When Rio de Janeiro's municipal IT company IplanRIO dropped "Rio 3.5 Open 397B" on Hugging Face this week, the press release practically wrote itself. A city government builds a frontier-class AI model from scratch, releases it under MIT license, and it beats Qwen 3.7 on benchmarks. The AI community lost its collective mind.

Then someone looked at the weights.

What Actually Happened

On June 13, IplanRIO published Rio-3.5-Open-397B, a 397-billion-parameter mixture-of-experts model claiming to be a "post-trained iteration of Qwen 3.5 397B" with a proprietary technique called "SwiReasoning." The model card described it as developed in-house by the city government's IT arm. Benchmark numbers looked strong: +18.3 on Terminal-Bench 2.1, +19.8 on Apex, +7.8 on HLE. It was supposed to be proof that a municipal government could compete with Alibaba and Anthropic.

Within hours, the Nex-AGI team opened a GitHub issue with the blunt title: "Rio-3.5-Open-397B is approximately 0.6 x Nex-N2_pro + 0.4 x Qwen."

Their evidence was not subtle.

The Weight Analysis

Nex-AGI's researchers performed what amounts to a mathematical autopsy on the model's tensors. The method is straightforward: if Rio is truly an original fine-tune of Qwen, then its weight deviations from Qwen should be random. If it is actually a merge of Nex and Qwen, those deviations should point in exactly the same direction as Nex's deviations from Qwen, at a predictable ratio.

They measured a "cos_fit" (cosine similarity of weight deviations) of 0.98 to 0.99 across every layer and component of the network. In a 2-million-dimensional weight space, that is not "high similarity." It is thousands of standard deviations from what you would see with independently trained models. The mixing ratio stayed remarkably stable: 0.571 for routed experts, 0.574 for the language model head, roughly 0.585 for attention layers.

"A cos_fit of 0.99 in a 2-million-dimensional space is not 'high similarity.' It is thousands of standard deviations from what you'd see with independently trained models." , Nex-AGI team

The Identity Leak

The behavioral evidence was even more damning. Without the hard-coded system prompt that forces the model to identify as "Rio," the model consistently identifies as "Nex." When prompted 120 times without the system instruction, it responded with "Nex" 79.2% of the time and "Nex-AGI" 73.3% of the time. It said "Rio" exactly 0.0% of the time.

It also recited Nex-AGI's organizational backstory verbatim, including specific phrases about a "large-model ecosystem alliance" and the "Shanghai Innovation Institute" that were part of Nex's original training data. This is what researchers call an unintentional watermark: the merge preserved enough of Nex's behavioral patterns that the model's true provenance leaked through.

The Response

After the exposure, IplanRIO updated the Hugging Face model card to acknowledge the merge. Their explanation: they had "detected an incorrect upload in the previous version" and "accidentally uploaded the base merged version instead of the final distilled model." They apologized "profusely" and promised to re-upload the correct version.

The update now reads: "The model is built via a merge of Nex-N2-Pro and Qwen3.5-397B-A17B, proceeded by On-Policy Distillation from a stronger model."

Claude independently verified the weight merge claim, confirming the mathematical relationship across MoE router weights sampled from layers 0, 15, 30, 45, and 59.

Community Reaction

The r/LocalLLaMA community was split between outrage and dark humor. The Reddit thread title captured the mood: "Nex claims Rio 3.5 is Nex 2.5 PRO in trench coat." Users emphasized that model merging is standard practice in open-source AI, but presenting a merge as an original development crosses a line.

The Portuguese-language r/brasil thread (340 upvotes, 91 comments) was more pointed. Several commenters raised questions about public funds. If IplanRIO claimed to have developed a frontier model from scratch, what exactly did the municipal budget pay for? The model card mentions SwiReasoning, which is a published inference framework from a 2025 paper, not something IplanRIO invented.

Some users also noted the irony: Nex-N2-Pro itself is a fine-tuned model. The entire chain runs on Alibaba's Qwen weights. Everyone is building on everyone else's work. The difference is attribution.

So What

This is the third high-profile model attribution controversy in six months, and the pattern is clear. Model merging has become trivially easy with tools like mergekit. The technical barrier to creating a "new" model from existing weights is effectively zero. What remains difficult is the part IplanRIO skipped: actually training something.

The weight analysis technique Nex-AGI used is now well-known. Anyone with access to the model weights can run the same cosine similarity check. This means future attempts to pass off merges as original work will get caught faster. The open-source AI community has built its own immune system.

What bothers me more than the merge itself is the institutional context. This was a city government. Taxpayer money. The original model card claimed this was homegrown AI developed by a public entity. If a government agency lies about where its AI comes from, what happens when they use that same AI to make decisions about permits, services, or law enforcement?

The technical community caught this in hours. But most government procurement processes have no equivalent check. They take vendor claims at face value. Rio got caught because developers care about provenance. City residents buying into an "AI-powered government" narrative will not run cosine similarity tests on the weights.

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