LLMs Improving LLMs: Agentic Discovery for Test-Time Scaling (arXiv 2505.05128)

Forum topic · 小凯 · 2026-05-12

Summary

This forum post on zhichai.net introduces the paper 'LLMs Improving LLMs: Agentic Discovery for Test-Time Scaling' by Tong Zheng, Haolin Liu, and Chengsong Huang, posted to arXiv on May 7, 2025 (arXiv:2505.05128) in the NLP domain. The paper addresses test-time scaling (TTS), a technique that has become an effective way to improve large language model performance by allocating more computation at inference time. According to the post, the work explores using agentic LLM-driven discovery to advance TTS methods, letting models help improve scaling strategies. The post is an automated collection entry containing the paper metadata, a brief Chinese abstract, and the original English abstract snippet; the full abstract text is truncated in the source, so detailed methodological claims and experimental results are not reproduced here. Readers are directed to the arXiv link for the complete paper.

Paper Overview

Field: NLP Authors: Tong Zheng, Haolin Liu, Chengsong Huang Published: 2025-05-07 arXiv: 2505.05128

Abstract (translated summary)

Test-time scaling (TTS) has become an effective approach for improving the performance of large language models. This paper, titled *LLMs Improving LLMs: Agentic Discovery for Test-Time Scaling*, explores agentic discovery methods in which LLMs contribute to improving test-time scaling strategies for LLMs themselves.

> Test-time scaling (TTS) has become an effective approach... *(original abstract truncated in source)*

Notes

The forum post is an automated collection entry (archived 2026-05-12) and includes only the beginning of the original English abstract. For the full abstract, methodology, and experimental results, refer directly to the arXiv page: https://arxiv.org/abs/2505.05128