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

NVIDIA · Inference and efficiency · Sep 17, 2026 · 13 citations · 137 upvotes · unverified

SoL-Pi scales auto-research loops for coding agents by optimizing harness-layer mechanisms to reduce token traffic and costs.

By Haozhe Liu, Tian Ye, Sensen Gao and 11 more · arXiv 2609.20519 · PDF · Code

UnverifiedSummarised by a model from the abstract; not yet checked by hand.

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About this paper

Venue: arXiv.org
Citations: 13, 0 influential · Semantic Scholar
Upvotes: 137 · Hugging Face
Lab: NVIDIA · on Companies · on Acquisitions · on Quarterly · on Paydays · on TechConf

Changes

CitationsNov 9, 2026Citations rose from 4 to 13.

New summaryOct 26, 2026SoL-Pi scales auto-research loops for coding agents by optimizing harness-layer mechanisms to reduce token traffic and costs.

TopicOct 26, 2026Moved from Agents and evaluation to Inference and efficiency.

CitationsOct 5, 2026Citations rose from 2 to 4.

UpvotesSep 28, 2026Hugging Face upvotes rose from 130 to 137.

CitationsSep 28, 2026Citations rose from 1 to 2.

CitationsSep 25, 2026A first count of 0 influential citations.

CitationsSep 25, 2026A first count of 1 citations.

New paperSep 25, 2026Found by the weekly Hugging Face scan; not yet checked by hand.