TextQuests: How Good are LLMs at Text-Based Video Games?

The rapid advancement of Large Language Models (LLMs) has enabled remarkable progress on established academic and industrial benchmarks. Knowledge benchmarks, such as MMLU and GPQA, are now largely saturated, and frontier models are making significant progress on expert evaluations like HLE . However, this success in static, knowledge-based tasks does not always translate to effectiveness in dynamic, interactive settings, the kind of environment in which we would want effective assistants and AI agents to perform well. Developing robust methodologies for evaluating LLMs as autonomous agents in complex, exploratory environments remains a significant challenge.

Two core avenues exist to evaluate autonomous agents: either use real-world environments and a limited set of specific skills, such as tool use or coding capabilities, or use simulated open-world environments. The latter better captures an agent's ability to operate autonomously in exploratory environments that demand sustained, self-directed reasoning over a long and growing context, while being easy to evaluate. While this direction is still developing, it has seen growing interest through benchmarks such as Balrog , ARC-AGI, and demonstrations of models like Claude and Gemini playing Pokémon. Building on this emerging vein of work, we introduce TextQuests .

TextQuests is a benchmark built upon 25 classic Infocom interactive fiction games. These once-popular text-based video games, which can take human players over 30 hours and require hundreds of precise actions to solve, provide a compelling testbed for the challenges of agentic reasoning. They demand that an agent demonstrate:

Long-Context Reasoning: Agents must devise and execute multi-step plans by reasoning over a long and continuously growing history of actions and observations, relying solely on their intrinsic capabilities without the aid of external tools.

Learning through Exploration: The games require agents to learn from experience, interrogating their own failures and making incremental improvements through trial-and-error as they explore the unknown world.

Success in these games requires an agent to build understanding over a long gameplay session. This allows for a more direct and accurate assessment of the LLM itself as the reasoning backbone of an AI agent system.