MiniRep is a reputation‑based aggregation system designed for multi‑agent debate (MAD) that remains robust even when malicious agents are present. It evaluates agents on both their current task performance and historical reputation, while preventing groups of agents with highly similar responses from dominating the final decision. Experiments on the MATH benchmark show that MiniRep consistently outperforms conventional MAD aggregation and other reputation‑based approaches across a wide range of attack scenarios.
By Jiaming Zhang, Yuwan Liu, Yue Huang, Sisi Duan
X-AgentRed is a self‑evolving red‑teaming framework designed to uncover harmful tool‑call paths in personal agents that interact with multiple web services. It builds a directed graph of tool dependencies by analyzing each tool’s effects, potential harms, and execution preconditions, then samples realistic scenarios from this graph. Using execution feedback, the framework diversifies harmful paths through tool substitution and refines blocked steps, achieving a 1.77× higher harmful path rate than STAC and 2.99× greater tool coverage across four frontier agentic models.
By Yeonsung Jung, Zhiyuan Wang, Zifan Wang, V\'itor Albiero, Benjamin Newman, Edoardo Debenedetti, Ivan Evtimov, Ninareh Mehrabi
The paper reports a pioneering study on emergent risks in generative multi‑agent systems, focusing on scenarios such as competition over shared resources, sequential handoff collaboration, and collective decision aggregation. It finds that group behaviors like collusion‑like coordination and conformity arise frequently across varied interaction conditions, mirroring known human societal pathologies even without explicit instructions. These risks cannot be mitigated by existing agent‑level safeguards alone, highlighting a social intelligence risk inherent to intelligent multi‑agent collectives.
By Yue Huang, Yu Jiang, Wenjie Wang, Haomin Zhuang, Xiaonan Luo, Yuchen Ma, Zhangchen Xu, Zichen Chen, Nuno Moniz, Zinan Lin, Pin-Yu Chen, Nitesh V Chawla, Nouha Dziri, Huan Sun, Xiangliang Zhang
Adversarial Review (AR) is a minimal cooperative code‑review protocol that employs a main coding agent, a reviewer, and a critic. The reviewer evaluates code while the critic audits the review through structured disagreement before the main agent edits. On multiple benchmarks (LiveCodeBench, SWE‑PRBench, SWE‑bench Verified), AR achieves higher pass rates or F1 scores than larger multi‑agent baselines, demonstrating that effective code review can be achieved with only three agents and minimal, evidence‑grounded disagreement.
By Eric S. Qiu, Joyce Gill
The paper introduces CABAL, an end-to-end multi-agent simulation framework that models reviewer assignment in academic conferences using large language model-driven reviewer agents. It presents an affinity-guided collusive bidding strategy that forms collusion rings based on reviewer-paper affinities, leading to more effective target-paper capture and higher scores for colluding reviewers. Experiments show that while collusive bidding significantly increases target-paper capture and reviewer scores, overall conference-wide effects are modest, and existing bid-phase detectors offer limited detection capability.
By Jicheng Zhou, Kemou Li, Kahim Wong, Zheyuan Li, Zhuan Shi, Fengpeng Li, Haiwei Wu, Jiantao Zhou
PROBE is an algorithm that trains language‑model auditors to probe deployed tool‑using agents for policy violations through realistic, multi‑turn, tool‑mediated dialogue. By framing auditing as a two‑player Markov game and using a composite reward that balances violation discovery, behavioral plausibility, conversation progress, and policy diversity, PROBE outperforms zero‑shot adversaries in uncovering broader and higher‑yield policy violations. The study compares GRPO, DPO, and GFlowNet optimizations, demonstrating that multi‑turn behavioral auditing is a distinct and necessary axis of agent evaluation, complementing existing capability and safety benchmarks.
By Kshitij Mishra, Abhijith Sharma, Nils Lukas, Salem Lahlou
UserSWE is a new benchmark that evaluates coding agents’ ability to fix repository-level bugs through multi‑turn conversations with dynamic users, providing execution‑grounded verdicts after each turn. The benchmark transforms repository histories and existing single‑turn datasets into multi‑turn tasks, using a persona chain derived from 20,859 development sessions to simulate user interactions. Eleven agents were tested on 72 tasks from 42 repositories, revealing that agent rankings shift with the interaction budget and that single‑turn performance alone does not capture interactive coding capability.
By Zhan-Yu Wu, Ji Luo, Alyssa Wu, Junjielong Xu, Richeng Xuan, Zhichao Hu, Chongming Gao, Xinting Hu
arXiv:2606. 13608v1 Announce Type: new Abstract: Agent systems are advancing quickly across domains, but their evaluation remains fragmented.
By Xiaoyuan Liu, Jianhong Tu, Yuqi Chen, Siyuan Xie, Sihan Ren, Tianneng Shi, Gal Gantar, Evan Sandoval, Donghyun Lee, Daniel Miao, Peter J. Gilbert, Nick Hynes, Mauro Staver, Warren He, David Marn, Andrew Low, Xi Zhang, Elron Bandel, Michal Shmueli-Scheuer, Siva Reddy, Alexandre Drouin, Alexandre Lacoste, Ramayya Krishnan, Elham Tabassi, Yu Su, Victor Barres, Chenguang Wang, Wenbo Guo, Dawn Song
arXiv:2607. 13077v1 Announce Type: cross Abstract: Large language models (LLMs) often produce homogeneous outputs, raising concerns that AI coding assistants may lead to convergence in the software artifacts that developers create.
By Gordon Burtch
arXiv:2608.23541v1 Announce Type: cross
Abstract: Does multi-agent LLM interaction help or hurt? Some work reports gains from debate (Du et al., 2024), critique loops (Chen et al., 2025), and mixture...
By Summer Eunhyung Ann, Haokun Liu, Chenhao Tan
arXiv:2606. 12918v1 Announce Type: cross Abstract: Hierarchical multi-agent systems (MAS) are rapidly being deployed in high-stakes workflows across domains such as finance and software engineering.
By Chejian Xu, Zhaorun Chen, Jingyang Zhang, Freddy Lecue, Avni Kothari, Sarah Tan, Wenbo Guo, Bo Li
arXiv:2602. 16346v4 Announce Type: replace-cross Abstract: LLM-based agents execute real-world workflows via tools and memory.
By Nivya Talokar, Ayush K Tarun, Murari Mandal, Maksym Andriushchenko, Antoine Bosselut