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ICLR 2027 AI Policy for Authors

LLMs are increasingly finding use in various research workflows, varying in sophistication from formulating proofs to formatting tables. We ask that authors explicitly state how they used LLMs in their submission, both in the paper’s text as well as in the paper submission form. To guide authors about the nature and extent of reporting, we provide a list of common research subtasks along with the necessity of disclo…

ICLR 2027 AI Policy for Authors

Improving Policy Learning via Language Dynamics DistillationCohere

OPERA: Automatic Offline Policy Evaluation with Re-weighted Aggregates of Multiple EstimatorsCohere

Policy Primer - Translating SafetyCohere Policy Primer - Efficient AICohere Contrastive Policy Gradient: Aligning LLMs on sequence-level scores in a supervised-friendly fashionCohere Policy Primer - The AI Language GapCohere

Empower Your Team with Policy as CodePulumi

New Policy as Code Capabilities with CrossGuardPulumi

Go Wiki: PortingPolicy - The Go Programming LanguageGo

Thanks for the Memories: The Power of Event SourcingThenewstack

Policy as Code for Any Cloud ProviderPulumi

Achieve security and compliance goals with policy guardrails in Docker ScoutDocker

DEPLOYMENT_DELETEDVercel

Policy Comes to Team and Enterprise, with New Out-of-the-box Policies and Management ExperiencePulumi

Fetch directive - GlossaryDeveloper Mozilla

Navigation directive - GlossaryDeveloper Mozilla

Reporting directive - GlossaryDeveloper Mozilla

Backward compatibility guidelines for library authorsKotlinlang

Policy-as-Code: Flexible Kubernetes governance with KyvernoCncf

Introducing the pulumi policy analyze Command for Existing StacksPulumi