Datasets:
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pretty_name: "YC-Bench"
language:
- en
license: apache-2.0
size_categories:
- n<1K
task_categories:
- text-generation
tags:
- benchmark
- agents
- long-horizon
- simulation
- evaluation
citation: |
@misc{collinear-ai2025ycbench,
author = {{Collinear AI}},
title = {{YC-Bench}: Your Company Bench — A Long-Horizon Coherence Benchmark for {LLM} Agents},
year = {2025},
howpublished = {\url{https://github.com/collinear-ai/yc-bench}}}
---
# YC-Bench
Long-horizon agent benchmark. The LLM plays CEO of an AI startup for 1 simulated year via CLI tool use against a deterministic discrete-event simulation.
Tests: employee allocation, prestige specialization, cash flow, deadline risk, adversarial client detection — sustained over hundreds of turns.
Source: [github.com/collinear-ai/yc-bench](https://github.com/collinear-ai/yc-bench)
## Evaluation
Download `run_yc_bench_job.py` from this repo, then:
```bash
hf jobs uv run run_yc_bench_job.py \
--flavor cpu-basic --timeout 3h \
--secrets OPENAI_API_KEY \
-- openai/gpt-5.4
```
Or run locally: `uv run run_yc_bench_job.py openai/gpt-5.4`
Runs medium preset on seeds 1-3 and reports average final funds. Pass the appropriate `--secrets` flag for your provider (`ANTHROPIC_API_KEY`, `OPENROUTER_API_KEY`, etc). Any [LiteLLM-compatible](https://docs.litellm.ai/docs/providers) model string works.
## Scoring
**Average final funds (USD) across seeds 1, 2, 3.** Bankrupt = $0.
```
score = average(max(0, final_funds_cents / 100) for each seed)
```
## Submitting to leaderboard
Open a PR on the model's HF repo adding `.eval_results/yc-bench.yaml`. See [`sample_eval_result.yaml`](sample_eval_result.yaml) in this repo for the format.
## License
Apache 2.0
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