This paper introduces a local search method for improving an existing program
with respect to a measurable objective. Program Optimization with Locally
Improving Search (POLIS) exploits the structure of a program, defined by its
lines. POLIS improves a single line of the program while keeping the remaining
lines fixed, using existing brute-force synthesis algorithms, and continues
iterating until it is unable to improve the program's performance. POLIS was
evaluated with a 27-person user study, where participants wrote programs
attempting to maximize the score of two single-agent games: Lunar Lander and
Highway. POLIS was able to substantially improve the participants' programs
with respect to the game scores. A proof-of-concept demonstration on existing
Stack Overflow code measures applicability in real-world problems. These
results suggest that POLIS could be used as a helpful programming assistant for
programming problems with measurable objectives.

本文介绍了一种用于改进现有程序的本地搜索方法，即基于单项游戏评分提高程序效率的 POLIS 方法，在 27 人的用户研究中得到验证，可以作为可衡量目标的编程问题的有用助手。

使用局部搜索优化程序：你能优化我的代码吗？

Can You Improve My Code? Optimizing Programs with Local Search

Polis is a platform that leverages machine intelligence to scale up
deliberative processes. In this paper, we explore the opportunities and risks
associated with applying Large Language Models (LLMs) towards challenges with
facilitating, moderating and summarizing the results of Polis engagements. In
particular, we demonstrate with pilot experiments using Anthropic's Claude that
LLMs can indeed augment human intelligence to help more efficiently run Polis
conversations. In particular, we find that summarization capabilities enable
categorically new methods with immense promise to empower the public in
collective meaning-making exercises. And notably, LLM context limitations have
a significant impact on insight and quality of these results.
However, these opportunities come with risks. We discuss some of these risks,
as well as principles and techniques for characterizing and mitigating them,
and the implications for other deliberative or political systems that may
employ LLMs. Finally, we conclude with several open future research directions
for augmenting tools like Polis with LLMs.

研究使用大型语言模型进行 Polis 参与的挑战，并发现总结能力使公共意义建设得到新的有效方法，并讨论了其风险与各个方面的应用。

LLMs 对基于 Polis 可伸缩性讨论的机遇与风险

Opportunities and Risks of LLMs for Scalable Deliberation with Polis

AI systems may be better thought of as peers than as tools. This paper
explores applications of augmented collective intelligence (ACI) beneficial to
collaborative ideation. Design considerations are offered for an experiment
that evaluates the performance of hybrid human- AI collectives. The
investigation described combines humans and large language models (LLMs) to
ideate on increasingly complex topics. A promising real-time collection tool
called Polis is examined to facilitate ACI, including case studies from citizen
engagement projects in Taiwan and Bowling Green, Kentucky. The authors discuss
three challenges to consider when designing an ACI experiment: topic selection,
participant selection, and evaluation of results. The paper concludes that
researchers should address these challenges to conduct empirical studies of ACI
in collaborative ideation.

研究探讨使用增强的集体智能的应用，结合人类和大型语言模型在协同创意中的表现，进一步介绍了一个名为 Polis 的实时集合工具，并总结出三个挑战以期设计并完成实验。