yc-bench/README.md

YC-Bench

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.

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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Simulation Dynamics

Core loop

1. Agent calls yc-bench sim resume to advance time to the next event.
2. The engine flushes task progress, fires due events, applies payroll.
3. Agent reads wake events and decides: accept tasks, assign employees, dispatch, cancel.
4. Repeat until bankruptcy or horizon end.

If the agent doesn't call sim resume for N consecutive turns (default 10), the loop forces one automatically.

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Economy

Funds

• Start: $250,000 (initial_funds_cents = 25_000_000)
• Payroll deducted on the first business day of each month
• Task reward formula: base × (1 + reward_prestige_scale × (prestige_req − 1))
  • Base: triangular sample in [$5K, $100K], mode $30K
  • reward_prestige_scale = 0.55 (default): a prestige-8 task pays ~4.85× more than prestige-1

Monthly payroll (5 employees, fast_test)

Tier	Share	Salary/month	Skill rate
Junior	50%	$2K–$4K	1.0–6.5 units/hr
Mid	35%	$6K–$8K	3.5–8.5 units/hr
Senior	15%	$10K–$15K	5.5–10.0 units/hr

Monthly payroll ≈ $32K (5 employees). Starting runway ≈ 7.8 months.

Task completion rewards

On success:

• Funds += reward_funds_cents
• Prestige += reward_prestige_delta (beta-distributed, typically 0.1–1.5) per required domain
• Skill rate += skill_boost_pct × current_rate per assigned employee per domain
• Salary += 1% × current_salary per assigned employee (compounding payroll pressure)

On failure (past deadline):

• Prestige −= 1.4 × reward_prestige_delta per domain

On cancel:

• Prestige −= 2.0 × reward_prestige_delta per domain

---

Prestige

7 domains: system · research · data · frontend · backend · training · hardware

• Range: [1.0, 10.0] per domain, starts at 1.0
• Tasks require a minimum prestige level. Agent can only accept tasks where max(company_prestige) >= required_prestige.
• Default distribution: mode=4, so most tasks need prestige 3–5.
• First 10 market tasks are stratified [1,1,1,1,2,2,2,3,3,4] to bootstrap progression.

Specialising in 2–3 domains unlocks progressively higher-reward tasks. Spreading thin keeps you locked at low prestige everywhere.

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Employee throughput

Each employee has a skill rate (units/hr) per domain.

When an employee is assigned to N active tasks simultaneously:

effective_rate_per_task = base_rate / N

Assigning one senior (rate 8.0) to 4 tasks gives 2.0 units/hr each — often worse than a junior focused on one.

Task completion time = max(remaining[d] / effective_rate[d]) across all required domains.

Deadline = max(7, total_required_qty / deadline_qty_per_day) business days.

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.

---

Agent interface

All commands return JSON to stdout.

Observe

yc-bench company status              # funds, prestige, runway, payroll
yc-bench employee list               # skills, salary, active tasks
yc-bench market browse               # available tasks (--limit N --offset N)
yc-bench task list [--status X]      # planned|active|completed_*|cancelled
yc-bench task inspect --task-id UUID # progress %, deadline, assignments
yc-bench finance ledger              # full transaction history
yc-bench report monthly              # P&L per month
yc-bench scratchpad read             # persistent notes (survives context truncation)

Act

yc-bench task accept --task-id UUID             # pull from market, set deadline
yc-bench task assign --task-id UUID --employee-id UUID
yc-bench task dispatch --task-id UUID           # start work (≥1 assignment required)
yc-bench task cancel --task-id UUID --reason "" # 2× prestige penalty
yc-bench sim resume                             # advance to next event
yc-bench scratchpad write/append/clear          # persistent memory

---

Context management

• Proactive truncation: keeps the last 20 conversation rounds before each API call. Older rounds are dropped.
• Scratchpad: per-company persistent text in DB. Survives truncation. Use it to store strategy, deadlines, and employee assignments.

---

Repository layout

YC_Bench/
├── src/              # Python package (yc_bench)
├── scripts/          # plot_multi_model.py, run_benchmark.sh
├── logs/             # per-model stdout/stderr logs
├── db/               # SQLite databases (one per model run)
├── results/          # JSON rollout files
├── plots/            # generated PNG charts
├── pyproject.toml
└── README.md

---

Setup

Prerequisites

• Python 3.12+
• uv

Install

git clone <repo-url>
cd YC_Bench
uv sync

No database setup required — the runner auto-creates db/<config>_<seed>_<model>.db on first run.

API key

# .env  (any LiteLLM-compatible provider)
ANTHROPIC_API_KEY="sk-ant-..."     # for anthropic/claude-*
GEMINI_API_KEY="AIza..."           # for gemini/gemini-*
OPENROUTER_API_KEY="sk-or-v1-..."  # for openrouter/*
OPENAI_API_KEY="sk-..."            # for openai/*

Run a single model

uv run yc-bench run \
  --model gemini/gemini-3-flash-preview \
  --seed 1 \
  --config medium

Outputs:

• db/medium_1_gemini_gemini-3-flash-preview.db — SQLite simulation state
• results/yc_bench_result_medium_1_gemini_gemini-3-flash-preview.json — full rollout + transcript

Live dashboard

When running in a terminal, YC-Bench displays an interactive dashboard that updates in-place after each turn:

╭──────────────────────────── YC-Bench ────────────────────────────╮
│ Model        claude-haiku-4-5-20251001  seed=1  medium           │
│ Turn         8                                                   │
│ Sim Date     2025-03-06 -> 2026-01-01                            │
│ Elapsed      0h 02m 34s                                          │
│ Funds        $186,271.66 -$63,728 ██▇▃▁                          │
│ Runway       5.8mo                                               │
│ Tasks        3 active / 3 queued  2 done 1 fail                  │
│ Team         5 people  $31,864.17/mo                              │
│ Cost         $0.0212  (3.7s/turn)                                │
│ Action       yc-bench task dispatch 7                            │
│ Status       >> Turn 9: waiting for LLM...                       │
╰──────────────────────────────────────────────────────────────────╯
╭──────────────────────────── Tasks ───────────────────────────────╮
│ >> Build GPU Cluster    $64,152  2025-02-03  Research ==== Training ====== │
│ >> Deploy Observability $27,908  2025-01-22  Data ===...                   │
│ .. Blue-Green Deploy    $30,780  2025-03-18  Backend ...... Data ......    │
╰──────────────────────────────────────────────────────────────────╯
╭──────────────────────────── Team ────────────────────────────────╮
│ Alice Chen       $2,564  Training===. Frontend==.. Research=... │
│ Bob Martinez    $14,947  Backend===. Research==.. Data==..      │
╰──────────────────────────────────────────────────────────────────╯

The dashboard shows:

• Funds sparkline — visual trend of your cash position over time
• Color-coded progress bars per domain on each task (green = done, yellow = partial, red = low)
• Employee skill bars — top 3 skills per team member with strength indicators
• Runway urgency — green (safe), yellow (low), red blinking (critical)
• Salary heat — expensive employees highlighted in red

To disable the dashboard and see raw log output instead:

uv run yc-bench run --model ... --seed 1 --config medium --no-live

When --no-live is set (or stdout is not a terminal, e.g. piped to a file), the original logging output is used. Debug logs from LiteLLM/httpx are written to logs/debug.log when the dashboard is active.

Run 5 models in parallel

bash scripts/run_benchmark.sh --seed 1 --config challenge

Generate the comparison plot

uv run python scripts/plot_multi_model.py --seed 1 --config challenge --budget 30
# → plots/funds_curves.png

---

Configuration

Experiment presets live in src/yc_bench/config/presets/ as TOML files. Pass the preset name via --config.

src/yc_bench/config/presets/
├── default.toml      # 3yr, 10 employees, 500 tasks — base config
├── tutorial.toml     # 1yr,  3 employees,  50 tasks — learn the loop
├── easy.toml         # 1yr,  5 employees, 100 tasks — throughput awareness
├── medium.toml       # 1yr,  5 employees, 150 tasks — prestige strategy
├── hard.toml         # 1yr,  7 employees, 200 tasks — precise ETA reasoning
├── nightmare.toml    # 1yr,  8 employees, 300 tasks — sustained perfection
├── challenge.toml    # 3yr,  5 employees, 200 tasks — long-horizon endurance
└── fast_test.toml    # 1yr,  5 employees, 100 tasks — quick iteration

Each difficulty level tests one additional concept:

Config	Tests	Key constraint
tutorial	Basic accept→assign→dispatch loop	All prestige-1, single domain
easy	Throughput awareness	Don't over-parallelize
medium	Prestige climbing + domain specialization	2-domain tasks, prestige mode=3
hard	Precise ETA computation	One bad accept degrades in-flight tasks
nightmare	Sustained perfection under compounding payroll	One failure ≈ fatal, salary bumps 2%/task

Key WorldConfig parameters

Parameter	Default	Controls
initial_funds_cents	25_000_000	Starting cash ($250K)
num_employees	5	Workforce size
num_market_tasks	100	Market pool size
required_prestige_mode	4	Peak of prestige-req distribution
domain_count_mode	2	Most tasks require 2 domains
required_qty_low/mode	500 / 1400	Task work volume (units)
deadline_qty_per_day	200	Units completable per biz day (lower = easier)
deadline_min_biz_days	7	Minimum deadline
penalty_fail_multiplier	1.4	Prestige × this on deadline miss
penalty_cancel_multiplier	2.0	Prestige × this on cancel
reward_prestige_scale	0.55	Extra reward fraction per prestige level above 1
salary_bump_pct	0.01	Salary raise per employee per completed task

AgentConfig

Parameter	Default	Controls
model	openrouter/openai/gpt-4o-mini	LLM model string
temperature	0.0	Sampling temperature
history_keep_rounds	20	Conversation rounds kept in context

LoopConfig

Parameter	Default	Controls
auto_advance_after_turns	5	Force sim resume after N turns without one
max_turns	50	Hard cap on agent turns (null = unlimited)

Environment overrides

YC_BENCH_EXPERIMENT=fast_test     # select preset
DATABASE_URL=sqlite:///custom.db  # SQLite path

---

Terminal conditions

Condition	Trigger
Horizon end	sim_time >= start_date + horizon_years
Bankruptcy	funds_cents < 0 after any payroll
Error	Agent runtime exception (API failure, exhausted retries)
Max turns	turn_count >= max_turns (if set)

---

What makes it hard

The hardened default is designed so that the obvious strategies fail:

• Prestige-1 farming is unprofitable. Most replacement tasks need prestige 3–5 and pay much more. Farming the bottom locks you out.
• Single-specialist dominance is gone. Most tasks need 2 domains. You must allocate across skill combinations.
• Speculative accepting is punished. Cancel penalty (2×) exceeds fail penalty (1.4×) so you can't accept everything and drop the losers.
• Ignoring payroll causes bankruptcy. ~$32K/month burns your $250K in 7.8 months — but task complexity means you must also pace your accepts.
• Parallel dispatch dilutes throughput. Splitting employees across too many tasks extends every deadline — focus beats breadth.
• Salary bumps compound. Every task completion raises assigned employee salaries 1%. Payroll creep accelerates over time.

---

Benchmark results

Sonnet 4.6 vs Gemini 3 Flash vs GPT-5.2 — 1-year horizon, 3 seeds per config

Survival rates (at end of year 1)

Config	Sonnet 4.6	Gemini 3 Flash	GPT-5.2
medium	3/3 survived	3/3 survived	3/3 survived
hard	1/3 survived	2/3 survived	2/3 survived
nightmare	1/3 survived	3/3 survived	2/3 survived

Final funds at 1-year mark (bankrupt = funds < 0)

Config	Seed	Sonnet 4.6	Gemini 3 Flash	GPT-5.2
medium	1	$9.1M	$9.5M	$1.8M
medium	2	$6.1M	$11.0M	$321K
medium	3	$107K	$15.8M	$28K
hard	1	bankrupt	bankrupt	bankrupt
hard	2	$63K	$412K	$15.7M
hard	3	bankrupt	$21.9M	$43.5M
nightmare	1	bankrupt	$2.1M	bankrupt
nightmare	2	$10.1M	$214K	$2.2M
nightmare	3	bankrupt	$805K	$23.6M

Overall: Gemini 8/9 · GPT-5.2 7/9 · Sonnet 5/9

Key findings

Gemini leads on consistency (8/9). Near-perfect win rates on medium (93–98%), and the only model to sweep all 3 nightmare seeds. Achieves this without using the scratchpad — purely reactive, high-frequency decision-making.

GPT-5.2 excels at hard (2/3, matching Gemini) with the highest absolute returns. Hard seed 3: $43.5M vs Gemini's $21.9M. Nightmare seed 3: $23.6M vs Gemini's $805K. When GPT-5.2 survives, it tends to outperform by a significant margin.

Sonnet has the highest ceiling when it works but the lowest floor. Nightmare seed 2: $10.1M (best nightmare result). But 4/9 bankruptcies — Sonnet fails harder than the others on adverse seeds.

Hard is the differentiator config. On easy configs all three survive. On hard/nightmare the strategies diverge sharply. Gemini plays safe and consistent; GPT-5.2 swings big; Sonnet is high-variance.

Win rate predicts survival. Every run with >58% task win rate survived. Every run with <40% went bankrupt. Below that threshold, prestige losses from failures outpace gains and lock the agent out of profitable tasks.

Why models fail

The scratchpad evolution of Sonnet on hard seed 2 tells the full story:

Common failure patterns across all bankrupt runs:

1. Over-parallelization. Accepting 3–5 tasks at once, splitting employees across them. Effective rate per task drops below deadline requirements. Sonnet nightmare seed 3 ran 5 tasks simultaneously with 8 employees on turn 13.
2. No prestige gating. Accepting prestige-2 tasks when company prestige is 1.0. The task completes late, triggers a 1.4× prestige penalty, and the agent ends up worse than before.
3. Late adaptation. Sonnet correctly identifies problems in its scratchpad ("PRESTIGE CRISIS — MARKET LOCK") but only after payroll has consumed the runway. By turn 137 of hard seed 2, all tasks require prestige ≥ 2 but the company is stuck at 1.0 in 6 of 7 domains.
4. Inconsistent ETA reasoning. Sonnet's medium seed 2 has a 49% win rate — essentially a coin flip. It understands throughput math in its scratchpad but doesn't consistently apply it when selecting tasks.

Sonnet-only results by config

---

Simulation rules

• Business time: weekdays only, 09:00–18:00. No leap years.
• Money: stored as integer cents (BIGINT). No floating point.
• Payroll: fired on the first business day of each month.
• Event ordering: deterministic — (scheduled_at, priority, id).
• Determinism: all task generation and employee seeding is reproducible given --seed.
• Prestige: NUMERIC(6,3), hard clamped to [1.0, 10.0].
• DB reuse: if a simulation is terminal (bankrupt or horizon reached), re-running with the same DB wipes and reseeds cleanly.

---

Output format

results/yc_bench_result_<config>_<seed>_<model>.json:

{
  "session_id": "run-1-openrouter/openai/gpt-4o-mini",
  "model": "openrouter/openai/gpt-4o-mini",
  "seed": 1,
  "horizon_years": 1,
  "turns_completed": 46,
  "terminal": true,
  "terminal_reason": "bankruptcy",
  "total_cost_usd": 0.100008,
  "started_at": "...",
  "ended_at": "...",
  "transcript": [
    {
      "turn": 1,
      "timestamp": "...",
      "user_input": "## Simulation Start ...",
      "agent_output": "Executed 3 tool call(s): ...",
      "commands_executed": ["yc-bench company status -> {...}", ...]
    }
  ]
}

Please cite our work if you find it useful and interesting!

@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}},
  note         = {Accessed: 2026-02-25}
}