96
Sequential hourly transitions
Maximum support-checked acute-care horizon.
Evidence
Evidence for clinical AI translation and hardening.
Current evidence supports checked synthetic clinical worlds and bounded diagnostic workflows. It does not yet establish a completed closed-loop hardening result or broad real-world model improvement.
Current strongest result
96 checked sequential hourly transitions.
Synset has internally validated acute-care trajectory generation across four days of hourly clinical evolution. This supports generated-artifact checking, not a completed hardening claim.
Evidence snapshot: June 16, 2026 · Public methodology note v1.0
36,917
Generated candidates
Across the retained 64-hour and 96-hour experiment arms.
14,143
Accepted candidates
Accepted across those retained experiment arms.
22,774
Rejected candidates
Rejected before inclusion in the accepted set.
Caveat: This is an internal support-checking result. The funnel combines retained 64-hour and 96-hour experiment arms. It is not held-out future patient accuracy, clinical truth, regulatory readiness, or synthetic-control-arm validity, and it does not show that targeted synthetic data improved a model.
Release certificates
Why generated material was accepted.
A release certificate records why generated material was accepted for a declared use. File integrity alone does not establish clinical validity.
Release record
Release Certificate
- Clinical fitPASS
- Population supportPASS
- Timeline consistencyPASS
- Metadata safetyPASS
- Text groundingPASS
Clinical fit
Values and transitions satisfy stated clinical constraints.
Population support
Patterns remain consistent with empirical cohort-level structure.
Timeline consistency
Patient state changes remain plausible across time.
Metadata safety
No source IDs, internal linkage, or forbidden identifiers.
Text grounding
Notes, dialogue, and claims-style outputs stay tied to structured evidence.
Evidence tiers
Artifact checks, model diagnosis, and hardening proof are separate.
Certificates govern generated material. Held-out real data determines whether an intervention met its prespecified evidence standard.
Tier 1
Generated-world release evidence
Records whether synthetic clinical worlds passed the clinical, timing, population-support, metadata, and grounding checks for their stated use.
Supported now for bounded generated artifacts. A release certificate does not establish downstream model improvement.
Tier 2
Bounded diagnostic evidence
Maps failure surfaces with declared scenarios, model-facing evidence, rubrics, response scoring, and explicit use limits.
The diagnostic workflow is implemented for bounded assessments. A public outcome case has not yet been published.
Tier 3
Closed-loop hardening evidence
Compares frozen original and updated models on held-out real data using prespecified success and no-regression criteria.
Not yet complete as a public proof asset. Broad model-improvement claims remain out of scope until this evidence and external replication exist.
Technical evidence package
What the record behind an engagement contains.
The record keeps the model use, failure hypothesis, generated material, intervention, evaluation protocol, results, and claim boundary distinct.
Private model artifacts, patient-level evaluation data, raw responses, reviewer notes, internal run logs, hidden rubrics, and source-derived details are not public by default.
- declared model use and evaluation scope
- failure-surface and correctability summary
- synthetic-world release-check summary
- original and updated model references
- frozen intervention and evaluation versions
- held-out evaluation reference, if applicable
- success and no-regression summary
- public/private evidence classification
- remaining-risk and claim-boundary statement
Failure discovery
Find the blind spot before prescribing the fix.
Checked clinical worlds can expose gaps that ordinary retrospective validation misses. A useful result may be an improved candidate, a requirement for new real labels, a narrower intended use, or a defensible decision not to proceed.
- missingness
- delayed signal
- care-pathway variation
- subgroup presentation
- documentation drift
- longitudinal drift
- counterfactual inconsistency
- unsafe reassurance
- failure to ask for missing context
- contraindication misses
Hardening evidence standard
Held-out real data tests whether the intervention worked.
Held-out real data is a customer-controlled real evaluation set that is not used to select, tune, or choose the hardening intervention. It determines whether the updated model met the prespecified success and no-regression criteria within the declared evaluation scope.
- holdout designated before intervention selection
- prespecified success criteria
- prespecified no-regression checks
- frozen intervention before final evaluation
- original and updated model versions
- calibration and subgroup review
- no iterative optimization against final holdout results
- remaining-risk and stop-signal report
What Synset is proving next
The next proof is a closed loop.
Synset must identify one clinically meaningful weakness, generate targeted hardening material, train or support an updated model, and demonstrate a prespecified improvement on held-out real data without unacceptable regression.
Next gate
one supported clinical model and declared use
Next gate
frozen original and updated model versions
Next gate
customer-controlled held-out real evaluation data
Next gate
prespecified success and no-regression criteria
Next gate
external replication and a public-safe report
Research and roadmap directions
These are roadmap or partner-driven directions, not current validated claims unless a specific evidence artifact says otherwise.
- broader autonomous remediation across model classes
- patient-conditioned assurance, not patient-specific prediction
- life-sciences and real-world-evidence exploration
- imaging, genetic, waveform, and voice modalities
- trial-feasibility exploration, not synthetic control arms
Start with a readiness assessment.
Start with one supported model, one declared use, one suspected weakness, and whether a held-out evaluation set exists and where it can be evaluated. Do not send patient-level data by email.