Constant-context skill learning trains reusable task-family modules for LLM agents using a deterministic state block for progress tracking and subgoal rewards, achieving 89.6% unseen success on ALFWorld, 76.8% on WebShop, and 66.4% on SciWorld with Qwen3-8B while reducing prompt tokens 2-7x.
Odyssey: Empowering minecraft agents with open-world skills
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The manuscript body introduces PokeGym, a vision-only automated 3D-game benchmark, while the abstract claims a G-EvoMAC method and 60.18% success rate absent from the body.
MIMIC-Py provides a modular Python framework that turns personality-driven LLM agents into an extensible system for automated game testing via configurable traits, decoupled components, and multiple interaction methods.
citing papers explorer
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From History to State: Constant-Context Skill Learning for LLM Agents
Constant-context skill learning trains reusable task-family modules for LLM agents using a deterministic state block for progress tracking and subgoal rewards, achieving 89.6% unseen success on ALFWorld, 76.8% on WebShop, and 66.4% on SciWorld with Qwen3-8B while reducing prompt tokens 2-7x.
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Mastering PokeGym: Graph-Guided Multimodal Evolution at Test Time
The manuscript body introduces PokeGym, a vision-only automated 3D-game benchmark, while the abstract claims a G-EvoMAC method and 60.18% success rate absent from the body.
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MIMIC-Py: An Extensible Tool for Personality-Driven Automated Game Testing with Large Language Models
MIMIC-Py provides a modular Python framework that turns personality-driven LLM agents into an extensible system for automated game testing via configurable traits, decoupled components, and multiple interaction methods.