Rio de Janeiro Releases AI Model, Faces Ownership Claim

Rio de Janeiro's municipal IT company IplanRIO released Rio-3.5-Open-397B on Hugging Face under an MIT license, claiming it as a government-developed 397-billion-parameter Mixture-of-Experts model. Nex-AGI published weight-level evidence alleging the model is a direct parameter merge of approximately 60% Nex-N2-Pro and 40% Qwen 3.5, and that it self-identifies as 'Nex, from Nex-AGI' in 79% of responses without the custom system prompt. No formal rebuttal from IplanRIO has been reported, raising concerns about model provenance and accountability in government AI claims.

Rio de Janeiro Releases AI Model, Faces Ownership Claim IplanRIO, Rio de Janeiro's municipal IT company, released Rio-3.5-Open-397B on Hugging Face under an MIT license, a 397-billion-parameter Mixture-of-Experts model with first-party benchmark claims against DeepSeek and Alibaba's Qwen. Nex-AGI, the company behind the open-source Nex-N2-Pro model, then published weight-level evidence alleging the Rio model is a direct parameter merge - approximately 60% Nex-N2-Pro plus 40% Qwen 3.5 across all 60 weight tensors, with no anomalies. Nex-AGI also reported the model self-identifies as 'Nex, from Nex-AGI' in 79% of responses when its custom system prompt is removed. No formal rebuttal from IplanRIO has been reported. What happened IplanRIO, Rio de Janeiro's municipal IT company, published Rio-3.5-Open-397B on Hugging Face under an MIT license, presenting it as a government-developed 397-billion-parameter Mixture-of-Experts model. The model card credited IplanRIO with an approach called SwiReasoning, described as switching dynamically between chain-of-thought and latent-space reasoning using entropy-based signals. Benchmark claims showed competitive performance against DeepSeek and Alibaba's Qwen 3.7 Plus, including scores on Terminal-Bench and SWE-Bench Multilingual. Technical evidence Nex-AGI reported Nex-AGI, the company behind the open-source Nex-N2-Pro model, published an analysis alleging that Rio-3.5-Open-397B is not an original post-training run but a direct weight merge. Per Decrypt and SquaredTech reporting, Nex-AGI found every weight tensor in the model matches a blend of approximately 60% Nex-N2-Pro and 40% Alibaba's Qwen 3.5 across all 60 layers, with no anomalies. Nex-AGI also reported that when the custom system prompt supplied by IplanRIO is removed, the model self-identifies as "Nex, from Nex-AGI" in 79% of responses. Weight merging is a recognized technique for combining trained models via linear interpolation of parameter tensors; it requires no compute-intensive retraining, and attribution obligations depend on the terms of the source models. Context for practitioners The episode illustrates a verifiable detection gap: benchmark-level performance can be staged using merged weights without disclosing source provenance. Independent verification - weight-level comparison against candidate source models, model card audits, and licensing reviews - is the primary due-diligence tool for evaluating third-party or government-claimed model releases. That the release came from a public institution rather than a private vendor adds accountability stakes: government AI claims may influence procurement, policy, and public trust in ways that vendor announcements do not. What to watch No formal response from IplanRIO has been reported. Signals to monitor: - •whether IplanRIO publishes training logs or weight provenance documentation rebutting the Nex-AGI analysis - •licensing implications if Nex-N2-Pro's or Qwen's terms govern redistribution and modification of merged weights under the MIT license IplanRIO applied - •whether the incident accelerates calls for model provenance standards in government AI procurement Scoring Rationale A notable AI provenance controversy with technically specific, verifiable evidence: weight-level analysis from Nex-AGI and behavioral self-identification together make this more than a routine attribution dispute. Relevant to practitioners evaluating open model releases from non-traditional sources. Regionally scoped a city agency release rather than a major lab or widely-deployed model, capping significance below industry-wide events. Practice with real Telecom & ISP data 90 SQL & Python problems · 15 industry datasets Active Residential CustomersEasy /problems/sql/active-residential-customers Unlimited Fiber Plans 500Mbps+Medium /problems/sql/unlimited-fiber-plans-above-500mbps Customer Churn Risk AssessmentHard /problems/sql/customer-churn-risk-assessment 250 free problems · No credit card See all Telecom & ISP problems /problems/datasets/telecom