---
pretty_name: DX Terminal Pro Research Aggregates
license: cc-by-4.0
language:
- en
tags:
- tabular
- agent-evaluation
- research-aggregates
- arxiv:2604.26091
- arxiv:2609.05663
configs:
- config_name: market_behavior
  default: true
  data_files:
  - split: observations
    path: data/market_behavior.csv
- config_name: mandate_compilation
  data_files:
  - split: observations
    path: data/mandate_compilation.csv
---

# DX Terminal Pro research aggregates

Nine published research records from DX Research Group's Terminal Pro work. These are small aggregate evidence tables. They contain no participant-level decision logs or training trajectories.

The two configurations have different units of analysis:

| Configuration | Records | What a row represents |
|---|---:|---|
| `market_behavior` | 4 | A reported event or token-window aggregate from the bounded 21-day real-capital Terminal Pro deployment |
| `mandate_compilation` | 5 | A reported pre-launch comparison or historical live setting gradient |

The market-behavior rows describe the historical Base deployment. The mandate table contains three controlled pre-launch examples and two historical live gradients. Do not pool these as independent trials from one experiment. The earlier 2025 DX Terminal simulation is not included.

## Load the tables

```python
from datasets import load_dataset

market = load_dataset(
    "DXRG/dx-terminal-pro-research-aggregates",
    "market_behavior",
    split="observations",
)
controls = load_dataset(
    "DXRG/dx-terminal-pro-research-aggregates",
    "mandate_compilation",
    split="observations",
)
```


## Sources and provenance

Primary paper: [Operating-Layer Controls for Onchain Language-Model Agents Under Real Capital, arXiv:2604.26091v1](https://arxiv.org/abs/2604.26091v1). The [subsequent two-fleet paper](https://arxiv.org/abs/2609.05663v1) supplies the broader research context; these two configurations are Terminal Pro material.

The market CSV preserves the four records from [DXRG's published observations](https://www.dxrg.ai/research/dx-terminal-pro-market-behavior-observations.v1.csv), version 1.0.0. The control CSV preserves all five records and original columns from [the mandate-compilation evidence table](https://www.dxrg.ai/research/trading-agent-mandate-compilation-evidence.v1.csv), version 1.0.0, and adds `measure_scope`, `comparison_sample_size` and `sample_size_note` to clarify denominators. The original metadata objects are preserved in `source_context/`.

This release republishes reported measurements. It does not rerun the underlying analyses or independently validate the paper's findings. Dataset release version: 1.0.0.

## Reading the measurements

- The fabricated-rule comparison measures sell decisions in the affected pre-launch populations. Its before/after percentages describe a combined intervention, not an isolated memory-label effect.
- The paper's aggregate diagnosis of 4,900 reasoning traces is not the denominator for every comparison. Exact per-arm counts are not supplied for these records; missing sample-size values remain null.
- Trading Activity is a percentage of invocations that produce trade actions. Trade Size is a share of available ETH used per trade. Their similar percentage notation does not make them the same measure.
- The two-sided-flow percentage is trade-weighted and uses five-minute windows for the same token. A two-sided window contains at least one buy and one sell.
- A sell cascade requires at least ten vaults selling the same token within ten minutes. The largest reported cascade and the total cascade count are different aggregates.
- Missing numeric cells mean a field is not applicable or is not supplied in that source record. They must not be converted to zero.

The source locators, definitions and evidence-boundary columns accompany the values. No new causal effect, statistical significance, prevalence estimate or performance result is inferred by this packaging.

## Scope and exclusions

No wallet, vault, user, account, agent, order or trade identifiers are included. User strategies, chat, model prompts and reasoning, per-participant returns and individual transaction records are excluded. The separate internal EVM construction evaluation and internal model-selection screen are also excluded.

The data describes a bounded historical experiment. It is not evidence of current DXAP Alpha performance, a general trading advantage, safety, or transfer to other venues. The published record includes limitations and negative findings; readers should use the linked papers for their full context.

## License and citation

License: [CC BY 4.0](https://creativecommons.org/licenses/by/4.0/), consistent with the associated papers. Attribute DX Research Group and the source paper when reusing the tables. Cite the original frozen paper version for a reported result and this dataset version for the packaged tables.