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Rylan Schaeffer

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#machine learning Preprint Sep 2026

How Strong Is the Evidence for the Artificial Hivemind? Reevaluating Evidence for the Open-Ended Homogeneity of Language Models

The paper concludes that inference-time interventions are inadequate for combating the Artificial Hivemind, and shows that more generalizable solutions are needed at the model training level, and that an inference-time intervention (prompting) reliably raises measured response diversity.

Rylan Schaeffer, Brando Miranda, Joshua Kazdan et al. · 0 citations

Jailbreaking Jailbreaks: A Proactive Defense for LLMs

P RO A CT represents an orthogonal defense strategy that serves as an additional guardrail to enhance LLM safety against the most effective attacks.

Wei-Liang Zhao, Daniel Ben-Levi, Jinjun Peng et al. · 1 citation
Preprint Aug 2026

A Few Pages of Markdown: Committed AI Configuration and Lower Quality Cost after Coding-Agent Adoption

Coding agents increase development velocity but also technical debt. Prior work reports only average effects across adopters, hiding wide differences between teams. We introduce RAMP (Repository AI Maturity Profile), a four-level cumulative maturity model grounded in version-controlled artifacts that teams commit to co...

Yegor Denisov-Blanch, Shyam Agarwal, Pavel Azaletskiy et al. · 0 citations

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