$\texttt{YC-Bench}$: Benchmarking AI Agents for Long-Term Planning and Consistent Execution
As LLM agents tackle increasingly complex tasks, a critical question is whether they can maintain strategic coherence over long horizons: planning under uncertainty, learning from delayed feedback, and adapting when early mistakes compound. We introduce $\texttt{YC-Bench}$, a benchmark that evaluates these capabilities by tasking an agent with running a simulated startup over a one-year horizon spanning hundreds of turns. The agent must manage employees, select task contracts, and maintain profitability in a partially observable environment where adversarial clients and growing payroll create compounding consequences for poor decisions. We evaluate 12 models, both proprietary and open sou...
| Authors | Muyu He, Adit Jain, Anand Kumar et al. |
|---|---|
| Primary category | cs.CL |
| Published | 1 April 2026 |
| Last updated at source | 1 April 2026 |
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