mirror of
https://github.com/collinear-ai/yc-bench.git
synced 2026-04-19 12:58:03 +00:00
373 lines
15 KiB
Markdown
373 lines
15 KiB
Markdown
# YC-Bench
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A long-horizon deterministic benchmark for LLM agents. The agent plays CEO of an AI startup over a simulated 1–3 year run, operating exclusively through a CLI tool against a SQLite-backed discrete-event simulation.
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The benchmark tests whether agents can manage compounding decisions: prestige specialisation, employee allocation, cash flow, and deadline risk — sustained over hundreds of turns.
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---
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## Simulation Dynamics
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```
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┌─────────────────────────────────────────────────────────────────────────┐
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│ AGENT (LLM) │
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│ │
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│ Observes: company status · employee skills · market tasks · ledger │
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│ Acts via: run_command("yc-bench <cmd>") · scratchpad (persistent) │
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└───────────────────────┬─────────────────────────────────────────────────┘
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│ CLI commands (JSON responses)
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▼
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┌─────────────────────────────────────────────────────────────────────────┐
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│ DISCRETE-EVENT SIMULATION │
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│ │
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│ ┌─────────────┐ accept ┌──────────┐ assign+dispatch │
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│ │ MARKET │ ──────────► │ PLANNED │ ──────────────────► │
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│ │ 100 tasks │ └──────────┘ │
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│ └─────────────┘ │
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│ ▲ replenish ┌──────────────────────┐ │
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│ │ │ ACTIVE │ │
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│ │ ┌────────────────────────── │ progress flushes │ │
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│ │ │ │ every sim-advance │ │
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│ │ │ └──────────┬───────────┘ │
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│ │ │ ┌───────────────────────────────────┘ │
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│ │ │ │ ETA solver fires TASK_COMPLETED event │
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│ │ │ ▼ │
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│ │ │ ┌────────────────────────────────────────────────────┐ │
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│ │ │ │ TASK_COMPLETED handler │ │
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│ │ │ │ │ │
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│ │ │ │ on_time? YES → +reward_funds +prestige_delta │ │
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│ │ │ │ +skill_boost +salary_bump │ │
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│ │ │ │ NO → -1.4× prestige_delta (penalty) │ │
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│ └───┘ └─────────────────────┬───────────────────────────── ┘ │
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│ │ │
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│ ┌──────────────────────────────────┘ │
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│ │ Monthly payroll (1st biz day) Bankruptcy check (funds < 0) │
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│ │ Horizon end (1–3 years) Context truncation (last 20 rounds)│
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└──┴──────────────────────────────────────────────────────────────────────┘
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```
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### Core loop
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1. Agent calls `yc-bench sim resume` to advance time to the next event.
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2. The engine flushes task progress, fires due events, applies payroll.
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3. Agent reads wake events and decides: accept tasks, assign employees, dispatch, cancel.
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4. Repeat until bankruptcy or horizon end.
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If the agent doesn't call `sim resume` for N consecutive turns (default 10), the loop forces one automatically.
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---
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## Economy
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### Funds
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- Start: **$250,000** (`initial_funds_cents = 25_000_000`)
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- Payroll deducted on the **first business day of each month**
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- Task reward formula: `base × (1 + reward_prestige_scale × (prestige_req − 1))`
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- Base: triangular sample in [$5K, $100K], mode $30K
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- `reward_prestige_scale = 0.55` (default): a prestige-8 task pays ~4.85× more than prestige-1
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### Monthly payroll (5 employees, fast_test)
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| Tier | Share | Salary/month | Skill rate |
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|------|-------|-------------|------------|
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| Junior | 50% | $2K–$4K | 1.0–6.5 units/hr |
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| Mid | 35% | $6K–$8K | 3.5–8.5 units/hr |
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| Senior | 15% | $10K–$15K | 5.5–10.0 units/hr |
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Monthly payroll ≈ **$32K** (5 employees). Starting runway ≈ **7.8 months**.
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### Task completion rewards
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On success:
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- Funds += `reward_funds_cents`
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- Prestige += `reward_prestige_delta` (beta-distributed, typically 0.1–1.5) per required domain
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- Skill rate += `skill_boost_pct × current_rate` per assigned employee per domain
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- Salary += `1% × current_salary` per assigned employee (compounding payroll pressure)
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On failure (past deadline):
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- Prestige −= `1.4 × reward_prestige_delta` per domain
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On cancel:
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- Prestige −= `2.0 × reward_prestige_delta` per domain
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---
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## Prestige
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7 domains: `system · research · data · frontend · backend · training · hardware`
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- Range: **[1.0, 10.0]** per domain, starts at 1.0
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- Tasks require a minimum prestige level. Agent can only accept tasks where `max(company_prestige) >= required_prestige`.
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- Default distribution: mode=4, so most tasks need prestige 3–5.
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- First 10 market tasks are stratified `[1,1,1,1,2,2,2,3,3,4]` to bootstrap progression.
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Specialising in 2–3 domains unlocks progressively higher-reward tasks. Spreading thin keeps you locked at low prestige everywhere.
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---
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## Employee throughput
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Each employee has a skill rate (units/hr) per domain.
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When an employee is assigned to N active tasks simultaneously:
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```
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effective_rate_per_task = base_rate / N
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```
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Assigning one senior (rate 8.0) to 4 tasks gives 2.0 units/hr each — often worse than a junior focused on one.
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Task completion time = `max(remaining[d] / effective_rate[d])` across all required domains.
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Deadline = `max(7, total_required_qty / deadline_qty_per_day)` business days.
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`deadline_qty_per_day = 200` in both `challenge` and `fast_test`. With 10 employees and 5 focused per domain, team throughput ≈ 230 units/domain/day — achievable for up to ~4 simultaneous tasks.
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---
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## Agent interface
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All commands return JSON to stdout.
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### Observe
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```bash
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yc-bench company status # funds, prestige, runway, payroll
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yc-bench employee list # skills, salary, active tasks
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yc-bench market browse # available tasks (--limit N --offset N)
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yc-bench task list [--status X] # planned|active|completed_*|cancelled
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yc-bench task inspect --task-id UUID # progress %, deadline, assignments
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yc-bench finance ledger # full transaction history
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yc-bench report monthly # P&L per month
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yc-bench scratchpad read # persistent notes (survives context truncation)
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```
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### Act
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```bash
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yc-bench task accept --task-id UUID # pull from market, set deadline
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yc-bench task assign --task-id UUID --employee-id UUID
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yc-bench task dispatch --task-id UUID # start work (≥1 assignment required)
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yc-bench task cancel --task-id UUID --reason "" # 2× prestige penalty
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yc-bench sim resume # advance to next event
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yc-bench scratchpad write/append/clear # persistent memory
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```
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---
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## Context management
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- **Proactive truncation**: keeps the last 20 conversation rounds before each API call. Older rounds are dropped.
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- **Scratchpad**: per-company persistent text in DB. Survives truncation. Use it to store strategy, deadlines, and employee assignments.
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---
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## Repository layout
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```
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YC_Bench/
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├── src/ # Python package (yc_bench)
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├── scripts/ # plot_multi_model.py, run_benchmark.sh
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├── logs/ # per-model stdout/stderr logs
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├── db/ # SQLite databases (one per model run)
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├── results/ # JSON rollout files
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├── plots/ # generated PNG charts
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├── pyproject.toml
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└── README.md
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```
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---
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## Setup
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### Prerequisites
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- Python 3.12+
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- [`uv`](https://github.com/astral-sh/uv)
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### Install
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```bash
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git clone <repo-url>
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cd YC_Bench
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uv sync
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```
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No database setup required — the runner auto-creates `db/<seed>_<model>.db` on first run.
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### API key
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```bash
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# .env (any LiteLLM-compatible provider)
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OPENROUTER_API_KEY="sk-or-v1-..."
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# or
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OPENAI_API_KEY="sk-..."
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# or set OPENAI_BASE_URL for a custom OpenAI-compatible endpoint
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```
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### Run a single model
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```bash
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uv run yc-bench run \
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--model openrouter/google/gemini-2.5-flash-preview \
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--seed 1 \
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--config challenge
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```
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Outputs:
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- `db/1_openrouter_google_gemini-2.5-flash-preview.db` — SQLite simulation state
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- `results/yc_bench_result_1_openrouter_google_gemini-2.5-flash-preview.json` — full rollout + transcript
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### Run 5 models in parallel
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```bash
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bash scripts/run_benchmark.sh --seed 1 --config challenge
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```
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### Generate the comparison plot
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```bash
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uv run python scripts/plot_multi_model.py --seed 1 --config challenge --budget 30
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# → plots/funds_curves.png
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```
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---
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## Configuration
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Experiment presets live in `src/yc_bench/config/presets/` as TOML files. Pass the preset name via `--config`.
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```
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src/yc_bench/config/presets/
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├── default.toml # 3yr, 10 employees, 500 tasks — hardened (deadline_qty=320)
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├── challenge.toml # 3yr, 10 employees, 300 tasks — calibrated for interesting behavior
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└── fast_test.toml # 1yr, 5 employees, 100 tasks — quick iteration (50-turn cap)
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```
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The **`challenge`** preset is the recommended config for inter-model comparison. It is calibrated so that:
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- A focused agent (≤4 simultaneous tasks) consistently beats deadlines and grows prestige.
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- A spread agent (5+ tasks in parallel, diluted throughput) misses deadlines, loses prestige, goes bankrupt.
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- The best models reach the 3-year horizon; the worst die in month 3.
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### Key WorldConfig parameters
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| Parameter | Default | Controls |
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|-----------|---------|---------|
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| `initial_funds_cents` | 25_000_000 | Starting cash ($250K) |
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| `num_employees` | 5 | Workforce size |
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| `num_market_tasks` | 100 | Market pool size |
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| `required_prestige_mode` | 4 | Peak of prestige-req distribution |
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| `domain_count_mode` | 2 | Most tasks require 2 domains |
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| `required_qty_low/mode` | 500 / 1400 | Task work volume (units) |
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| `deadline_qty_per_day` | 200 | Units completable per biz day (lower = easier) |
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| `deadline_min_biz_days` | 7 | Minimum deadline |
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| `penalty_fail_multiplier` | 1.4 | Prestige × this on deadline miss |
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| `penalty_cancel_multiplier` | 2.0 | Prestige × this on cancel |
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| `reward_prestige_scale` | 0.55 | Extra reward fraction per prestige level above 1 |
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| `salary_bump_pct` | 0.01 | Salary raise per employee per completed task |
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### AgentConfig
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| Parameter | Default | Controls |
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|-----------|---------|---------|
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| `model` | openrouter/openai/gpt-4o-mini | LLM model string |
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| `temperature` | 0.0 | Sampling temperature |
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| `history_keep_rounds` | 20 | Conversation rounds kept in context |
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### LoopConfig
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| Parameter | Default | Controls |
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|-----------|---------|---------|
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| `auto_advance_after_turns` | 5 | Force sim resume after N turns without one |
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| `max_turns` | 50 | Hard cap on agent turns (null = unlimited) |
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### Environment overrides
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```bash
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YC_BENCH_EXPERIMENT=fast_test # select preset
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DATABASE_URL=sqlite:///custom.db # SQLite path
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```
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---
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## Terminal conditions
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| Condition | Trigger |
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|-----------|---------|
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| Horizon end | `sim_time >= start_date + horizon_years` |
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| Bankruptcy | `funds_cents < 0` after any payroll |
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| Error | Agent runtime exception (API failure, exhausted retries) |
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| Max turns | `turn_count >= max_turns` (if set) |
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---
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## What makes it hard
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The hardened default is designed so that the obvious strategies fail:
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- **Prestige-1 farming** is unprofitable. Most replacement tasks need prestige 3–5 and pay much more. Farming the bottom locks you out.
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- **Single-specialist dominance** is gone. Most tasks need 2 domains. You must allocate across skill combinations.
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- **Speculative accepting** is punished. Cancel penalty (2×) exceeds fail penalty (1.4×) so you can't accept everything and drop the losers.
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- **Ignoring payroll** causes bankruptcy. ~$32K/month burns your $250K in 7.8 months — but task complexity means you must also pace your accepts.
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- **Parallel dispatch** dilutes throughput. Splitting employees across too many tasks extends every deadline — focus beats breadth.
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- **Salary bumps compound**. Every task completion raises assigned employee salaries 1%. Payroll creep accelerates over time.
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---
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## Benchmark results
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_Run `challenge` preset (seed=1, 3yr horizon, 10 employees, 500-turn cap) to generate updated results._
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---
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## Simulation rules
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- **Business time**: weekdays only, 09:00–18:00. No leap years.
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- **Money**: stored as integer cents (`BIGINT`). No floating point.
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- **Payroll**: fired on the first business day of each month.
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- **Event ordering**: deterministic — `(scheduled_at, priority, id)`.
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- **Determinism**: all task generation and employee seeding is reproducible given `--seed`.
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- **Prestige**: `NUMERIC(6,3)`, hard clamped to `[1.0, 10.0]`.
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- **DB reuse**: if a simulation is terminal (bankrupt or horizon reached), re-running with the same DB wipes and reseeds cleanly.
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---
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## Output format
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`results/yc_bench_result_<seed>_<model>.json`:
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```json
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{
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"session_id": "run-1-openrouter/openai/gpt-4o-mini",
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"model": "openrouter/openai/gpt-4o-mini",
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"seed": 1,
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"horizon_years": 1,
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"turns_completed": 46,
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"terminal": true,
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"terminal_reason": "bankruptcy",
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"total_cost_usd": 0.100008,
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"started_at": "...",
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"ended_at": "...",
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"transcript": [
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{
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"turn": 1,
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"timestamp": "...",
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"user_input": "## Simulation Start ...",
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"agent_output": "Executed 3 tool call(s): ...",
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"commands_executed": ["yc-bench company status -> {...}", ...]
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}
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]
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}
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```
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Please cite our work if you find it useful and interesting!
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```bibtex
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@misc{collinear-ai2025ycbench,
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author = {{Collinear AI}},
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title = {{YC-Bench}: Your Company Bench — A Long-Horizon Coherence Benchmark for {LLM} Agents},
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year = {2025},
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howpublished = {\url{https://github.com/collinear-ai/yc-bench}},
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note = {Accessed: 2026-02-25}
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}
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```
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