Document consolidated corrected dense-100m

README.md

@@ -26,25 +26,13 @@ configs:
     path: "data/dense-100m/train/*"
   - split: validation
     path: "data/dense-100m/validation/*"
-- config_name: dense-max-100m
-  data_files:
-  - split: train
-    path: "data/dense-max-100m/train/*"
-  - split: validation
-    path: "data/dense-max-100m/validation/*"
-- config_name: grounded-max-100m
-  data_files:
-  - split: train
-    path: "data/grounded-max-100m/train/*"
-  - split: validation
-    path: "data/grounded-max-100m/validation/*"
 ---
 
 <div align="center">
 
 # StateBench v1
 
-### 310 million verified state episodes for pretraining, SFT, and on-policy SDFT
+### 110 million verified state episodes for pretraining, SFT, and on-policy SDFT
 
 Train models to **retain**, **edit**, **address**, and **transform** state across long execution traces.
 
@@ -55,9 +43,7 @@ Train models to **retain**, **edit**, **address**, and **transform** state acros
 | 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 maximum-density stress training on the compact register VM |
-| **grounded-max-100m** | 100M episodes · 96.57 GiB | You want the dense-max oracles in domain-grounded, structurally varied prompts |
+| **dense-100m** | 100M episodes · 96.60 GiB | You need high-throughput state training with grounded, structurally varied prompts |
 
 All 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.
 
@@ -71,19 +57,11 @@ All configurations contain structured prompt, completion, and teacher-only privi
     dense = load_dataset(
         "aabbdev/StateBench-v1", "dense-100m", split="train", streaming=True
     )
-    dense_max = load_dataset(
-        "aabbdev/StateBench-v1", "dense-max-100m", split="train", streaming=True
-    )
-    grounded = load_dataset(
-        "aabbdev/StateBench-v1", "grounded-max-100m", split="train", streaming=True
-    )
 
 | 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 |
-| grounded-max-100m | 24,875,000 rows | 125,000 rows |
 
 ## Train three ways
 
@@ -126,22 +104,22 @@ The default configuration includes a ready-to-use text column. Dense configurati
 | 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 and dense-max-100m are compact register-VM curricula. dense-max-100m balances 384 curriculum labels and a pairwise schedule of 32 renderer IDs and 64 transformation IDs; those IDs describe coverage scheduling, while its published prompt surface remains intentionally VM-centric.
+default implements 36 explicit behavioral families. The corrected dense-100m configuration rematerializes 100M verified trajectories through 24 domain dialects: the 16 primitive domains and 8 compositions above. Its 32 document structures and 64 metamorphic surface passes are physically present in the prompts, including data formats, code-like records, logs, traces, conversations, and nested documents.
 
-grounded-max-100m rematerializes the same verified dense-max trajectories through 24 domain dialects: the 16 primitive domains and 8 compositions above. Its 32 document structures and 64 metamorphic surface passes are physically present in the prompts, including data formats, code-like records, logs, traces, conversations, and nested documents. This configuration targets surface transfer while preserving the exact audited answers; it remains synthetic state training, not a factual natural-language corpus.
+The dense semantic kernel trains long-horizon set, remove, additive, multiplicative, observation, and no-op transitions. Domain dialects diversify how those transitions are presented; they do not turn each label into a full simulator of that real-world system. StateBench remains synthetic state training, not a factual natural-language corpus.
 
 ## Expected model capabilities
 
 | Capability | Training pressure |
 |:--|:--|
 | Working-memory utilization | Retain independent values across long active traces |
-| Precise state mutation | Write, overwrite, delete, patch, rollback, and restore without collateral damage |
+| Precise state mutation | Write, overwrite, delete, add, and scale without collateral damage |
 | Associative addressability | Resolve exact, hierarchical, indirect, and collision-prone keys |
-| Algorithmic recurrence | Execute counters, automata, permutations, graphs, and bounded data structures |
-| Online adaptation | Bind and remap rules inside context without updating model weights |
+| Algorithmic recurrence | Execute compact transition sequences over long horizons |
+| In-context rule use | Apply a supplied transition legend without updating model weights |
 | Interference control | Protect persistent state from noise, churn, concurrent writes, and repeated reads |
-| Transactional and distributed reasoning | Track snapshots, messages, replicas, clocks, and conflict resolution |
-| Streaming stability | Preserve behavior across checkpoints, chunks, retries, and long horizons |
+| Surface robustness | Preserve semantics across structured documents, logs, code-like records, and conversations |
+| Streaming stability | Preserve behavior across long traces and packed independent episodes |
 
 StateBench trains state handling, not factual knowledge. It does not by itself expand a model's physical context window, add world knowledge, or guarantee general reasoning improvements outside state-intensive tasks.
 
@@ -161,18 +139,16 @@ StateBench trains state handling, not factual knowledge. It does not by itself e
 
 | 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 |
-| grounded-max-100m | 160.61 units / 1k chars | ~107–120B tokens |
+| dense-100m | 160.09 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.
 
 ## Integrity
 
-- 310M semantic identities are unique within their respective configurations.
+- 110M semantic identities are unique within their respective configurations.
 - Required fields, roles, answer JSON, and SDFT isolation passed full-corpus audits.
 - No benchmark name, family label, or privileged answer appears in model-visible prompts.
 - SHA-256 checksums cover every Parquet shard.
 - The public training corpus is separate from private StateBench evaluation material.
 
-Release manifests: dataset_manifest.json, dense-100m-manifest.json, dense-max-100m-manifest.json, and grounded-max-100m-manifest.json.
+Release manifests: dataset_manifest.json and dense-100m-manifest.json.