MC-EvoBench: Auditing Experience-Driven Self-Improvement in Minecraft Agents

Key points

• A saved memory or successful final action does not by itself establish that a reusable capability was acquired; evaluation must separate capability change from carried-over resources, unequal source exposure, and early execution failures.1
• We construct MC-EvoBench as a controlled Minecraft benchmark with a fixed catalog of 19 registered evolution chains, including 12 primary evaluation chains and seven calibration or probe chains, together with three graph-aligned paired probe families for transfer.1
• The benchmark represents each task as a task-state graph, defines stage-level environment verifiers, assigns matched source and target roles, resets the target world, and permits only a declared agent artifact to cross the source-target boundary.1
• MC-EvoBench thus offers a controlled testbed for durable, transferable embodied capabilities.1

Sources (1)

• [1]MC-EvoBench: Auditing Experience-Driven Self-Improvement in Minecraft Agents
  arXiv (AI, ML, NLP, CV, robotics, multi-agent) · Oct 9, 04:37 PM

  A central challenge in embodied recursive self-improvement is enabling agents to convert interaction history into capabilities that persist beyond one world and transfer to later tasks.

  A saved memory or successful final action does not by itself establish that a reusable capability was acquired; evaluation must separate capability change from carried-over resources, unequal source exposure, and early execution failures.

Extractive summary: sentences quoted from the sources.