Docs
Capability Map
What exists today, what is thin evidence, and what remains future work.
ZeroModel is being positioned as the artifact layer for Visual AI Computing. The current implementation supports a concrete alpha capability surface.
| Capability | What it means | Claim posture |
|---|---|---|
| Artifact kernel | Deterministic VPM identity, cell/source mapping, layout recipes, provenance | Validated core |
| Dense policy views | Multiple named policy views over the same scored table | Validated mechanism |
| Spatial optimizer | Metric-weight view profile for explicit top-left mass objective | Validated for the objective |
| Decision manifold | Temporal geometry over consistent scored panel sequences | Validated for deterministic summaries |
| PHOS / top-left gates | Lightweight no-model-at-decision-time threshold consumers | Validated for simple gates |
| Bundles | .vpm zip bundles with manifest round trip |
Validated |
| Rendering | Dependency-light PNG/SVG fields | Implemented |
| Learning traces | Before/after/held-out/regression evidence of learning | Validated for scored traces |
| Training progress | Checkpoint telemetry artifacts with best-checkpoint evidence | Validated for checkpoint telemetry |
| Tracker adapters | Dependency-light parsing of exported tracker files | Validated for exported files |
| Critic evidence | Critic, verifier, RAG, and policy scores as risk-first artifacts | Validated for scored traces |
| Agent trajectory VPMs | Step/tool/evidence/memory/claim inspection maps | Next public example |
Strong marketing line
ZeroModel is building toward the future of Visual AI Computing: a world where AI systems produce inspectable visual artifacts, not just text outputs and scalar scores.
Current honest line
ZeroModel turns scored data into deterministic, inspectable Visual Policy Map artifacts.
Both lines can appear on the site, but they play different roles. The first is the ambition. The second is the validated claim.