Fix dataset card tables and capability coverage

Replace wide and headerless tables with responsive two- and three-column layouts; add compact domain and model-capability coverage.

README.md

@@ -46,11 +46,11 @@ Train models to **retain**, **edit**, **address**, and **transform** state acros
 
 ## Choose a configuration
 
-| Configuration | Best for | Scale | Representation |
-|:--|:--|:--|:--|
-| **default** | Broad behavioral coverage and varied surfaces | 10,000,000 programs · 7.80 GiB | 36 families across M1–M11 |
-| **dense-100m** | High-throughput state-intensive training | 100M episodes · 94.78 GiB | 4 episodes/row · compact shared VM |
-| **dense-max-100m** | Maximum semantic and surface diversity | 100M episodes · 93.45 GiB | 384 families · 32 renderers · 64 transformations |
+| Configuration | Scale | Use when |
+|:--|:--|:--|
+| **default** | 10,000,000 programs · 7.80 GiB | You need 36 explicit families and varied surfaces |
+| **dense-100m** | 100M episodes · 94.78 GiB | You need high-throughput training on a compact shared VM |
+| **dense-max-100m** | 100M episodes · 93.45 GiB | You need 384 families, 32 renderers, and 64 transformations |
 
 Both configurations contain structured prompt, completion, and teacher-only privileged context. Every target is computed by an executable reference machine, never authored by a teacher model.
 
@@ -68,10 +68,11 @@ Both configurations contain structured prompt, completion, and teacher-only priv
         "aabbdev/StateBench-v1", "dense-max-100m", split="train", streaming=True
     )
 
-| Split | default | dense-100m | dense-max-100m |
-|:--|--:|--:|--:|
-| Train | 9,949,600 rows | 24,875,000 / 99.5M episodes | 24,875,000 / 99.5M episodes |
-| Validation | 50,400 rows | 125,000 / 0.5M episodes | 125,000 / 0.5M episodes |
+| Configuration | Train | Validation |
+|:--|--:|--:|
+| default | 9,949,600 rows | 50,400 rows |
+| dense-100m | 24,875,000 rows | 125,000 rows |
+| dense-max-100m | 24,875,000 rows | 125,000 rows |
 
 ## Train three ways
 
@@ -100,19 +101,19 @@ The default configuration includes a ready-to-use text column. dense-100m avoids
 
 ### 16 primitive domains
 
-| | | | |
-|:--|:--|:--|:--|
-| Retention & lifecycle | Capacity & eviction | Addressing & aliasing | Editing & transactions |
-| Selectivity & access | Interference & concurrency | Automata & control | Algebra & reversibility |
-| Data structures | Graphs & spatial state | Temporal streaming | Online adaptation |
-| Distributed replication | Agents & messaging | Integrity & error control | Runtime state |
+| Area | Domains |
+|:--|:--|
+| Memory | Retention & lifecycle · Capacity & eviction · Addressing & aliasing · Editing & transactions |
+| Control | Selectivity & access · Interference & concurrency · Automata & control · Algebra & reversibility |
+| Structures | Data structures · Graphs & spatial state · Temporal streaming · Online adaptation |
+| Systems | Distributed replication · Agents & messaging · Integrity & error control · Runtime state |
 
 ### 8 composition domains
 
-| | | | |
-|:--|:--|:--|:--|
-| Transactional + temporal | Adaptive + control | Concurrent + distributed | Graph + structures |
-| Robust streaming | Multi-agent state | Algebraic automata | Memory pressure |
+| Area | Compositions |
+|:--|:--|
+| State systems | Transactional + temporal · Adaptive + control · Concurrent + distributed · Graph + structures |
+| Stress systems | Robust streaming · Multi-agent state · Algebraic automata · Memory pressure |
 
 default implements 36 explicit behavioral families. dense-100m provides a compact shared register VM. dense-max-100m expands the typed VM to 40 opcodes, 256 primitive families, 128 compositions, 32 structural renderers, and 64 semantic-preserving transformations.
 
@@ -133,22 +134,22 @@ StateBench trains state handling, not factual knowledge. It does not by itself e
 
 ## Data contract
 
-| Field | default | dense configs | Purpose |
-|:--|:--:|:--:|:--|
-| prompt | yes | yes | Student-visible conversational input |
-| completion | yes | yes | Verified SFT target |
-| privileged_context | yes | yes | Teacher-only SDFT context |
-| text | yes | derived | Causal pretraining view |
-| semantic identity | one fingerprint | four fingerprints | Deduplication and audit |
-| family / macro / level | scalar | four-item lists | Curriculum filtering |
-| density_units_per_1k_chars | no | yes | Semantic density control |
+| Field | Availability | Purpose |
+|:--|:--|:--|
+| prompt | all configs | Student-visible conversational input |
+| completion | all configs | Verified SFT target |
+| privileged_context | all configs | Teacher-only SDFT context |
+| text | default; derived for dense | Causal pretraining view |
+| semantic identity | 1 default; 4 dense | Deduplication and audit |
+| family / macro / level | scalar default; lists dense | Curriculum filtering |
+| density_units_per_1k_chars | dense configs | Semantic density control |
 
 ## Dense configurations at a glance
 
-| Episodes | Physical rows | Density | Token estimate | Generator |
-|--:|--:|--:|--:|:--|
-| 100,000,000 | 25,000,000 | 162.01 units / 1k chars | 107–120B tokens | dense-100m |
-| 100,000,000 | 25,000,000 | 162.88 units / 1k chars | ~107–120B tokens | dense-max-100m |
+| Configuration | Density | Token estimate |
+|:--|--:|--:|
+| dense-100m | 162.01 units / 1k chars | 107–120B tokens |
+| dense-max-100m | 162.88 units / 1k chars | ~107–120B tokens |
 
 The token range was measured on 1,000 real packed rows with GPT-2 and Qwen3 tokenizers. Actual training cost depends on chat templates, truncation, packing, and the SDFT generation policy.