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README.md

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+---
+license: mit
+tags:
+  - mechanistic-interpretability
+  - nla
+  - gsm8k
+  - qwen
+  - thought-anchors
+  - sentence-embeddings
+---
+
+# nla-thought-anchors-rollouts
+
+Sentence-level NLA rollout dataset for Qwen2.5-7B-Instruct on GSM8K test set.
+
+For each activation vector (layer 20, 1319 examples), **40 NLA descriptions** were sampled from the actor `kitft/nla-qwen2.5-7b-L20-av` (temperature=0.7). Each description was split into sentences and scored with the NLA critic `kitft/nla-qwen2.5-7b-L20-ar`.
+
+Part of the [nla-thought-anchors](https://github.com/Realmbird/nla-thought-anchors) project.
+
+## Files
+
+| File | Extraction point | Rows |
+|---|---|---|
+| `hash.parquet` | `####` token (model finished CoT, about to write answer) | 175,240 |
+| `answer.parquet` | Answer digit token (answer now in context) | ~175,000 |
+
+## Schema
+
+| Column | Type | Description |
+|---|---|---|
+| `example_idx` | int32 | Index in GSM8K test split |
+| `is_correct` | bool | Whether model answer matched gold |
+| `gold_answer` | string | Ground-truth answer |
+| `rollout_idx` | int32 | 0–39 — which of the 40 sampled descriptions |
+| `sentence_idx` | int32 | Sentence position within this rollout |
+| `sentence_text` | string | The sentence text |
+| `sentence_embedding` | list\<float32\> len=384 | MiniLM-L6-v2 embedding |
+| `cos_sim_to_full_nla` | float32 | Cos-sim of sentence to its own rollout's full description |
+| `critic_cos_sim` | float32 | NLA critic reconstruction cos-sim vs original activation |
+| `critic_mse` | float32 | NLA critic reconstruction MSE vs original activation |
+| `contains_gold_answer` | bool | Sentence contains the gold answer number |
+| `contains_pred_answer` | bool | Sentence contains the predicted answer number |
+
+## Key findings
+
+**Reconstruction quality by correctness** (hash position):
+
+| Group | Mean critic cos-sim |
+|---|---|
+| Correct examples | 0.604 |
+| Incorrect examples | 0.416 |
+
+Incorrect examples reconstruct ~35% worse — the activation at `####` is qualitatively different when the model gets the answer wrong.
+
+**Thought anchor analysis**: sentences that are stable across all 40 rollouts (high cos-sim to cross-rollout centroid) reconstruct significantly better than peripheral sentences:
+
+| Group | Correct | Incorrect |
+|---|---|---|
+| Peripheral sentences (Q1 anchor score) | 0.503 | 0.366 |
+| Anchor sentences (Q4 anchor score) | 0.661 | 0.673 |
+
+The most striking result: **anchor sentences from incorrect examples (0.673) reconstruct better than peripheral sentences from correct examples (0.503)** — the stable recurring content is what carries the activation signal regardless of correctness.
+
+**Anchor score** is not stored in this dataset — compute it post-hoc from `sentence_embedding` by grouping rows by `example_idx`, computing the centroid of all embeddings, and taking cos-sim of each sentence to the centroid.
+
+## Usage
+
+```python
+from datasets import load_dataset
+import numpy as np
+from sklearn.metrics.pairwise import cosine_similarity
+
+ds = load_dataset("Realmbird/nla-thought-anchors-rollouts", split="hash")
+
+# compute anchor scores for each example
+import pandas as pd
+df = ds.to_pandas()
+embs = np.stack(df["sentence_embedding"].values)
+
+anchor_scores = np.zeros(len(df))
+for ex_idx, group in df.groupby("example_idx"):
+    idx = group.index
+    centroid = embs[idx].mean(axis=0)
+    centroid /= np.linalg.norm(centroid) + 1e-12
+    sims = cosine_similarity(embs[idx], centroid.reshape(1, -1)).flatten()
+    anchor_scores[idx] = sims
+
+df["anchor_score"] = anchor_scores
+```
+
+## Related
+
+- Step 1 activations (#### token): `Realmbird/nla-thought-anchors-hash`
+- Step 1 activations (answer token): `Realmbird/nla-thought-anchors-answer`
+- Step 2 NLA descriptions: `Realmbird/nla-thought-anchors-hash-step2`
+- NLA actor: `kitft/nla-qwen2.5-7b-L20-av`
+- NLA critic: `kitft/nla-qwen2.5-7b-L20-ar`