As noted in Lumo's Privacy Documentation, the system currently routes queries to several open-weight models, including Qwen and Kimi K2. While utilizing these models supports cost-efficiency and open-source diversity, research (e.g., NDSS 2026, "S1761") indicates they contain significant internal censorship and narrative engineering aligned with authoritarian state interests.

Proton’s core mission is to champion digital rights, encryption, and freedom against state surveillance. However, deploying models with embedded propaganda or censorship mechanisms creates a contradiction:

• Cognitive Risk: Users may receive subtly altered information or suppressed viewpoints on sensitive geopolitical topics (e.g., HK, Taiwan, human rights).
• Value Misalignment: This conflicts with Proton's historical stance supporting activists and resisting authoritarian control.

Proton should implement a bias mitigation layer specifically for these models. This could involve:

• Post-Training Alignment: Applying techniques similar to those discussed in ACL 2023 ("Mitigating Bias in Large Language Models") to strip state-aligned censorship.
• Routing Logic: Automatically routing sensitive queries (political, human rights, censorship-related) to models with stronger neutrality guarantees (e.g., Apertus, Llama variants) or applying strict red-teaming filters.
• Transparency: Clearly labeling when a response originates from a model known to have censorship constraints.

Why This Matters: For a privacy-first platform serving a global audience, the integrity of the information provided is as critical as the encryption of the channel. Mitigating these biases ensures Lumo remains a tool for liberation rather than an unwitting vector for state narratives.