXiv.org
Citations
8, 1 influential · Semantic Scholar
Upvotes
27 · Hugging Face
Code
github.com/ameliakuang/LLM-Game-Playing-Agents
Lab
Google DeepMind · on Companies · on Acquisitions

Changes

What changed
Influential citationsfirst count: 1Sep 25, 2026
Citationsfirst count: 8Sep 25, 2026
New paperFound by the weekly scan, unverifiedSep 25, 2026

Sources: each lab's own papers and arXiv, with citation and upvote counts from Semantic Scholar and Hugging Face. One-line summaries are for orientation, not a substitute for the paper. Logos via logo.dev; trademarks belong to their owners.

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