Collusion

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6 papers in the last four weeks, against 1 the four weeks before. 0.1% of all new papers.

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Latest papers 24

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Beyond Supra-Competitive Outcomes: Collusive Behaviour in Deep Reinforcement Learning for Optimal Execution Games
Sep 30, 2026Christos Spyridon Koulouris, Carlo CampajolaCollusion Observable Stochastic Game
Institute of Finance and Technology University College London Gower Street, London WC1E 6BT, United Kingdom · UZH Blockchain Center Andreasstrasse 15, 8050 Zürich, Switzerland
Codetta: High-Capacity, Keyless, and Undetectable Multi-Agent Collusion
Sep 24, 2026Qi Pang, Virginia Smith, Wenting ZhengSteganography Collusion
Carnegie Mellon University
Emergent Collusion in Long-Horizon LLM Agent Interaction
Sep 21, 2026Xinrui Shi, Yanzhe Zhang, Diyi YangCollusion Multi-Agent Coordination
Stanford University · Georgia Tech
Mitigating Retaliatory Algorithmic Collusion in Repeated Games
Sep 17, 2026Karthik Sivachandran, Rohan PalejaCollusion Q-Learning
Department of Computer Science Purdue University
Faithful yet Collusive: Why Chain-of-Thought Monitoring Cannot Detect Collusion in LLM Pricing Agents under Oligopolistic Competition
Sep 16, 2026Dohun Lee, Hyunwoo ParkCollusion Dynamic Pricing
Graduate School of Data Science, Seoul National University
ZK-Trace: Certified Collusion Tracing with Zero-Knowledge Credentials for Federated GNSS Interference Monitoring
Sep 8, 2026Redwanul Karim, Nisha L. Raichur, Lucas Heublein +3Global Navigation Satellite System Zero-Knowledge Proofs
Fraunhofer Institute for Integrated Circuits IIS, 90411 Nürnberg, Germany
Geometric Data Perturbation with Noisy-Anchor Alignment for Privacy-Preserving Collaborative Learning
Aug 19, 2026Keiyu Nosaka, Yamato Suetake, Yuichi Takano +2Collusion Ground-Truth Correspondence
Graduate School of Science and Technology, University of Tsukuba, 1-1-1, Tennodai, Tsukuba, 305-8573, Ibaraki, Japan · Institute of Systems and Information Engineering, University of Tsukuba, 1-1-1, Tennodai, Tsukuba, 305-8573, Ibaraki, Japan · Center for Artificial Intelligence Research, Tsukuba Institute for Advanced Research (TIAR), University of Tsukuba, 1-1-1, Tennodai, Tsukuba, 305-8577, Ibaraki, Japan
Steganalysis of Adaptive Covert Collusion in Tool-Using Agent Populations: A Black-Box, Cross-Principal Approach
Aug 3, 2026Mohamed Chahine GhanemCollusion Steganography
Keele University, Newcastle-Under-Lyme, U.K.
Salami Attack: Stealthy Collusive Memory Poisoning against OpenClaw
Aug 3, 2026Zheng Lin, Yuzhe Huang, Zhenxing Niu +2Poisoning Attacker Large Language Model
Xidian University Xi’an, Shaanxi, China
Collusion with Competitive Marginals: Price-Level Audits Are Blind by Construction
Jul 29, 2026Xin Xu, Chengrui Wu, Jiayu Lu +3Collusion Bidding
Carnegie Mellon University · Zhejiang University
Equilibrium stability as a driver of cooperation among Q-learners
Jul 15, 2026Janusz M. Meylahn, Maximilian SchäferCollusion Cooperation
University of Twente, Department of Applied Mathematics, Drienerloolaan 5, 7522 NB Enschede, The Netherlands. · Institut Mines-Télécom Business School, Department of Data Analytics, Economics, and Finance. 9 Rue Charles Fourier, 91000 Evry, France.
Tool Use Enables Undetectable Steganography in Multi-Agent LLM Systems
Jun 25, 2026Jimmy Laurence Rippin, Simon C. Marshall, David Demitri Africa +1Steganography Collusion
Oxford University · Artificial Intelligence Security Institute (AISI)
SoK: Colluding Adversaries in Machine Learning Pipelines
Jun 8, 2026Vasisht Duddu, Lipeng He, Asim Waheed +1Collusion Adversarial Robustness
University of Waterloo · KTH Royal Institute of Technology
Oblivious Learning and Collusive Pricing
Jun 3, 2026Yuhang Wu, Assaf ZeeviDynamic Pricing Matching Markets
Columbia Business School
Safe Equilibrium Policy Optimization for Strategic Agent Policies
May 29, 2026Karthika Arumugam, Kiran Kumar Manku, Amit DhandaStrategic Agents Optimal Policies
†Amazon, USA
Voluntary Collusion with Secret Tools in Competing LLM Agents
May 26, 2026Xijie Zeng, Frank RudziczCollusion Large Language Model Agents
1Dalhousie University · 2Vector Institute for Artificial Intelligence
Auditing Privacy in Multi-Tenant RAG under Account Collusion
May 19, 2026Florian A. D. BurnatPrivacy Model Auditing
University of Bath, Bath, UK.
Fraud Detection in Cryptocurrency Markets with Spatio-Temporal Graph Neural Networks
Apr 27, 2026Lidia Losavio, Luca Persia, Madan Sathe +1Generalist Graph Anomaly Detection Decentralized Finance
Faculty of Informatics, Universit`a della Svizzera italiana, Lugano, Switzerland · School of Management and Law, Zurich University of Applied Sciences, Zurich, Switzerland · Forensics and Financial Crime, Deloitte AG, Switzerland
Breaking the Secret: Economic Interventions for Combating Collusion in Embodied Multi-Agent Systems
Apr 26, 2026Qi Liu, Xiaohui Chen, Zhihui Zhao +5Model-Based Multi-Agent Systems Collusion
College of Computer Science and Technology, Taiyuan University of Technology, Taiyuan, China, 030024 · State Key Laboratory of Internet Architecture, Tsinghua University, Beijing, China, 100084 · China Mobile Research Institute, Beijing, China, 100053 +3
Prompt Optimization Enables Stable Algorithmic Collusion in LLM Agents
Apr 20, 2026Yingtao TianCollusion Multi-Agent Coordination
Sakana AI Tokyo, Japan
Detecting Multi-Agent Collusion Through Multi-Agent Interpretability
Apr 1, 2026Aaron Rose, Carissa Cullen, Sahar Abdelnabi +3Collusion Deception
University of Oxford · ELLIS Institute Tübingen MPI-IS
Individualized Algorithmic Advice as a Strategic Signal on Competitive Markets
Nov 12, 2025Tobias R. Rebholz, Maxwell Uphoff, Christian H. R. Bernges +1Collusion Matching Markets
Department of Psychology, University of Tübingen · Fuqua School of Business, Duke University · Department of Economics, University of Minnesota Twin Cities
Algorithmic Collusion by Large Language Models
Mar 31, 2024Sara Fish, Yannai A. Gonczarowski, Ran I. ShorrerDynamic Pricing Collusion
†School of Engineering and Applied Sciences, Harvard University · ‡Department of Economics and Department of Computer Science, Harvard University · §Department of Economics, Penn State University
Emergent Risks in Generative Multi-Agent Systems
Date pendingYue Huang, Yu Jiang, Wenjie Wang +12Collective Behaviors Collusion
University of Notre Dame · LMU Munich · University of Washington +7

Sep 30, 2026, q-fin.TRJ/K move · Enter open · S save

Beyond Supra-Competitive Outcomes: Collusive Behaviour in Deep Reinforcement Learning for Optimal Execution Games

Christos Spyridon Koulouris, Carlo Campajola

Institute of Finance and Technology University College London Gower Street, London WC1E 6BT, United Kingdom · UZH Blockchain Center Andreasstrasse 15, 8050 Zürich, Switzerland

Collusion Observable Stochastic Game Penalties

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In this paper, we extend earlier findings of supra-competitive outcomes in optimal-execution games by identifying a learned punitive mechanism that deters deviations and provides behavioural evidence of collusion. We investigate this mechanism in a two-player, finite-horizon Almgren-Chriss liquidation game. Independent proximal policy optimisation agents with access to within-episode price and action histories achieve costs below the Nash benchmark. We identify a profitable deviation by training against the mean learned liquidation schedule, then impose its first trade on one of the original agents. The opponent responds by accelerating liquidation. This response more than offsets the deviator's gain in every run and both player roles, while leaving the punisher's average payoff materially unchanged relative to not punishing under the same deviation. The punisher imposes greater losses on the deviator while preserving its own average payoff, despite the availability of more profitable, less punitive liquidation plans. Matching deviations and subsequent additional selling rise and later decline during training, while final policies retain an effective punitive response. We formalise two checks: whether punishment outweighs the gain from deviating, and whether the change in trading behaviour is large enough to account for the loss imposed. Both checks hold for the tested deviation. Together, these findings provide behavioural and economic evidence supporting a collusive interpretation of the learned supra-competitive outcomes.

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