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

Paper recorded by Signals 4 on 2026-10-09 in cs.AI. Abstract reproduced from arXiv; link to the original below.

Published 2026-10-09 on arXiv · recorded by Signals 4 on 2026-10-12

Category: cs.AI · 人工智能 · first seen 2026-10-12

Abstract

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 e

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#349 most recent of 960 cs.AI papers we have recorded · ↑ newer: SpatialASM: Internalizing Active Mentalization for Spatial Reasoning · ↓ older: Same Query, Different Answer: Run-to-Run Variance in Semantic Query Pr
Cite this page: MC-EvoBench: Auditing Experience-Driven Self-Improvement in Minecraft Agents: the #349 most recent of 960 cs.AI papers we have recorded (as of 2026-10-09). Source: Signals 4 (Signals API) — https://data.jiangzhang.ca/signals4/t/papers/mc-evobench-auditing-experience-driven-self-improvement-in-minecraft-agents.html
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