arXiv cs.MA· Chai K. Toh, Vasu Raj Jain·· 2 天前AI 评分31
多层级 AI 可及性模型 MAIAM：面向前沿 AI 安全的按比例访问与能力限制
A Multi-Tiered AI Accessibility Model (MAIAM): Proportionate Access and Capability Limits for Comprehensive Frontier AI Safety
AI 导读
论文提出多层级 AI 可及性模型 MAIAM，按用途、安全与能力而非发布形式划分访问权限：Tier 1 面向学习、广泛可及且能力受限，Tier 2 面向研发、需注册与身份管控，Tier 3 全规模前沿 AI 仅限结构化、可记录、可问责的访问。
正文 · 原文
Abstract:Most debate about releasing capable AI systems treats the choice as binary: keep the model behind an API, or publish the weights. That framing runs together two separate decisions. One is how capable a system may become. The other is who may obtain a given capability, and on what terms. This paper is about the second decision. We propose a Multi-Tiered AI Accessibility Model (MAIAM) that sorts access by purpose, safety, and capability instead of by release artifact. Tier 1, AI for Learning, is broadly accessible and deliberately capability-bounded. Tier 2, AI for Exploration, serves research and development under registration, identity, and usage controls that tighten as capability rises. Tier 3, Full-Scale Frontier AI, is available only through structured, recorded, and accountable access. The principle behind the tiers is that accessibility should be proportional to capability, safety, and risk. We show why publishing frontier-class weights forfeits every safety lever a developer has after release, and, using evidence that safety fine-tuning can be stripped from open weights for a few hundred dollars, why an evaluation of the base model says little about its derivatives. We give criteria for assigning a system to a tier along capability, autonomy, domain-consequence, and usage/safety axes, and combine MAIAM with the SAFE AI Operating Point into a two-part framework: a Capability Boundary that fixes the safety limits not to be crossed, and an Accessibility Boundary that fixes who may obtain a capability below that limit. Capability limits alone do not make AI safe; a tiered access regime administered by regulating bodies is also needed.
| Subjects: | Artificial Intelligence (cs.AI); Multiagent Systems (cs.MA) |
| Cite as: | arXiv:2610.20921 [cs.AI] |
| (or arXiv:2610.20921v1 [cs.AI] for this version) | |
| https://doi.org/10.48550/arXiv.2610.20921 arXiv-issued DOI via DataCite (pending registration) |
Submission history
From: Vasu Raj Jain [view email]
[v1]
Wed, 14 Oct 2026 20:52:28 UTC (1,001 KB)
来源：arXiv cs.MA · arxiv.org