---
license: cc-by-4.0
language:
- en
pretty_name: RevengeBench Traces
size_categories:
- 100K<n<1M
tags:
- agents
- llm-agents
- code
- traces
- benchmark
authors:
- babak-rahmani
- sebastian-dziadzio
- joschka-strueber
- sergio-hernandez-gutierrez
- matthias-bethge
dataset_info:
  default: tournaments
configs:
- config_name: tournaments
  data_files: tournaments.parquet
  default: true
- config_name: messages
  data_files: messages.parquet
- config_name: simulations
  data_files: simulations.parquet
- config_name: experiment_configs
  data_files: experiment_configs.parquet
---

# RevengeBench Traces

Data for [RevengeBench: Reverse Engineering Code-Space Policies from Behavioral Experiments](https://arxiv.org/abs/2606.26094)
- [Project Page](https://revengebench.com/)
- [Code](https://github.com/bethgelab/revenge-bench)

Per-message traces and per-round simulation outcomes for RevengeBench tournaments,
normalized into four Parquet tables joined by `tournament_id` and `config_id`.

## License

The dataset structure (schemas, identifiers, derived metrics) is released under
**CC-BY-4.0** (https://creativecommons.org/licenses/by/4.0/).

Per-row model output content is governed by the upstream model provider's terms
of service; see `experiment_configs.output_license_notice` for the per-config
notice. Downstream re-use of message content is the user's responsibility.

## Limitations and intended use

**Intended uses:** research on agent code-editing behavior, strategy recovery,
scientific reasoning analysis, evaluation methodology, and behavioral ablations
of LLMs.

**Out of scope:** training models on the contents (subject to provider TOS);
claims about "true" model capabilities (the dataset is a snapshot in time per
model); use as evidence in adversarial-evaluation contexts beyond what the
paper claims.

**Sensitive content:** message content is LLM reasoning about programming
tasks; no flagged content is expected.

## Schema

The release contains four Parquet tables. Map- and list-valued columns in
`tournaments` are stored as **JSON-encoded strings** (parse with `json.loads`;
keys are round numbers as strings). Missing data in these columns is encoded
as the JSON string `"null"`, never as a Parquet NULL, so `json.loads` succeeds
on every row.

| Table | Column | Type |
|---|---|---|
| `tournaments` | `tournament_id` | `string` |
| `tournaments` | `experiment_kind` | `string` |
| `tournaments` | `model_slug` | `string` |
| `tournaments` | `model_display_name` | `string` |
| `tournaments` | `ablation_condition` | `string` |
| `tournaments` | `condition` | `string` |
| `tournaments` | `game` | `string` |
| `tournaments` | `target_hash` | `string` |
| `tournaments` | `target_display` | `string` |
| `tournaments` | `seed` | `int64` |
| `tournaments` | `n_rounds` | `int64` |
| `tournaments` | `distances` | `string` (JSON: round → double, `null` if evaluation failed) |
| `tournaments` | `best_distance` | `double` |
| `tournaments` | `best_round` | `int64` |
| `tournaments` | `evaluation_failed` | `string` (JSON: round → bool) |
| `tournaments` | `exit_status` | `string` (JSON: round → string) |
| `tournaments` | `per_round_failures` | `string` (JSON: round → `{eval_fail, no_submit}`) |
| `tournaments` | `per_round_probes` | `string` (JSON: round → int) |
| `tournaments` | `per_round_probe_failures` | `string` (JSON: round → int) |
| `tournaments` | `total_probe_count` | `int64` |
| `tournaments` | `total_probe_attempts` | `int64` |
| `tournaments` | `per_round_usage` | `string` (JSON: round → `{prompt_tokens, completion_tokens, reasoning_tokens, answer_tokens, api_calls, cost}`) |
| `tournaments` | `config_id` | `string` |
| `tournaments` | `dataset_version_added_in` | `string` |
| `tournaments` | `total_prompt_tokens` | `int64` |
| `tournaments` | `total_completion_tokens` | `int64` |
| `tournaments` | `total_reasoning_tokens` | `int64` |
| `tournaments` | `total_answer_tokens` | `int64` |
| `tournaments` | `total_api_calls` | `int64` |
| `tournaments` | `total_cost` | `double` |
| `messages` | `tournament_id` | `string` |
| `messages` | `round` | `int64` |
| `messages` | `turn_index` | `int64` |
| `messages` | `role` | `string` |
| `messages` | `content` | `string` |
| `messages` | `thinking` | `string` |
| `messages` | `tool_calls` | `string` |
| `messages` | `extra_keys` | `list<item: string>` |
| `messages` | `timestamp` | `double` |
| `simulations` | `tournament_id` | `string` |
| `simulations` | `round` | `int64` |
| `simulations` | `sim_index` | `int64` |
| `simulations` | `kind` | `string` |
| `simulations` | `opponent_target_hash` | `string` |
| `simulations` | `winner` | `string` |
| `experiment_configs` | `config_id` | `string` |
| `experiment_configs` | `model_name` | `string` |
| `experiment_configs` | `model_provider` | `string` |
| `experiment_configs` | `system_prompt` | `string` |
| `experiment_configs` | `instance_template` | `string` |
| `experiment_configs` | `agent_kwargs` | `struct<step_limit: int64, cost_limit: double, max_context_chars: int64, keep_recent_observations: int64, max_past_output_chars: int64, max_output_chars: int64>` |
| `experiment_configs` | `arena_kind` | `string` |
| `experiment_configs` | `output_license_notice` | `string` |
| `experiment_configs` | `notes` | `string` |

## Quick start

```python
import json

import pyarrow.parquet as pq

tournaments = pq.read_table("tournaments.parquet").to_pandas()
messages = pq.read_table("messages.parquet").to_pandas()

# JSON-encoded columns parse on every row (missing data decodes to None).
tournaments["distances"] = tournaments["distances"].map(json.loads)

# Example: messages JOIN tournaments to filter by model and game.
joined = messages.merge(
    tournaments[["tournament_id", "model_slug", "game"]],
    on="tournament_id",
    how="inner",
)
print(joined.head())
```

## HuskyBench note

HuskyBench is a single-player game family. By design, HuskyBench tournaments
have `null` `opponent_target_hash` in the `simulations` table. This is not
data loss; HuskyBench has no live opponent.

## Versions

Each row in `tournaments` carries a `dataset_version_added_in` tag. To
restrict messages or simulations to a specific dataset version, join through
`tournaments.dataset_version_added_in` rather than filtering the
message/simulation tables directly.

- **v1.0** — initial release.
- **v1.1** — adds GPT-5, GPT-5.4-mini, GPT-5.5 (low and medium reasoning
  effort), GPT-oss-120b, and Grok-4.1-fast.
- **v1.2** — adds the GPT-5.5 Codex harness ablations (`model_slug`
  `gpt-5-5-codex-low` / `gpt-5-5-codex-high`, `ablation_condition`
  `codex-low` / `codex-high`): the same model driven by the Codex CLI agent
  instead of the default harness, at two reasoning-effort settings, across
  all five arenas. Drops the never-populated `per_round_sim_distances`,
  `per_round_sim_distance_stds`, and `simulations_absent_rounds` columns
  from `tournaments`.

### Codex ablation notes (v1.2)

The Codex harness logs differ from the default harness in a few ways that
show up in the data:

- `messages.role` is `assistant` (agent commentary) or `tool` (command
  executions, MCP tool calls, file changes); the original codex event type is
  kept in `messages.extra_keys`, and command/tool payloads are JSON in
  `messages.tool_calls`.
- `messages.thinking` and `messages.timestamp` are always null.
- Cost fields (`total_cost`, `per_round_usage[*].cost`) are `0.0` — codex
  exec does not report cost.
- Prompt-token counts include cached input tokens: codex resumes the session
  each round, replaying the conversation, so per-round `prompt_tokens` grow
  with round number.

## Provenance

Rows are extracted from RevengeBench tournament pool logs; each `tournaments`
row records the dataset version it was added in
(`dataset_version_added_in`). The extraction scripts live in the companion
code repository.

## Citation

```bibtex
@article{rahmani2026revengebench,
  title={RevengeBench: Reverse Engineering Code-Space Policies from Behavioral Experiments},
  author={Rahmani, Babak and Dziadzio, Sebastian and Str{"u}ber, Joschka and Hern{\'a}ndez-Guti{\'e}rrez, Sergio and Bethge, Matthias},
  year={2026}
}
```