Create README.md

README.md

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+---
+language:
+- en
+pretty_name: CLIFT
+size_categories:
+- 5K<n<10K
+task_categories:
+- text-generation
+tags:
+- benchmark
+- evaluation
+- synthetic
+- reasoning
+- in-context-learning
+- transfer-learning
+- structured-output
+---
+
+<div align="center">
+
+# CLIFT
+
+**Contextual Learning across Inference, Format, and Transfer**
+
+A structured benchmark of **5,160** synthetic instances that stress-test whether models **learn latent rules from context**—then **apply** them under **format shifts**, **task families**, and **probe types** (forward, inverse, OOD, planning, and more).
+
+[![GitHub](https://img.shields.io/badge/Code-GitHub-181717?style=flat-square&logo=github)](https://github.com/LongarMD/CLIFT)
+[![Dataset](https://img.shields.io/badge/Records-5160-ffd21e?style=flat-square&logo=huggingface)](https://huggingface.co/datasets/longarmd/CLIFT)
+
+</div>
+
+---
+
+## At a glance
+
+| | |
+| :--- | :--- |
+| **Instances** | 5,160 |
+| **Design** | Full factorial over **task × format × application × difficulty**, with **10** i.i.d. draws per cell |
+| **Seed** | `42` (reproducible) |
+| **Language** | English prompts and instructions |
+| **Modality** | Text (completion-style `prompt` → string `target`) |
+
+### Load in Python
+
+```python
+from datasets import load_dataset
+
+ds = load_dataset("longarmd/CLIFT", split="train")
+# Each row: prompt, target, task, format, application, difficulty, latent_structure, ...
+```
+
+If the Hub loader is misconfigured, use the **Files** tab JSONL and stream with `json.loads` per line—the schema matches the table below.
+
+---
+
+## Why CLIFT?
+
+Modern LMs are often evaluated on fixed formats or single-shot skills. **CLIFT** targets a narrower but critical capability: **contextual learning**—inferring a **hidden structure** from the prompt (examples, traces, or specs), then answering under **controlled variation** along three axes:
+
+1. **Inference** — *What* must be learned (lookup rules, algorithms, spatial transforms, small dynamical systems, …).
+2. **Format** — *How* that knowledge is presented (demonstrations, natural language, execution traces, formal specs).
+3. **Transfer / application** — *How* the model must use it (forward prediction, inverse reasoning, articulation, OOD probes, planning, structural probes—**task-dependent** subsets).
+
+Together, these axes yield a **dense evaluation matrix** suited for diagnostics, ablations, and comparing training or prompting strategies—not a single leaderboard score in isolation.
+
+---
+
+## Task families
+
+Instances are grouped into **four families** spanning **10** canonical tasks:
+
+| Family | Tasks |
+| :--- | :--- |
+| **Functional mappings** | `lookup_table`, `arithmetic_rule`, `conditional_rule` |
+| **Algorithmic** | `insertion_sort`, `max_subarray`, `binary_search`, `naive_string_matcher` |
+| **Spatial** | `spatial_translation` |
+| **Dynamic structures** | `affine_dynamics_2d`, `register_machine_2d` |
+
+**Formats** (all tasks use this set where applicable): `demonstration`, `natural_language`, `trace`, `formal_spec`.
+
+**Difficulty**: integer levels **1**, **2**, **3** (structure complexity scales with level).
+
+**Application** varies by task (e.g. affine/register tasks use dedicated OOD-suffixed probes). The shipped matrix matches the open-source generator defaults in [`clift.common`](https://github.com/LongarMD/CLIFT/blob/main/src/clift/common.py).
+
+---
+
+## Dataset structure
+
+Data are distributed as **JSONL**: one JSON object per line, Hugging Face–friendly and line-diffable. A companion **`manifest.json`** (in the source repo) records generator kwargs, expected row count, and a **SHA-256** over the canonical payload for integrity checks.
+
+### Fields (per instance)
+
+| Field | Type | Description |
+| :--- | :--- | :--- |
+| `instance_id` | int | Stable index within the snapshot |
+| `task` | string | One of the 10 canonical task names |
+| `format` | string | Presentation format |
+| `application` | string | Probe / application axis |
+| `difficulty` | int | 1–3 |
+| `prompt` | string | Model input (completion-style) |
+| `target` | string | Reference answer (exact match is the primary check) |
+| `latent_structure` | object | **Gold structure** for analysis & tooling (not shown to the model) |
+| `instruct` | bool | Whether instruct/chat-style export was used |
+| `messages` | array (optional) | OpenAI-style chat turns, when enabled at export |
+| `metadata` | object (optional) | Extra fields when present |
+
+> **Note:** `latent_structure` is intentionally included for **research and scoring pipelines**. Treat it as **held-out supervision** for training—do not condition generation on it unless your experimental design explicitly allows it.