Gemma-SEA-LION-Guard-v2 Adds Action Governance for Regional Agents
Safeguarding Regional Agents: Announcing Gemma-SEA-LION-Guard-v2 (31B, E4B, E2B)
Guardrails tuned for Southeast Asian languages and regional contexts are scarce, and this release gives teams building agents a policy-enforcement layer beyond simple prompt filtering. Developers deploying agents in ASEAN markets can choose among three sizes to balance latency and coverage, or define custom policies instead of relying on generic moderation APIs. It contrasts with one-size-fits-all moderation models trained mostly on English and Western norms.
Singapore's SEA-LION project announced Gemma-SEA-LION-Guard-v2, a safety guardrail model family released in three sizes: 31B, E4B and E2B. The models target autonomous agents operating in multi-turn environments, where traditional single-turn moderation classifiers often fail. New capabilities include action governance, multi-turn evaluation and custom policy enforcement. The release is built on Gemma4 models and framed around Jev-style prediction of rising safety demands.
Safeguarding Regional Agents: Announcing Gemma-SEA-LION-Guard-v2 (31B, E4B, E2B)
Advancing AI Safety with Action Governance, Multi-Turn Evaluation, and Custom Policy Enforcement – Jev-style Prediction, Powered by Gemma4 Models As AI agents transition from simple conversational chatbots into autonomous tools operating in complex, multi-turn environments, safety guardrails must evolve alongside them. Traditional moderation models—designed primarily for single-turn safety classification—frequently fail when confronted with subtle regional...
