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README.md

@@ -34,68 +34,116 @@ train-eval-index:
 
 # Dataset Card for SYN-1B
 
-Repository: https://github.com/QuixiAI/AUM
-
 ## Dataset Summary
 
-SYN-1B is the AUM synthetic task corpus generated from the local `syn-1b-v1.3`
-specification. The Parquet files expose one decoded synthetic instance per row in
-the `text` column. The original packed `uint16` token shards, sidecars, and QA
-manifests remain in this directory for exact token-level training and audit use.
-
-The Hugging Face `validation` split corresponds to the SYN generator's `eval`
-split. F5 appears only in that held-out eval/validation split.
-
-## Purpose
-
-SYN-1B exists to test the central claim in AUM-Ø: a model should separate
-evidence from interpretation, then spend extra computation only when revising
-the interpretation is useful. AUM-Ø maintains an evidence state that records
-what has been observed and a compact hypothesis register that tracks how that
-evidence should currently be interpreted. The synthetic corpus provides
-programmatic tasks with known latent hypotheses so the training and evaluation
-harness can ask whether the register actually tracks the active rule, whether
-silent revision helps at sparse interpretive events, and whether pressure is
-allocated where counterfactual benefit predicts it will pay.
-
-As described in `AUM-Ø.md`, the broader pretraining recipe mixes filtered
-web/education text, code, and a 5% stream of synthetic structured tasks. That
-synthetic fraction is not filler: the sigma-decode probe, recency falsifier,
-flat-null control, and evidence-age measurements are computed on it. SYN-1B is
-the corpus-scale version of those tasks in the real 49,152-token SmolLM2 BPE
-space, as specified in `SYN-1B.md`.
-
-The task families are designed to isolate different parts of the mechanism:
+SYN-1B is a 1.04B-token synthetic language-modeling corpus of rule-governed
+text streams. Each row is a decoded instance in which the text establishes
+facts, mappings, bindings, or simple generative rules; later spans may revise
+those rules, swap bindings, delay a query over long filler, or present a null
+control with event-like surface text that should not change the answer.
+
+The dataset is intended as structured pretraining data, not as a supervised
+instruction dataset. It uses ordinary next-token prediction, but the examples
+are generated from known latent state machines. That makes it useful when a
+researcher wants training data where long-context behavior, variable binding,
+belief revision, recency, distractor robustness, and held-out transfer can be
+measured exactly rather than inferred from natural text.
+
+The [data/](data/) Parquet files expose one decoded synthetic instance per row
+in the `text` column. The original packed `uint16` token shards, sidecars, and
+QA manifests are kept under [raw/](raw/) for exact token-level training and
+audit use. The Hugging Face `validation` split corresponds to the SYN
+generator's `eval` split. F5 appears only in that held-out eval/validation
+split.
+
+## Why Pretrain With It?
+
+SYN-1B gives language models repeated practice on behaviors that are important
+in real use but sparse, ambiguous, or hard to label in web text:
+
+- Tracking a rule or binding across many intervening tokens.
+- Updating an answer after a correction instead of copying the most recent
+  local surface pattern.
+- Preserving unchanged facts when only part of a rule changes.
+- Distinguishing real state-changing events from plausible-looking distractors.
+- Answering from evidence that may be hundreds or thousands of tokens old.
+- Transferring the same abstract update pattern to a held-out surface format.
+
+For general pretraining, SYN-1B can be mixed as a small synthetic fraction
+alongside web, education, code, or domain corpora to add dense, auditable
+examples of state tracking and revision. It is especially relevant for work on
+long-context models, recurrent or memory-augmented models, retrieval-free
+reasoning, continual belief updating, and curricula that target systematic
+generalization.
+
+## Other Applications
+
+Beyond pretraining, SYN-1B can be used as:
+
+- A controlled benchmark for long-context recall and rule updating.
+- A source of probing data for whether hidden states encode active mappings,
+  bindings, recency, and corrected values.
+- A stress test for distractor sensitivity and surface-marker shortcuts.
+- A data generator baseline for synthetic-curriculum, data-mixture, and
+  scaling-law studies.
+- A reproducible corpus for mechanistic interpretability, because the raw
+  sidecars under [raw/sidecars/](raw/sidecars/) identify events, writes, query
+  positions, answer positions, evidence spans, composition depth, controlled gap
+  lengths, and held-out families.
+
+## Limitations
+
+SYN-1B is synthetic by design. It should not be treated as a substitute for
+natural-language pretraining data, an instruction-following dataset, or a
+factuality benchmark. The task families cover a controlled set of rule-updating
+and long-context patterns, so improvements on SYN-1B should be checked against
+naturalistic and out-of-distribution evaluations before making broad claims.
+
+The decoded Parquet view is convenient for Hugging Face tooling, while the
+packed token shards and sidecars in [raw/](raw/) are the reference artifacts for
+exact token-level audits and reproduction. Users who need full supervision
+metadata should read the sidecars rather than relying only on the compact
+Parquet columns.
+
+## Task Families
+
+The task families isolate different forms of state tracking:
 
 | Family | Role |
 | --- | --- |
 | F1 branch reversal | A rule holds, then an event changes the active mapping. |
 | F2 binding swap | Entity-to-attribute bindings are corrected by swaps rather than a single surface marker. |
 | F3 delayed correction / recall | Old evidence and controlled evidence age test whether stored evidence survives and can be reinterpreted. |
-| F4 flat null | No interpretive event occurs, so pressure and silent depth should stay near zero. |
-| F5 modular-stream switch | Eval-only transfer task with no training exposure, used to test whether probes generalize beyond memorized formats. |
+| F4 flat null | No interpretive event occurs; event-like distractors test false-positive rule updates. |
+| F5 modular-stream switch | Eval-only transfer task with no training exposure, used to test transfer beyond memorized surface formats. |
+
+## Source and Reproducibility
 
-This local Hugging Face view is a convenience format. The raw packed token
-stream and sidecars are the authoritative artifact for mechanism probes,
-token-level QA, and exact reproduction of the SYN-1B build.
+The generator, QA suite, and build specification are available in the
+[source repository](https://github.com/quixiai/aum). See
+[SYN-1B.md](https://github.com/quixiai/aum/blob/main/SYN-1B.md) for the corpus
+specification.
 
 ## How to Load
 
 ```python
 from datasets import load_dataset
 
-ds = load_dataset("/home/hotaisle/datasets/SYN-1B")
+ds = load_dataset("QuixiAI/SYN-1B")
 ```
 
 ## Dataset Stats
 
-| Split | Rows | Synthetic instance tokens |
+| Split | Rows | Synthetic instance tokens in raw build |
 | --- | ---: | ---: |
-| `train` | 4,046,772 | 1,000,002,097 |
-| `validation` | 178,463 | 40,002,369 |
-| `total` | 4,225,235 | 1,040,004,466 |
+| `train` | 4,063,978 | 1,000,001,463 |
+| `validation` | 178,863 | 40,002,882 |
+| `total` | 4,242,841 | 1,040,004,345 |
 
-Tokenizer: `HuggingFaceTB/SmolLM2-135M`
+The Hugging Face dataset is decoded text. Consumers can tokenize the `text`
+column with any tokenizer. The token counts above refer to the reference raw
+build in [raw/](raw/), which was generated and QA-audited with the
+`HuggingFaceTB/SmolLM2-135M` tokenizer.
 
 ## Data Schema
 
@@ -106,7 +154,7 @@ Columns:
 - `token_len`, `task_token_count`, `filler_token_count`, `controlled_gap_tokens`,
   `composition_depth`, `num_queries`: compact per-instance metadata.
 - `shard`, `window_index`, `start_offset`, `token_hash`: join keys back to the
-  packed raw token stream and sidecar records.
+  packed raw token stream and sidecar records under [raw/](raw/).
 
 ## Licensing Information
 

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generation.log

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-SYN tokenizer: name=HuggingFaceTB/SmolLM2-135M class=GPT2TokenizerFast transformers=4.57.1 vocab=49152 backend_hash=2225e8fb36485529
-Command terminated by signal 15
-	Command being timed: "python3 train/gen_SYN-1B.py --out-dir /home/hotaisle/datasets/SYN-1B --train-tokens 1000000000 --eval-tokens 40000000 --shard-size-tokens 100000000 --seq-len 4096 --seed 1337"
-	User time (seconds): 17346.21
-	System time (seconds): 278.19
-	Percent of CPU this job got: 100%
-	Elapsed (wall clock) time (h:mm:ss or m:ss): 4:53:39
-	Average shared text size (kbytes): 0
-	Average unshared data size (kbytes): 0
-	Average stack size (kbytes): 0
-	Average total size (kbytes): 0
-	Maximum resident set size (kbytes): 145311460
-	Average resident set size (kbytes): 0
-	Major (requiring I/O) page faults: 0
-	Minor (reclaiming a frame) page faults: 62126764
-	Voluntary context switches: 806
-	Involuntary context switches: 94033
-	Swaps: 0
-	File system inputs: 0
-	File system outputs: 39632624
-	Socket messages sent: 0
-	Socket messages received: 0
-	Signals delivered: 0
-	Page size (bytes): 4096
-	Exit status: 0

qa_optimized.log

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-SYN tokenizer: name=HuggingFaceTB/SmolLM2-135M class=GPT2TokenizerFast transformers=4.57.1 vocab=49152 backend_hash=2225e8fb36485529
-QA start tokenizer
-QA done tokenizer ok=True
-QA start manifest_tokenizer
-QA done manifest_tokenizer ok=True
-QA start packing_policy
-QA done packing_policy ok=True
-QA load_records train
-QA loaded_records train count=4046772
-QA start window_integrity_train
-QA done window_integrity_train ok=True
-QA start task_density_train
-QA done instance_task_density_train ok=True
-QA start offset_contract_train
-QA done offset_contract_train ok=True
-QA start instance_span_roundtrip_train
-QA done instance_span_roundtrip_train ok=True
-QA load_records eval
-QA loaded_records eval count=178463
-QA start window_integrity_eval
-QA done window_integrity_eval ok=True
-QA start task_density_eval
-QA done instance_task_density_eval ok=True
-QA start offset_contract_eval
-QA done offset_contract_eval ok=True
-QA start instance_span_roundtrip_eval
-QA done instance_span_roundtrip_eval ok=True
-QA start controlled_distribution
-QA done controlled_distribution ok=False
-QA start marker
-QA done marker ok=True
-QA start dedup
-QA done dedup ok=True
-QA start labels
-QA done labels ok=True
-QA start split_integrity
-QA done split_integrity ok=True
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-QA done schema_completeness ok=True
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-QA done schema_admission ok=True
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-QA done query_position_contract ok=True
-QA start distractor_surface
-QA done distractor_surface ok=True
-QA start f3_age
-QA done f3_age ok=True
-QA start filler_scaffolding
-QA done filler_scaffolding ok=True
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-QA done train_eval_symbol_exclusion ok=True
-QA start f1_rule_consistency
-QA done f1_rule_consistency ok=True
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-QA done minimal_suffix ok=True
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-QA done composition_depth_recompute ok=True
-QA start age_flag
-QA done age_flag_recompute ok=True
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-QA done content_decode ok=True
-QA start provenance
-QA done provenance_audit ok=True
-QA start difficulty_strata
-QA done difficulty_strata ok=True
-QA start metadata_recomputation
-QA done metadata_recomputation ok=False
-{
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-          "owners": [
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-}
-Command exited with non-zero status 1
-	Command being timed: "python3 train/gen_SYN-1B.py --qa-only /home/hotaisle/datasets/SYN-1B --seq-len 4096 --seed 1337"
-	User time (seconds): 33218.09
-	System time (seconds): 270.23
-	Percent of CPU this job got: 831%
-	Elapsed (wall clock) time (h:mm:ss or m:ss): 1:07:06
-	Average shared text size (kbytes): 0
-	Average unshared data size (kbytes): 0
-	Average stack size (kbytes): 0
-	Average total size (kbytes): 0
-	Maximum resident set size (kbytes): 160051776
-	Average resident set size (kbytes): 0
-	Major (requiring I/O) page faults: 0
-	Minor (reclaiming a frame) page faults: 46516997
-	Voluntary context switches: 2449273
-	Involuntary context switches: 440337
-	Swaps: 0
-	File system inputs: 0
-	File system outputs: 240
-	Socket messages sent: 0
-	Socket messages received: 0
-	Signals delivered: 0
-	Page size (bytes): 4096
-	Exit status: 1

MANIFEST.syn-1b-v1.3.json → raw/MANIFEST.syn-1b-v1.3.json

@@ -4,41 +4,41 @@
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@@ -440,11 +440,15 @@
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@@ -462,17 +466,17 @@
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@@ -533,11 +537,11 @@
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@@ -615,7 +619,7 @@
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@@ -866,8 +900,8 @@
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@@ -875,7 +909,7 @@
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@@ -890,14 +924,14 @@
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@@ -928,13 +962,13 @@
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@@ -954,7 +988,7 @@
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@@ -1011,14 +1045,14 @@
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@@ -1069,7 +1103,7 @@
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@@ -1093,18 +1127,18 @@
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@@ -1131,20 +1165,20 @@
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@@ -1152,7 +1186,7 @@
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@@ -1168,7 +1202,7 @@
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@@ -1279,7 +1313,7 @@
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@@ -1302,45 +1336,45 @@
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