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README.md

@@ -23,20 +23,24 @@ tags:
 > **incomplete**. Please **do not download or benchmark** until this notice is removed.
 > Expected complete: within a few hours. Thanks for your patience.
 
-**LittleCurriculum** is an ≈88-billion-token English pretraining corpus distilled from
-[FineWeb-Edu](https://huggingface.co/datasets/HuggingFaceFW/fineweb-edu) through a five-stage
-filtering pipeline aligned with U.S. Common Core standards for grades **K–5**. Content, facts, and
-vocabulary characteristic of material taught **above Grade 5** are explicitly removed.
+**LittleCurriculum** is a **developmentally restricted** ≈88-billion-token English pretraining corpus:
+a **precision-first** subset of [FineWeb-Edu](https://huggingface.co/datasets/HuggingFaceFW/fineweb-edu)
+filtered to the U.S. **K–5** developmental level. A multi-stage pipeline enforces constraints on
+**knowledge, syntactic complexity, and reasoning** (with Common Core State Standards as the grade-level
+reference), removing material characteristic of the **Beyond-K–5** curriculum—advanced concepts such as
+algebra or higher-grade science—while retaining text a K–5 learner can process.
 
 It is the corpus behind the **LittleLearner** models — language models trained from scratch under a
 *pedagogically controlled knowledge boundary*, released alongside matched unfiltered controls. See the
 [LittleLearner model collection](https://huggingface.co/littlelearner).
 
-> **A note on scope.** LittleCurriculum is filtered for **K-5 reading level and linguistic
-> complexity** — not for subject matter or child-appropriateness. Simple-language web text on mature
-> or adult topics can appear; this is **not** a curated curriculum and **not** a child-safe corpus.
-> The filter constrains the *complexity* of the training distribution — the variable our experiments
-> control — not its subject matter. See **Scope of filtering** and **Limitations** below.
+> **A note on scope.** The pipeline constrains the **developmental level** of a text—the knowledge,
+> linguistic complexity, and reasoning it demands—not its subject matter. Advanced **Beyond-K–5 concepts**
+> (calculus, quantum physics, and the like) are filtered to near-zero, but subject matter itself is not
+> curated: everyday content on any topic a curious child might follow—aviation, current events, crime in
+> the news—can appear when written at a K–5 level. The design is deliberately *precision-first*, trading
+> recall for a sharp, interpretable boundary (validated on CommonCoreText and WeeBit; see paper §3.1).
+> See **Scope of filtering** and **Limitations** below.
 
 ## Dataset at a glance
 
@@ -86,27 +90,20 @@ distribution's coverage is explicitly specified. Suitable for pretraining langua
 interpretable knowledge boundary, continual-learning and boundary-probing studies, and machine-vs-child
 learning comparisons.
 
-## Scope of filtering — what the pipeline does and doesn't do
+## Scope of filtering: developmental level, not topic
 
-The five stages score **linguistic complexity and reading level** (word Age-of-Acquisition, frequency,
-grade-level classifiers, advanced-notation/vocabulary blocklists). They deliberately do **not** score
-**topic** or **appropriateness**. Consequences to be aware of:
-
-- **It is a complexity control, not a curriculum.** Documents pass because they are written at a K-5
-  *reading level*, not because their subject is part of a K-5 syllabus. Everyday web content — DIY and
-  hobby guides, product listings, local news — appears when its language is simple enough.
-- **It is not a child-safe or content-moderated corpus.** Mature or adult subject matter (crime, news,
-  commerce, etc.) can appear **when written in simple language**, because the filter measures *how* text
-  is written, not *what* it is about. Do not treat LittleCurriculum as safe-for-children content.
-- **Why this is the right control for our experiments.** The scientific variable under study is the
-  *complexity ceiling* of the pretraining distribution — what reasoning and knowledge the model can
-  acquire — which reading-level filtering targets directly. Topical curation is neither feasible at 88B
-  tokens nor what the experiments require.
+Consistent with the paper, the pipeline enforces constraints on **knowledge, syntactic complexity, and
+reasoning**—the *developmental level* a text demands—rather than curating subject matter. Two things
+follow: (i) advanced **Beyond-K–5 concepts** (e.g. calculus, quantum physics) are removed to near-zero;
+and (ii) subject matter is otherwise not restricted, so everyday content on any topic a curious child
+might follow—aviation, current events, crime in the news—can appear when written at a K–5 level. The
+filter is *precision-first*: tuned for near-zero Beyond-K–5 retention at the cost of discarding much
+in-scope material, yielding a sharp, interpretable boundary rather than an exhaustive sample of K–5 text.
 
 ## Limitations
 
-- **Reading-level filter, not topical/safety filter.** See **Scope of filtering** above ��� topic and
-  appropriateness are out of scope for the pipeline.
+- **Developmental-level filter, not topical curation.** See **Scope of filtering** above — the pipeline
+  removes Beyond-K–5 *concepts*, not general subject matter, which can span any domain at a K–5 level.
 - **Not zero-exposure.** The filter removes advanced academic *content and skills*, not every token. A
   measured spot-check found beyond-curriculum vocabulary present only at low, grade-appropriate
   frequency (e.g. "adrenaline" 0.075%, "hormone" 0.177%, "epinephrine" 0.006%, "ballistic missile"