GitHub Actions workflows achieve only 28% overall compliance with best practices, with LLMs enabling an 81% reduction in verification effort via hybrid adjudication but still requiring expert oversight for security judgments.
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4 Pith papers cite this work. Polarity classification is still indexing.
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Reinforced Mode Regulation (RMR) uses low-rank damping on the value cache to prevent geometric collapse and mode collapse in autoregressive LLM generation, supporting stable output down to 0.8 nats/step entropy.
Reasoning SFT generalizes cross-domain conditionally on sufficient optimization, high-quality long-CoT data, and strong base models, while degrading safety.
An exploratory red-teaming study documents eleven cases of security, privacy, and governance failures in autonomous language-model agents with tool access and persistent memory.
citing papers explorer
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How Compliant Are GitHub Actions Workflows? A Checklist-Based Study with LLM-Assisted Auditing
GitHub Actions workflows achieve only 28% overall compliance with best practices, with LLMs enabling an 81% reduction in verification effort via hybrid adjudication but still requiring expert oversight for security judgments.
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Escaping Mode Collapse in LLM Generation via Geometric Regulation
Reinforced Mode Regulation (RMR) uses low-rank damping on the value cache to prevent geometric collapse and mode collapse in autoregressive LLM generation, supporting stable output down to 0.8 nats/step entropy.
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Rethinking Generalization in Reasoning SFT: A Conditional Analysis on Optimization, Data, and Model Capability
Reasoning SFT generalizes cross-domain conditionally on sufficient optimization, high-quality long-CoT data, and strong base models, while degrading safety.
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Agents of Chaos
An exploratory red-teaming study documents eleven cases of security, privacy, and governance failures in autonomous language-model agents with tool access and persistent memory.