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SoL-Pi: Recursively Scaling Auto-Research Loops for Efficient Agent Harness research paper by NVIDIA, 2026

NVIDIA · Sep 17, 2026 · Agents and evaluation · 1 citations · 130 upvotes · unverified

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What it shows

As coding agents move from supervised code completion to unattended, around-the-clock exploration, their work expands from isolated predictions into long trajectories of reasoning, tool use, and feedback.

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

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About this paper
Authors
Haozhe Liu, Tian Ye, Sensen Gao and 11 more
arXiv
2609.20519 · PDF
Citations
1, 0 influential · Semantic Scholar
Upvotes
130 · Hugging Face
Code
github.com/NVlabs/SoL-Pi
Lab
NVIDIA · on Companies · on Acquisitions · on Quarterly · on Paydays · on TechConf

Changes

What changed
Influential citationsfirst count: 0Sep 25, 2026
Citationsfirst count: 1Sep 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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