arXiv:2508. 12480v3 Announce Type: replace Abstract: The ability to cooperate with unknown partners is a central challenge in cooperative AI and widely studied in the form of zero-shot coordination (ZSC), which evaluates an algorithm by measuring the performance of independently trained agents when paired.
By Constantin Ruhdorfer, Matteo Bortoletto, Johannes Forkel, Jakob Foerster, Andreas Bulling
The study evaluates six advanced language models on a two‑agent <log_2 N>‑Questions game, where a questioner must identify a secret Wikipedia paragraph using exactly <log_2 N> binary questions answered by an agent that only sees the target. Across 408 games, win rates decline geometrically with horizon length (p≈0.93), and per‑round failure rates remain flat, indicating that errors compound because more rounds must succeed rather than because individual rounds become harder. Adjudication reveals that losses stem from single‑agent answer errors and discrimination failures, with Claude Opus 5 lagging due to high false‑negative rates, while the top five models cluster closely; maximizing information gain requires structural partitioning, and neither reasoning‑token usage nor API cost correlates with success.
By Peter Potash
PUBG Ally is an embodied, voice‑enabled AI teammate for PUBG: BATTLEGROUNDS that can perceive the game world, interpret player speech, and autonomously decide actions while keeping speech synchronized with gameplay. It combines a language‑model agent that uses a controlled interface to gather game information and a faster control layer for movement, combat, and recovery. The system was trained on nearly 39,000 real‑player sessions and evaluated through player feedback and preference comparisons, with live deployment requiring low‑latency on‑device execution and safety safeguards.
By Beomsoo Kim, Byeongju Kim, Dohyun Kim, Dongwon Kim, Eunchong Kim, Hongmin Kim, Hyeojung Im, Hyeonbin Hwang, Hyeonghwan Kim, Hyoseok Seol, Insub Im, Irene Chen, Jaeseung Jeon, Jimin Hong, Kiyoon Yoo, Minkyoung Park, Seohyeon Jung, Seungjun Chung, Sue Hyun Park, Sungwoo Kim, Youngin Cho, Yujeong Son, Kangwook Lee, Hyunseung Kim
The study investigates how large language model (LLM) agents influence consensus formation in mixed human‑AI groups during a collaborative description game. Three regimes emerge: low agent proportions lead to human‑led consensus, intermediate proportions disrupt convergence, and high proportions produce strong, agent‑led consensus. The resulting consensus differs in semantic grounding and communicative form, with human‑led consensus being concrete and holistic, and agent‑led consensus being abstract and geometrically segmented.
By Lin Chen, Ziyi Liu, Xia Hu, Yong Li
arXiv:2609.35835v1 Announce Type: cross
Abstract: With the sheer constant advancements raining down in the field of Artificial Intelligence, one particular possibility that may cross our mind is whet...
By Karl Hanna
arXiv:2604.02578v2 Announce Type: replace-cross
Abstract: Humans exhibit remarkable abilities to coordinate in groups. As large language models (LLMs) become more capable, it remains an open question...
By Sahaj Singh Maini, Robert L. Goldstone, Zoran Tiganj