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LLMs Improving LLMs: Agentic Discovery for Test-Time Scaling research paper by Google, 2026

Google · May 8, 2026 · Agents and evaluation · 2 citations · 70 upvotes · unverified

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What it shows

AutoTTS automates test-time scaling strategy discovery by formulating it as controller synthesis over reasoning trajectories and probe signals, achieving improved accuracy-cost tradeoffs with minimal computational overhead.

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

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About this paper
Authors
Tong Zheng, Haolin Liu, Chengsong Huang and 10 more
arXiv
2605.08083 · PDF
Venue
arXiv.org
Citations
2, 0 influential · Semantic Scholar
Upvotes
70 · Hugging Face
Code
github.com/zhengkid/AutoTTS
Lab
Google · on Companies · on Acquisitions · on Paydays · on TechConf · on Releases

Changes

What changed
Influential citationsfirst count: 0Sep 25, 2026
Citationsfirst count: 2Sep 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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