Release SynapDrive AI v1.0.0 governed neuroadaptive runtime - #1
Merged
Conversation
This file contains hidden or bidirectional Unicode text that may be interpreted or compiled differently than what appears below. To review, open the file in an editor that reveals hidden Unicode characters.
Learn more about bidirectional Unicode characters
Sign up for free
to join this conversation on GitHub.
Already have an account?
Sign in to comment
1 participant
Add this suggestion to a batch that can be applied as a single commit.This suggestion is invalid because no changes were made to the code.Suggestions cannot be applied while the pull request is closed.Suggestions cannot be applied while viewing a subset of changes.Only one suggestion per line can be applied in a batch.Add this suggestion to a batch that can be applied as a single commit.Applying suggestions on deleted lines is not supported.You must change the existing code in this line in order to create a valid suggestion.Outdated suggestions cannot be applied.This suggestion has been applied or marked resolved.Suggestions cannot be applied from pending reviews.Suggestions cannot be applied on multi-line comments.Suggestions cannot be applied while the pull request is queued to merge.Suggestion cannot be applied right now. Please check back later.
This release transforms SynapDrive AI into a governed neuroadaptive shared autonomy research runtime with uncertainty aware abstention, signal quality and drift detection, qualified decoder integration, multimodal fusion, shared autonomy, counterfactual planning, symbolic world modeling, Simplex style fallback control, ErrP based contradiction handling, evidence gated memory, guarded adaptation, deterministic fault injection, tamper evident evidence chains, signing, replay, benchmarking, and release validation.
Local Windows verification is green on Python 3.13.2: 138 tests passed, Ruff passed, Pyright reports 0 errors, and the v1 release validator passed all 24 checks. The release also includes reproducible validation evidence, SHA 256 manifest verification, updated safety and claim boundaries, and removal of legacy fabricated or placeholder control paths.
Synthetic verification metrics are explicitly labeled as synthetic and are not presented as human EEG, clinical, hardware safety, or physical BCI performance claims.