Evaluating open-ended generation remains non-trivial because quality varies across style, correctness, grounding, and safety. Moreover, a fixed prompt-response abstraction is insufficient for a reusable evaluation system as deployed applications increasingly expose multi-turn dialogues, retrieved evidence, and tool-calling traces. We present Pollux, a universal criteria-based evaluation framework centered on Pollux Judge, a compact Qwen3-4B-based LLM-as-a-Judge that evaluates such heterogeneous interactions through free-form user-defined criteria, scales, and output formats. Pollux follows the criteria methodology represented in the Pollux Benchmark (35 task groups, 2,115 expert-authored prompts), using it as a human-grounded resource for judge validation. The judge is trained with GRPO on Russian- and English-language data and is designed for bilingual deployment through a language-independent interaction schema. On the held-out expert-labeled test set, Pollux reaches a Spearman correlation of 0.744, exceeding every evaluated general-purpose baseline, including models with up to 1.1T parameters, while remaining suitable for local or OpenAI-compatible serving.

System demonstration video: https://drive.google.com/file/d/1PF6L-RcrS24JVUdaVRi01j237yTdftEd/