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A dangerous-capability threshold should trigger safety evaluation before an irrevocable open-weight release, since a closed model can still be patched afterward and an open one cannot.
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Jul 4, 2026
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Adopt the capability-threshold evaluation framework from AI-02 as the single trigger for pre-release review, regardless of intended release strategy. For any model that crosses that threshold, require completed evaluation before an open-weight release specifically (closed release can proceed under AI-02's existing post-release reporting and review obligations, since a closed model remains patchable/restrictable after deployment in a way an open one does not). Maintain current federal policy support for open-weight competitiveness as consistent with the platform's research-and-collaboration value; route adversary-model national-security concerns through narrow procurement restrictions (AI-07's domain) rather than domestic release restrictions; and fund independent monitoring capacity so the underlying risk/benefit judgment is revisited on a defined cadence, not left to age silently.
The regulatory chokepoint should be capability-and-irrevocability, not an open-versus-closed philosophical binary — the narrow claim is that whether a release decision can be undone matters more than whether the weights are public, which is a question about release timing, not about openness as a value.
Primary — Research, Innovation, and Collaboration. "We endorse open-source development and collaboration" is not a peripheral value here: it is the entire reason this issue resists a blanket restriction on open-weight release, and it's why Proposal 3 explicitly treats continued open-weight competitiveness as consistent with, not opposed to, this platform's values.
Secondary and directly in tension — Privacy, Security, and Trust. The irrevocability argument is real, and the party does not wave it away: "we prioritize cybersecurity to protect privacy and national security" is squarely implicated by a model whose safety training can be stripped in minutes once weights are public. The resolution is narrow and structural (Proposal 2's release-timing rule) rather than either value overriding the other across the board.
The current Republican administration's AI Action Plan and its 2026 executive order are explicitly, officially pro-open-weight, framing openness as geostrategically valuable. That's a stated policy position, not an inference. Concern about adversary-controlled models has drawn bipartisan action instead: the House Select Committee on the CCP's alarm over DeepSeek, and bills like the "No DeepSeek on Government Devices Act" (Reps. LaHood, R, and Gottheimer, D). That bipartisan concern is about adversary-origin models specifically, not open-weight release in general, which is consistent with, not opposed to, the administration's pro-domestic-open-weight stance. This isn't a clean partisan divide so much as a shared adversary-vs-domestic distinction layered on top of an open-vs-closed debate neither party has taken a hard official side on. The Innovation Party's delta: its capability-triggered, release-timing-specific safeguard is more granular than either party's current position, which tends toward "open is good, adversary models are bad" without engaging the narrower irrevocability argument this issue makes.
The strongest good-faith objection, already conceded in this issue's strategy layer: measuring model capability accurately enough to justify delaying an open release is not yet a mature science. A critic could argue that without a reliable measurement, a "capability trigger" is functionally just as arbitrary as the blanket open-vs-closed rule this issue explicitly rejects. It offers the appearance of precision without yet having the substance to back it, since an evaluation regime that can't reliably tell "safe to release" from "not yet" doesn't fully resolve the irrevocability problem on day one. An imperfect capability trigger is still an improvement over the status quo it replaces: a blanket rule with no trigger at all, or no rule whatsoever. Evaluation science maturing over time is an argument for funding that science now, which this issue's own proposal already does, not a reason to fall back to a cruder rule while waiting for a precision that may take years to arrive.
The open-source developer community bears friction and delay if evaluation requirements slow releases, especially while evaluation science is still maturing. National security bears the risk if evaluation science lags capability growth and a dangerous model clears a review that wasn't rigorous enough to catch it. Smaller model developers bear a proportionally higher compliance burden relative to well-resourced labs that can absorb evaluation costs more easily, a version of the same structural concern this issue already names regarding AI-02 and AI-10. The same answer applies here: the compute threshold itself is what keeps this cost confined to labs training at frontier scale, not imposed on the far larger population of smaller developers fine-tuning or building on top of existing models.
Turn frustration into useful pressure.
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