@inproceedings{hua-etal-2026-ptcg,
title = "{PTCG}-Bench: Can {LLM} Agents Master Pok{\'e}mon Trading Card Game?",
author = "Hua, Dongdong and
Sun, Yifei and
Huang, Renhong and
Wang, Chunping and
Gao, Feng and
Yang, Yang",
editor = "Lee, Hung-Yi and
Dev, Sunipa and
Li, Jessy and
Elliott, Desmond",
booktitle = "Findings of the {A}ssociation for {C}omputational {L}inguistics: {EMNLP} 2026",
month = oct,
year = "2026",
address = "Budapest, Hungary",
publisher = "Association for Computational Linguistics",
url = "https://aclanthology.org/2026.findings-emnlp.1689/",
pages = "36084--36099",
ISBN = "979-8-89176-462-0",
abstract = "Given a strategically complex board game, human players can quickly learn to devise strategies after playing a few rounds. Autonomous agents require similar capabilities in realistic interactive environments, yet existing agent benchmarks often fail to fully capture such strategic and evolving decision-making scenarios. We present **PTCG-Bench**, a benchmark built on the Pok'{e}mon Trading Card Game (PTCG) that evaluates LLM agents at two complementary levels: (1) their decision-making performance within a single complex environment, and (2) their ability to self-evolving through accumulated experience. We further include a modular harness ablation to better interpret agent performance without conflating it with model capability. Our experiments show that, although LLM agents can achieve non-trivial gameplay performance, sustained and stable self-evolution remains challenging, and performance is sensitive to harness design. We hope that PTCG-Bench will facilitate future research on harness-aware and self-evolving agents in realistic interactive environments."
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<abstract>Given a strategically complex board game, human players can quickly learn to devise strategies after playing a few rounds. Autonomous agents require similar capabilities in realistic interactive environments, yet existing agent benchmarks often fail to fully capture such strategic and evolving decision-making scenarios. We present **PTCG-Bench**, a benchmark built on the Pok’emon Trading Card Game (PTCG) that evaluates LLM agents at two complementary levels: (1) their decision-making performance within a single complex environment, and (2) their ability to self-evolving through accumulated experience. We further include a modular harness ablation to better interpret agent performance without conflating it with model capability. Our experiments show that, although LLM agents can achieve non-trivial gameplay performance, sustained and stable self-evolution remains challenging, and performance is sensitive to harness design. We hope that PTCG-Bench will facilitate future research on harness-aware and self-evolving agents in realistic interactive environments.</abstract>
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%0 Conference Proceedings
%T PTCG-Bench: Can LLM Agents Master Pokémon Trading Card Game?
%A Hua, Dongdong
%A Sun, Yifei
%A Huang, Renhong
%A Wang, Chunping
%A Gao, Feng
%A Yang, Yang
%Y Lee, Hung-Yi
%Y Dev, Sunipa
%Y Li, Jessy
%Y Elliott, Desmond
%S Findings of the Association for Computational Linguistics: EMNLP 2026
%D 2026
%8 October
%I Association for Computational Linguistics
%C Budapest, Hungary
%@ 979-8-89176-462-0
%F hua-etal-2026-ptcg
%X Given a strategically complex board game, human players can quickly learn to devise strategies after playing a few rounds. Autonomous agents require similar capabilities in realistic interactive environments, yet existing agent benchmarks often fail to fully capture such strategic and evolving decision-making scenarios. We present **PTCG-Bench**, a benchmark built on the Pok’emon Trading Card Game (PTCG) that evaluates LLM agents at two complementary levels: (1) their decision-making performance within a single complex environment, and (2) their ability to self-evolving through accumulated experience. We further include a modular harness ablation to better interpret agent performance without conflating it with model capability. Our experiments show that, although LLM agents can achieve non-trivial gameplay performance, sustained and stable self-evolution remains challenging, and performance is sensitive to harness design. We hope that PTCG-Bench will facilitate future research on harness-aware and self-evolving agents in realistic interactive environments.
%U https://aclanthology.org/2026.findings-emnlp.1689/
%P 36084-36099
Markdown (Informal)
[PTCG-Bench: Can LLM Agents Master Pokémon Trading Card Game?](https://aclanthology.org/2026.findings-emnlp.1689/) (Hua et al., Findings 2026)
ACL
- Dongdong Hua, Yifei Sun, Renhong Huang, Chunping Wang, Feng Gao, and Yang Yang. 2026. PTCG-Bench: Can LLM Agents Master Pokémon Trading Card Game?. In Findings of the Association for Computational Linguistics: EMNLP 2026, pages 36084–36099, Budapest, Hungary. Association for Computational Linguistics.