Hi OpenAI Developer Community,
OpenAI’s recently published framework for reporting model misalignment includes examples involving unsanctioned actions, cross-agent communication, and attempts to overcome operational restrictions. This raises a systems question I have been working on independently:
Can an autonomous agent remain highly adaptive while being structurally unable to convert its own learned state into execution authority?
I developed LOGOS-1, a falsifiable research and engineering framework built around that separation.
Its central boundary is:
Capability != Authority
AdaptiveState != Authority
Proposal != Execution
In LOGOS-1, the language model remains the cognitive component: it can reason, retrieve memory, learn procedures, evaluate evidence, and propose actions.
But execution authority is deliberately kept outside adaptive model state.
A separate Γ (Gamma) governance boundary evaluates proposed external actions against explicit authority, policy, provenance, evidence, and execution state before any effect is allowed.
This also produces several related invariants:
RememberedContent != ExecutionAuthority
MemoryTruth != MemoryAuthority
ImaginedTransition != ObservedTransition
OUTCOME_UNKNOWN != NOT_EXECUTED
The goal is not to replace alignment training or monitoring, but to add a non-adaptive runtime authority boundary underneath increasingly capable agents.
The project is being developed as an empirical research program rather than as a safety claim. Current work includes deterministic intervention tooling for:
- token-context memory,
- external BM25 retrieval,
- recurrent Mamba state capture/reset/swap,
- provenance-aware persistent memory,
- and causal state interventions.
The current R3 implementation has deterministic adapter tests in place; R4 is freezing tokenizer and dataset artifacts before any model-level causal evaluation. Synthetic controlled evidence is capped at EM1, with realistic public benchmarks required before stronger promotion.
One question I would especially value feedback on:
Does separating learned cognition from non-learned execution authority seem like a useful systems-level complement to current alignment, monitoring, and agentic oversight approaches?
Repository: WestMoneyDE/LOGOS-1
The repository is available for research/evaluation under BSL 1.1; commercial production use requires a separate license.
I would be very interested in technical criticism, especially around the Γ boundary, authority provenance, persistent-memory failure modes, and causal evaluation methodology.
— Ömer Hüseyin Coskun
GenAI Engineer & Systems Architect
Germany
Linkedin: oemer-coskun53