RevengeBench Traces

Data for RevengeBench: Reverse Engineering Code-Space Policies from Behavioral Experiments

• Project Page
• Code

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

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

@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}
}