惯性聚合 高效追踪和阅读你感兴趣的博客、新闻、科技资讯
阅读原文 在惯性聚合中打开

推荐订阅源

V
V2EX
C
Check Point Blog
博客园_首页
B
Blog
D
Docker
U
Unit 42
量子位
I
InfoQ
有赞技术团队
有赞技术团队
Martin Fowler
Martin Fowler
GbyAI
GbyAI
L
LangChain Blog
云风的 BLOG
云风的 BLOG
博客园 - Franky
美团技术团队
T
The Blog of Author Tim Ferriss
阮一峰的网络日志
阮一峰的网络日志
月光博客
月光博客
Vercel News
Vercel News
Recent Announcements
Recent Announcements
雷峰网
雷峰网
大猫的无限游戏
大猫的无限游戏
小众软件
小众软件
Google DeepMind News
Google DeepMind News

cs updates on arXiv.org

Beyond Binary Edits Robust Multimodal Knowledge Editing with Adversarial Subspace Alignment Agentic Proving for Program Verification MemAudit: Post-hoc Auditing of Poisoned Agent Memory via Causal Attribution and Structural Anomaly Detection OpenSkillEval: Automatically Auditing the Open Skill Ecosystem for LLM Agents One Policy, Infinite NPCs: Persona-Traceable Shared RL Policies for Scalable Game Agents How Human-Like Are Large Language Models? A Register-Aware Linguistic Evaluation Framework Benchmarking Google Embeddings 2 against Open-Source Models for Multilingual Dense Retrieval and RAG Systems Structure-Guided Entity Resolution: Fine-Tuning LLMs for Robust Name Matching in Complex Linguistic Contexts Solving the Aircraft Disassembly Scheduling Problem Co-ReAct: Rubrics as Step-Level Collaborators for ReAct Agents CP or DP? Why Not Both: A Case Study in the Partial Shop Scheduling Problem Asking For An Old Friend: Diagnosing and Mitigating Temporal Failure Modes in LLM-based Statutory Question Answering EDGE-OPD: Internalizing Privileged Context with Evidence Guided On-Policy Distillation ARES: Automated Rubric Synthesis for Scalable LLM Reinforcement Learning SSDAU: Structured Semantic Data Augmentation for Joint Entity and Relation Extraction Naturalistic measure of social norms alignment Articulatory strategy as a source of variation in acoustic vowel dynamics When Planning Fails Despite Correct Execution: On Epistemic Calibration for LLM-Based Multi-Agent Systems EquiSumm : A Gender Bias-Aware Framework for Inclusive Tweet Summarization Metacognition as Reward: Reinforcing LLM Reasoning via Knowledge and Regulation Signals From Correctness to Preference: A Framework for Personalized Agentic Reinforcement Learning Cultural Adaptation in Large Language Models for Political Discourse Emotion Recognition in Sign Language Conversation ClimateChat-300K: A Multi-Modal Facebook Dataset for Understanding Diverse Perspectives in Climate Communication AraHopeCorpus: Annotation Guidelines and Dataset for Hope Speech in Arabic Social Media Crisis Discourse Human-in-the-Loop Multi-Agent Ventilator Decision Support with Contextual Bandit Preference Learning Convergence Without Understanding: When Language Models Agree on Representations but Disagree on Reasoning DART: Semantic Recoverability for Structured Tool Agents Ontological Knowledge Blocks: Executable Compliance and Profile-Based Validation for Trustworthy AI Systems Parallel Context Compaction for Long-Horizon LLM Agent Serving
Fifty Years of Specification Completeness: What Aviation ...
[Submitted on 23 Jun 2026] · 2026-06-25 · via cs updates on arXiv.org

View PDF HTML (experimental)

Abstract:Aviation software certification has operationalised three structural requirements for governed software systems since 1992: structured governance linkage between governing specifications and operational evidence, context-bounded validity that triggers revalidation when operational context changes, and an objective evidence architecture that defines what proof means and what makes it sufficient. These requirements appear in DO-178C and DO-330 and are enforced through FAA and EASA certification.
No existing framework requires these structural properties as intrinsic properties of individual AI governance documents. A system prompt, an this http URL file, a governance policy, or a task envelope can be deployed without satisfying any of the three requirements aviation has enforced for three decades. Aviation is the most technically rigorous instance: its standard-setting bodies have acknowledged that their frameworks break down for AI systems, yet none requires these properties of individual governance documents.
Aviation's structural requirements break down at the system level because AI systems are non-deterministic, but remain transferable at the document level: the governance artifact is a static artifact whose structural properties can be evaluated independently of the stochastic system it governs.
The paper maps DO-178C's traceability architecture, DO-330's requalification triggers, and DO-178C's objective evidence requirements onto three structural findings: epoch limits on governance document validity, proof surfaces as the revalidation feedback mechanism, and the absence of structural completeness requirements in AI governance instruments. An empirical companion (arXiv:2604.21090) found that 37% of AI governance documents fall below the structural quality threshold. PromptQ's seven-principle framework operationalises these requirements at the governance document layer.

Submission history

From: Christiaan Zietsman [view email]
[v1] Tue, 23 Jun 2026 19:51:13 UTC (83 KB)