---
license: cc-by-4.0
task_categories:
- image-text-to-text
language:
- en
tags:
- gui-agent
- mobile-agent
- long-horizon
- sparse-history
- android
- preference-data
size_categories:
- 10K<n<100K
pretty_name: CausalCache Sparse-History Corpus (GUI-Odyssey)
---

# CausalCache Sparse-History Corpus

**A controlled corpus for testing whether a GUI agent actually *reads* its visual history — or merely benefits from history being present.**

Mobile GUI agents normally see a sliding window of the *most recent* screenshots. That is a poor fit for long-horizon tasks, where the screen you need to remember may be twenty steps back. This corpus lets you train and, more importantly, **falsify** adapters that claim to read a sparse, non-contiguous selection of historical screenshots.

The policy stays **frozen** throughout. Every row in a group shares one target action, so the objective is purely about *relative* teacher-forced log-probability between conditions — cross-entropy weight is zero.

<img src="figures/arms.svg" alt="The five arms: one target action, one thing varies at a time" width="100%">

## Why the arms are built this way

The trap in this kind of work is measuring a **format effect** or a **history-presence effect** and reporting it as a **content effect**. So each comparison changes exactly one thing:

| quantity | comparison | what it isolates |
|---|---|---|
| `format_effect` | `R0 − N0` | official multi-turn vs single-turn prompt |
| `frozen_selection_effect` | `S0 − R0` | sparse selection, **with no adapter at all** |
| `adapter_on_recent` | `RA − R0` | adapter's effect on a *recent* window |
| `adapter_on_sparse` | `SA − S0` | adapter's effect on a *sparse* window |
| **`main_claim`** | **`SA − RA`** | **the adapter's contribution, prompt held fixed** |
| `deployment_delta` | `SA − N0` | end-to-end vs the deployed baseline |

Two properties make this checkable rather than merely asserted:

- **`S0` is exactly `SA`'s bypass twin** — same messages, same images, only `adapter_mode` differs. So `adapter_on_sparse` needs no extra forward pass, and a zero-init adapter *must* reproduce `SA − RA == frozen_selection_effect`. That identity is a built-in correctness assertion for any scorer you write.
- **Token counts confirm the isolation.** Measured on the real processor: `R0` and `RA` tokenize to *identical* lengths, as do `S0`, `SA` and all three negatives. `SA − RA` therefore cannot be a prompt-length artifact.

## Negatives

Three corrupted-history variants, all sparse + adapter-active, let you ask whether the model distinguishes *correct* history from *plausible but wrong* history:

| negative | corruption |
|---|---|
| `SA_neg_step_shuffled` | same images, **cyclically shifted** so every position is mismatched |
| `SA_neg_irrelevant` | images from a **different episode in the same split**, matched position-by-position on the age vector and preferring the same resolution |
| `SA_neg_duplicate` | one history image repeated K times |

A cyclic shift is used rather than a reversal because reversing an odd-length sequence leaves the middle image correctly paired.

## Composition

<img src="figures/corpus.svg" alt="Corpus composition" width="100%">

## Usage

```python
import gzip, json, tarfile
from huggingface_hub import snapshot_download

root = snapshot_download(
    "gavinlaw/causalcache-sparse-history-guiodyssey-v5", repo_type="dataset"
)

with gzip.open(f"{root}/samples.jsonl.gz", "rt", encoding="utf-8") as fh:
    rows = [json.loads(line) for line in fh]

# images live in per-shard tars; each row's paths are relative to the shard root
with tarfile.open(f"{root}/shards/images-part00.tar") as tf:
    tf.extractall("part00/")

group = {}
for r in rows:
    if r["pair_group"] == rows[0]["pair_group"]:
        group[r["arm_slot"]] = r

assert group["SA"]["target_text"] == group["RA"]["target_text"]   # one target per group
assert group["S0"]["adapter_mode"] == "bypass"                    # SA's bypass twin
assert group["SA"]["selected_images"] == group["S0"]["selected_images"]
```

### Row schema

| field | meaning |
|---|---|
| `pair_group` | `"{episode}:{decision_step}"` — the unit of comparison |
| `arm_slot` | `N0`/`R0`/`S0`/`RA`/`SA`/`SA_neg_*` — **the primary key within a group** |
| `adapter_mode` | `"bypass"` or `"active"` — **the sole authority on the adapter switch** |
| `role` | `deployment_baseline` / `reference` / `measurement` / `positive` / `negative` |
| `budget` | K, the number of history images |
| `selected_steps` | 1-based, strictly increasing, all `< decision_step` |
| `selected_images`, `current_image` | paths relative to the shard root |
| `target_text` | byte-identical across every arm in the group |
| `split` | `train` / `heldout` — **read this field; do not recompute a hash** |
| `negative_kind`, `negative_scale` | present only on negatives |

> **Read `adapter_mode`; never infer it from a name.** Consumers that pattern-match on `arm_slot` prefixes will silently break the moment an arm is renamed — that exact bug is why the field exists.

## Provenance

Everything is pinned. This corpus was rebuilt on a second machine from these public sources alone and produced a **byte-identical** result — the sorted `samples.jsonl` sha256 matched exactly, on both machines:

```
4f6f7065f2053ff0501a2767e98a3656046106c4042b0cc9cf97e9e1c494e063
```

| | repo | revision |
|---|---|---|
| trajectories | [`cua-lite/GUIOdyssey`](https://huggingface.co/datasets/cua-lite/GUIOdyssey) | `ea08072b30e523fb4492e4f4597505879ffcd63b` |
| frozen policy used for scoring | [`mPLUG/GUI-Owl-1.5-8B-Instruct`](https://huggingface.co/mPLUG/GUI-Owl-1.5-8B-Instruct) | `06d5faecff74840bab2be2425e9c42667a5d04fc` |

Screenshots and action descriptions originate from **[GUI-Odyssey](https://huggingface.co/datasets/OpenGVLab/GUI-Odyssey)** (OpenGVLab), released under **CC BY 4.0**; this derivative keeps the same license and attribution. Please cite GUI-Odyssey alongside this corpus.

## Known limitations

Stated plainly, because they change how results must be analysed:

- **Groups are not independent.** Several decision points come from the same trajectory. Confidence intervals must use **episode-cluster bootstrap** — treating the 3,252 groups as i.i.d. understated our intervals by roughly half (0.053 vs 0.096 on the same data).
- **Short histories admit very few sparse selections.** With `current_step=9, K=4` there are only 5 legal non-adjacent subsets, so those groups' `S0`/`SA` prompts repeat heavily and contribute almost no variance to `S0 − R0` or `SA − RA`. This follows from the non-adjacency constraint, not from a defect — but report those strata separately.
- **`irrelevant` donors are age-matched, not semantics-matched.** They come from a different episode at a comparable task stage; they are not adversarially chosen to be confusable.
- **The corpus does not settle whether an adapter works.** It is built so that a negative result is legible. On our own frozen baseline, the three negative margins are *negative* — the frozen model scores corrupted history *above* correct history. Any claim of content sensitivity has to clear that bar first.

## Citation

```bibtex
@misc{causalcache_sparse_history_v5,
  title  = {CausalCache Sparse-History Corpus (GUI-Odyssey)},
  author = {Luo, Jiaxuan},
  year   = {2026},
  url    = {https://huggingface.co/datasets/gavinlaw/causalcache-sparse-history-guiodyssey-v5}
}
```

Please also cite the upstream source:

```bibtex
@article{lu2024guiodyssey,
  title   = {GUIOdyssey: A Comprehensive Dataset for Cross-App GUI Navigation on Mobile Devices},
  author  = {Lu, Quanfeng and Shao, Wenqi and Liu, Zitao and Du, Lingxiao and Meng, Fanqing
             and Li, Boxuan and Chen, Botong and Huang, Siyuan and Zhang, Kaipeng and Luo, Ping},
  journal = {arXiv preprint arXiv:2406.08451},
  year    = {2024}
}
```