Duplicate from Gugu8/LOOM

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LICENSE

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+Open Data Attribution Training Disclosure License (ODATL‑1.0)
+Version 1.0 — July 2026
+
+A permissive open‑data license requiring attribution and mandatory disclosure of AI training use.
+
+--------------------------------------------------------------------
+SECTION 1 — DEFINITIONS
+--------------------------------------------------------------------
+1.1 “Dataset” refers to the collection of data, files, metadata, annotations, structures, or any other materials distributed under this License, including any updates, subsets, or modified versions.
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README.md

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+---
+license: other
+language:
+- en
+tags:
+- LOOM
+- English
+- CoT
+- code
+- math
+---
+# LOOM: Language-Only Operational Microworlds
+
+**LOOM** is a synthetic natural-language reasoning dataset designed to teach language models the deep structures behind code and math without exposing source code, formal equations, or symbolic programming syntax.
+
+Instead of showing code or math notation, LOOM trains models on **ordinary-language microworlds** where the hidden logic is algorithmic: state changes, causal chains, conditionals, invariants, iteration, and reverse reasoning.
+
+The dataset is intended as a **pre-code / pre-math reasoning substrate**. It teaches the model to simulate rules, track hidden states, infer causes, and produce step-by-step logical traces.
+
+## What LOOM Teaches
+
+LOOM examples are designed to implicitly train the following skills:
+
+- **State tracking**: following how objects change across events.
+- **Causal chaining**: propagating effects through multiple rules.
+- **Conditionals**: reasoning through if/else-style branches.
+- **Invariants**: understanding conserved quantities or balanced properties.
+- **Iteration**: reasoning about repeated actions until completion.
+- **Reverse reasoning**: inferring causes from observed outcomes.
+- **Chain-of-thought traces**: producing intermediate reasoning before the final answer.
+
+## What LOOM Avoids
+
+The dataset intentionally avoids:
+
+- Source code
+- Programming syntax
+- Formal equations
+- Mathematical notation
+- Code-like operators
+- Formulaic symbolic reasoning
+
+The surface text is plain natural language. The computational and mathematical structure remains latent.
+
+## Dataset Structure
+
+Each example is a JSONL object with the following fields:
+
+- `instruction`: The task prompt.
+- `world`: The rules describing the microworld.
+- `event`: The triggering event or observed state.
+- `question`: What the model must infer.
+- `reasoning`: A step-by-step natural-language logical trace.
+- `final_answer`: The final outcome.
+
+## Example
+
+```json
+{
+  "instruction": "Determine the outcome of the event based on the world rules. Provide a step-by-step logical trace before the final answer.",
+  "world": "Whenever the silver mirror reflects, it triggers the dormant shadow. Whenever the dormant shadow awakens, it triggers the golden seed.",
+  "event": "The silver mirror reflects.",
+  "question": "What is the final consequence for the golden seed?",
+  "reasoning": "First, the silver mirror reflects. Because the silver mirror reflected, the dormant shadow is triggered. Because the dormant shadow awakened, the golden seed is triggered.",
+  "final_answer": "The golden seed is triggered."
+}
+```
+
+## Categories
+
+### 1. Causal Chains
+
+Teaches transitive reasoning and state propagation.
+
+Example pattern:
+
+> Whenever A acts, it triggers B.  
+> Whenever B acts, it triggers C.  
+> A acts.  
+> Therefore, C is triggered.
+
+### 2. Conditionals
+
+Teaches branching logic.
+
+Example pattern:
+
+> If A happens, B happens.  
+> If A does not happen, C happens.
+
+### 3. Invariants
+
+Teaches conservation-style reasoning.
+
+Example pattern:
+
+> The total balance between A and B is preserved.  
+> If A gains something, B must lose it.
+
+### 4. Iteration
+
+Teaches repeated actions, termination conditions, and cumulative effects.
+
+Example pattern:
+
+> The ritual repeats until the required number of actions has occurred.
+
+### 5. Reverse Logic
+
+Teaches abduction and backtracking.
+
+Example pattern:
+
+> The outcome happened.  
+> What must have caused it?
+
+## Loading the Dataset
+
+```python
+from datasets import load_dataset
+
+ds = load_dataset(
+    "YOUR_USERNAME/loom",
+    data_files={"train": "loom_dataset.jsonl"},
+    split="train",
+)
+
+print(ds[0])
+```
+
+Replace `YOUR_USERNAME` with your Hugging Face username.
+
+## Intended Use
+
+LOOM can be used for:
+
+- Reasoning-focused continued pretraining
+- Chain-of-thought style supervision
+- Synthetic reasoning warm-up before code or math training
+- Evaluation of rule-following and hidden-state tracking
+- Data augmentation for abstract reasoning tasks
+
+It is not intended to replace real code or math datasets. It is intended to teach the underlying operational reasoning that makes those domains easier to learn.
+
+## Generation
+
+This dataset was procedurally generated using combinatorial natural-language templates. Each example is constructed from randomized entities, actions, conditions, and causal relations.
+
+The generation process creates millions of unique examples while preserving logical consistency between:
+
+- the world rules,
+- the event,
+- the reasoning trace,
+- and the final answer.
+
+## Limitations
+
+LOOM is synthetic and stylized. Its language is intentionally simple and rule-based. It may contain repeated structures, artificial phrasing, and limited semantic diversity compared with natural web text.
+
+The dataset teaches abstract reasoning patterns, but it does not teach real programming syntax, libraries, APIs, or advanced mathematical formalism.
+
+## Citation
+
+```bibtex
+@misc{loom2026,
+  title={LOOM: Language-Only Operational Microworlds},
+  author={Gugu8},
+  year={2026},
+  howpublished={Hugging Face Datasets}
+}
+```

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