arXiv:2606. 11284v1 Announce Type: cross Abstract: Real-world multi-agent systems, from traffic coordination to resource allocation, are often modeled as general-sum games where individual incentives conflict with collective welfare.
By Wongyu Lee, Francesco Lelli, Omran Ayoub, Massimo Tornatore
The paper introduces a game-theoretic framework for coordinating multi-agent large language model (LLM) systems, modeling orchestrator–worker interactions as a bilevel coordination game. It analyzes textual reflection as stochastic movement over semantic memory states, providing finite-time bounds, lower bounds, and an information-theoretic impossibility result. Building on these insights, the authors propose Stochastic Reflective Memory Ascent (SRMA), a grounded evaluation mechanism that guarantees convergence under certain conditions and demonstrates improved performance on 500 SWE‑bench instances.
By Yihang Chen, Yuxiang Chen, Yuxuan Huang, Meng Fang, Weilin Luo, Jun Wang
arXiv:2306. 02704v2 Announce Type: replace-cross Abstract: We introduce \emph{Calibrated Stackelberg Games (CSGs)}, a generalization of the standard Stackelberg Games (SGs) framework.
By Nika Haghtalab, Chara Podimata, Kunhe Yang
arXiv:2606. 01561v1 Announce Type: new Abstract: Aligning Large Language Models (LLMs) with human preferences is often formulated via Direct Preference Optimization (DPO).
By Xiwen Chen, Wenhui Zhu, Jingjing Wang, Peijie Qiu, Zhipeng Wang, Huayu Li, ZhengXiao He, Xuanzhao Dong, Prayag Tiwari, Mingkun Xu, Yujian Xiong, Feng Luo, Abolfazl Razi, Brendan Hogan Rappazzo, Anderson Schneider, Yuriy Nevmyvaka
arXiv:2607. 04470v1 Announce Type: cross Abstract: Large Language Models (LLMs) offer a natural interface for translating human objectives into reward signals for cooperative multi-agent reinforcement learning (MARL), yet the training-time dynamics of this integration remain poorly understood.
By Faid Keddouri, Sohaib Houhou, Aissa Boulmerka, Nadir Farhi
arXiv:2608. 06741v1 Announce Type: new Abstract: Reasoning in large language models (LLMs) is often grounded in human text, human demonstrations, and human-generated rationales.
By Han Wang, Philippe Beardsell, Boning Li, Aaron Sasmita, Shuai Li, Hongyuan Zha, Baoxiang Wang