Document: 07 of 10
Status: Public Summary (L2/L3)
Source reference: Capability Map 2026-06-02
Logos is an early symbolic-control bridge integrated with Sonata’s neural stack. It operates as a separate symbolic environment that can participate in training and evolution experiments through:
It is not a mature reasoning platform. It is an experimental bridge between learned representations and symbolic constraints, implemented at a prototype level.
In reflection-mode tests, Logos successfully detected contradictions in generated outputs. When the neural module produced results that violated symbolic constraints, Logos flagged the contradiction and provided trace information.
Logos can contribute to training by computing penalty terms based on symbolic rule violations. In validated test scenarios, these penalties influenced the loss function and guided optimization away from outputs that violated defined axioms.
Structural evolution experiments completed successfully with Logos-based guard enabled. The guard function validated candidate mutations against symbolic constraints before accepting them into the evolving population. This demonstrates a working loop between the neural evolution system and a symbolic validation layer.