Datasets:

collinear-ai
/

yc-bench

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

Evaluation

Download run_yc_bench_job.py from this repo, then:

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 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 in this repo for the format.

License

Apache 2.0