arXiv:2606.19613v1 Announce Type: cross Abstract: We introduce StaminaBench, a benchmark that measures the stamina of coding agents: how many consecutive interaction turns (change requests) they can handle before failing. Unlike the prevailing fraction-of-tasks-solved metric, this matches real vibe-coding where sessions run dozens or hundreds of turns. In StaminaBench, agents implement a REST API server and modify it across a tunable number of procedurally generated follow-up change requests - 100 in our experiments, resulting in codebases of up to 6,000 lines. Tests are generated fully progra
The proliferation of coding agents necessitates more robust evaluation benchmarks that reflect real-world, iterative development cycles rather than single-task completion.
This benchmark addresses a critical gap in evaluating AI coding agents, moving beyond simple task completion to assess their 'stamina' and ability to handle complex, ongoing projects, which is crucial for their integration into mainstream development workflows.
The criteria for evaluating and developing AI coding agents will shift, prioritizing their ability to sustain performance over long interactive sequences and adapt to evolving requirements, rather than just solving isolated problems.
- · AI agent developers focused on long-term interaction
- · Companies seeking highly autonomous coding solutions
- · Software development teams adopting AI tools
- · AI agent developers focused solely on single-turn tasks
- · Benchmarks limited to simple, one-off project evaluations
Increased focus on memory, context management, and iterative refinement capabilities in AI coding agent research and development.
Accelerated adoption of AI agents for more complex, multi-stage software projects as their sustained reliability improves.
The developer role for human engineers shifts further towards oversight, high-level design, and complex problem-solving, rather than repetitive coding tasks.
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Read at arXiv cs.AI