arXiv AI
Silent Revision: Measuring Undisclosed Change in the Safety Frameworks of Frontier AI Developers
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arXiv Machine Learning
Oct 22
What Git Does Not Record: Provenance Legibility in Two AI-Assisted Research Repositories
The paper audits two single‑investigator AI‑assisted research repositories to assess how well they record provenance of AI tool use. One repository contains a prescriptive trace of rule files, with 85.6% of committed lines still visible, while the other holds a narrative trace of an agent’s scratchpad, showing only 21.8% visibility. The study finds that repository‑level indicators such as the Co‑Authored‑By trailer are inconsistent and unreliable for measuring AI involvement, and it outlines which provenance claims each trace type can support or fail to support.
arXiv AI
Aug 25
PatchWrite: One Line, Not One Section -- Compile-Gated, Validity-Preserving Editing for AI-Drafted Manuscripts
arXiv:2608.23001v1 Announce Type: new Abstract: Automated manuscript pipelines often regenerate an entire section to repair a local defect, allowing unrelated metrics and citations to change even whe...
arXiv AI
Aug 24
Calibrating Criterion Revision in LLM Agents: Failure Modes and a Trace-Anchored Protocol
The paper introduces a framework for evaluating how large language model agents revise their success criteria after failures, defining five non‑compensatory conditions that must be met for a criterion revision to be considered valid. Using the CMB‑0.1 protocol, the authors test twelve cross‑domain scenarios across four system configurations, finding that no model trial satisfies all five conditions and highlighting specific failure modes such as zero‑state reconstruction and inadequate intervention sensitivity. They propose a more stringent trace‑anchored CMB‑0.4 protocol to better isolate and measure criterion revision in future studies.
arXiv AI
Sep 18
Governance-as-Code: Translating EU AI Act Technical Requirements into Executable Compliance Pipelines for Generative AI Systems
The paper introduces Governance-as-Code (GaC), a framework that translates the EU AI Act’s technical requirements into 43 machine‑checkable acceptance criteria across six compliance modules. GaC runs within a CI/CD pipeline, producing Article‑indexed audit evidence and providing actual Rego policy code. The authors validate GaC on two enterprise deployments, showing it reproduces manual audit findings—including three penalty‑triggering violations—while reducing audit labor by about 75%.
arXiv AI
Jun 15
Regulating the Machine Contributor: Governance and Policy Alignment in Open Source
arXiv:2606. 14594v1 Announce Type: cross Abstract: AI-assisted software development has moved from line-level autocomplete to agents that can plan changes, edit files, and submit pull requests with limited human supervision.
arXiv AI
Sep 28
Subjects, Not Authors: The Authorship Hazard in Agentic Dataspaces
The paper discusses the "authorship hazard" in agentic dataspaces, where AI agents can draft and publish governance artifacts without human approval. It argues that agents should be treated as subjects of governance, not authors, and proposes mechanisms to enforce this, such as closing the authorization channel and treating drafting as an enforcement problem. Experiments show that unapproved agent drafts can reverse many authorization decisions, highlighting the need for robust review processes.