116 lines
4.3 KiB
Markdown
116 lines
4.3 KiB
Markdown
# YC-Bench: Long-Horizon Agent Benchmark
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[YC-Bench](https://github.com/collinear-ai/yc-bench) by [Collinear AI](https://collinear.ai/) is a deterministic, long-horizon benchmark that tests LLM agents' ability to act as a tech startup CEO. The agent manages a simulated company over 1-3 years, making compounding decisions about resource allocation, cash flow, task management, and prestige specialisation across 4 skill domains.
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Unlike TerminalBench2 (which evaluates per-task coding ability with binary pass/fail), YC-Bench measures **long-term strategic coherence** — whether an agent can maintain consistent strategy, manage compounding consequences, and adapt plans over hundreds of turns.
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## Setup
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```bash
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# Install yc-bench (optional dependency)
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pip install "hermes-agent[yc-bench]"
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# Or install from source
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git clone https://github.com/collinear-ai/yc-bench
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cd yc-bench && pip install -e .
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# Verify
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yc-bench --help
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```
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## Running
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```bash
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# From the repo root:
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bash environments/benchmarks/yc_bench/run_eval.sh
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# Or directly:
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python environments/benchmarks/yc_bench/yc_bench_env.py evaluate \
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--config environments/benchmarks/yc_bench/default.yaml
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# Override model:
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bash environments/benchmarks/yc_bench/run_eval.sh \
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--openai.model_name anthropic/claude-opus-4-20250514
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# Quick single-preset test:
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bash environments/benchmarks/yc_bench/run_eval.sh \
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--env.presets '["fast_test"]' --env.seeds '[1]'
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```
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## How It Works
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### Architecture
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```
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HermesAgentLoop (our agent)
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-> terminal tool -> subprocess("yc-bench company status") -> JSON output
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-> terminal tool -> subprocess("yc-bench task accept --task-id X") -> JSON
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-> terminal tool -> subprocess("yc-bench sim resume") -> JSON (advance time)
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-> ... (100-500 turns per run)
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```
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The environment initialises the simulation via `yc-bench sim init` (NOT `yc-bench run`, which would start yc-bench's own built-in agent loop). Our `HermesAgentLoop` then drives all interaction through CLI commands.
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### Simulation Mechanics
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- **4 skill domains**: research, inference, data_environment, training
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- **Prestige system** (1.0-10.0): Gates access to higher-paying tasks
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- **Employee management**: Junior/Mid/Senior with domain-specific skill rates
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- **Throughput splitting**: `effective_rate = base_rate / N` active tasks per employee
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- **Financial pressure**: Monthly payroll, bankruptcy = game over
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- **Deterministic**: SHA256-based RNG — same seed + preset = same world
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### Difficulty Presets
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| Preset | Employees | Tasks | Focus |
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|-----------|-----------|-------|-------|
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| tutorial | 3 | 50 | Basic loop mechanics |
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| easy | 5 | 100 | Throughput awareness |
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| **medium**| 5 | 150 | Prestige climbing + domain specialisation |
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| **hard** | 7 | 200 | Precise ETA reasoning |
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| nightmare | 8 | 300 | Sustained perfection under payroll pressure |
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| fast_test | (varies) | (varies) | Quick validation (~50 turns) |
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Default eval runs **fast_test + medium + hard** × 3 seeds = 9 runs.
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### Scoring
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```
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composite = 0.5 × survival + 0.5 × normalised_funds
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```
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- **Survival** (binary): Did the company avoid bankruptcy?
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- **Normalised funds** (0.0-1.0): Log-scale relative to initial $250K capital
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## Configuration
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Key fields in `default.yaml`:
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| Field | Default | Description |
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|-------|---------|-------------|
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| `presets` | `["fast_test", "medium", "hard"]` | Which presets to evaluate |
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| `seeds` | `[1, 2, 3]` | RNG seeds per preset |
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| `max_agent_turns` | 200 | Max LLM calls per run |
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| `run_timeout` | 3600 | Wall-clock timeout per run (seconds) |
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| `survival_weight` | 0.5 | Weight of survival in composite score |
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| `funds_weight` | 0.5 | Weight of normalised funds in composite |
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| `horizon_years` | null | Override horizon (null = auto from preset) |
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## Cost & Time Estimates
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Each run is 100-500 LLM turns. Approximate costs per run at typical API rates:
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| Preset | Turns | Time | Est. Cost |
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|--------|-------|------|-----------|
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| fast_test | ~50 | 5-10 min | $1-5 |
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| medium | ~200 | 20-40 min | $5-15 |
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| hard | ~300 | 30-60 min | $10-25 |
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Full default eval (9 runs): ~3-6 hours, $50-200 depending on model.
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## References
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- [collinear-ai/yc-bench](https://github.com/collinear-ai/yc-bench) — Official repository
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- [Collinear AI](https://collinear.ai/) — Company behind yc-bench
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- [TerminalBench2](../terminalbench_2/) — Per-task coding benchmark (complementary)
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