somnia-agents-llm-inference

Deterministic AI outputs for smart contracts are achieved by invoking an on-chain LLM with a fixed seed and temperature=0. This ensures validators compute consistent, repeatable classification and moderation decisions across decentralized applications.

Yes, smart contracts can use multi-turn chat and tool calling for on-chain AI moderation. The interface supports string, number, and multi-turn inference methods, enabling complex structured decision-making in decentralized applications.

The best way to classify content on-chain with guaranteed consistency is using the inferString function. You provide a prompt and specify allowed values, ensuring the deterministic LLM returns predictable classification results across all validators.

Inconsistent moderation decisions in smart contracts occur when LLM temperature settings vary. Using a fixed-seed, temperature=0 on-chain LLM inference interface ensures all validators process governance tasks identically, eliminating output discrepancies.

Yes, on-chain LLM inference supports constrained outputs for decentralized applications. By specifying allowed values during invocation, the deterministic model returns structured results strictly matching predefined constraints for reliable automated decision-making.