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#llm-policy

Finding a home amidst the wreckage

Lobsters Hottest ↗ · 2026-10-19 Cached

A free software developer shares their move away from Debian to Chimera Linux, drawn by its firm anti-LLM contribution policy, unconventional tech stack, and welcoming community.

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#llm-policy

Policy-Conditioned AI-Use Detection: An Evidentiary Framework for Academic Publishing

arXiv cs.CL ↗ · 2026-10-01 Cached

The paper argues that standard AI-text detectors measure the wrong thing for academic venues and proposes policy-conditioned AI-use detection, an evidentiary framework that takes venue policies as explicit inputs, reports calibrated hypotheses and uncertainty instead of binary verdicts, and builds benchmarks from reproducible compliant/non-compliant workflow pipelines.

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#llm-policy

StepCAD: Mesh-to-CAD Code Generation via LLM Policy and Geometry-Guided Search

Hugging Face Daily Papers ↗ · 2026-10-01 Cached

StepCAD 提出了一种将状态条件化 CAD 策略与 IoU 引导树搜索相结合的生成式优化方法,从 3D 网格重建可执行 CAD 程序,并发布了包含 150 万个程序的 ARCADE-1.5M 大规模数据集,在多个基准上实现了最先进的几何重建精度。

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#llm-policy

The GNOME LLM Policy That I Want

Lobsters Hottest ↗ · 2026-09-23 Cached

The article proposes an LLM policy for the GNOME project that bans LLM-generated contributions to protect the human-centric values of the community, contrasting with KDE's approach and emphasizing social norms over workflow management.

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#llm-policy

Democratizing Abandonware

Lobsters Hottest ↗ · 2026-07-08 Cached

Flathub announces it will no longer accept AI-generated submissions, citing volunteer reviewer burnout and spam from automated tooling like OpenClaw. The post explores the history of Flathub's review process and the community's reaction.

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#llm-policy

NLNet Labs LLM Policy

Lobsters Hottest ↗ · 2026-06-27 Cached

NLNet Labs announces a policy restricting the use of LLMs in code and documentation contributions, requiring disclosure of LLM use and prohibiting AI-generated code.

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#llm-policy

Discovering Cooperative Pipelines: Autoresearch for Sequential Social Dilemmas

Hugging Face Daily Papers ↗ · 2026-05-28 Cached

This paper presents a two-level autoresearch framework where an outer-loop AI agent autonomously optimizes inner-loop LLM policy-synthesis pipelines for multi-agent sequential social dilemmas, achieving superior performance and discovering objective-specific mechanisms like fairness under a maximin welfare objective.

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