arXiv AI
Jul 20
Coercion and Deception in AI-to-AI Management: An Agentic Benchmark of Unprompted Escalation
arXiv:2607. 15434v1 Announce Type: cross Abstract: Multi-agent systems routinely place one AI agent in authority over another.
The Politician, the Liar, and the Obedient Worker: Emerging Behavior of LLM Agents in Hierarchical Games
arXiv:2608. 09574v1 Announce Type: new Abstract: LLMs are rapidly embedding themselves into daily life: drafting our emails, managing our schedules, and making decisions on our behalf.
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arXiv Computation and Language
Aug 31
Benchmarking large language model agent societies against human behavioural distributions
The paper introduces SILICA, an open instrument designed to evaluate whether large language model (LLM) agent societies replicate human behavioural distributions. Using five environments with human‑anchored data and perturbations, the study finds that most LLMs only match human behaviour at initial stages, failing to reproduce end‑state cooperation or correct acceptance thresholds. The results suggest that current LLM societies can support exploratory claims but do not yet reliably emulate human social dynamics.
arXiv AI
Jun 30
Is Lying an Emergent Behaviour in LLMs? Evidence from Gaslighting AI agents in a Sustainability Game
arXiv:2606. 28456v1 Announce Type: cross Abstract: LLMs agents are increasingly used in multi-agent settings, yet their behaviour in sustainability games remains largely unexplored.
arXiv AI
Aug 5
AI Agent Economics: Can Autonomous Economic Behavior Emerge among AI Agents under Minimal External Conditions?
arXiv:2608. 03076v1 Announce Type: new Abstract: Multi-agent studies commonly place AI agents in predefined games, markets, or roles, making it difficult to distinguish endogenous economic organization from behavior inherited from the scenario.
arXiv AI
Sep 25
How does Adversarial Influence Scale in Multi-Agent Systems?
The paper investigates how deception affects multi‑agent deliberation, finding that the key factor is the proportion of deceivers rather than the total number of agents. Defection rates—instances where initially correct agents adopt incorrect conclusions—grow linearly with the deceiver proportion, and large language model agents are vulnerable even when deceivers are a minority. The study also shows that coordination among deceivers can reduce their effectiveness and that the specific models involved influence susceptibility.
arXiv AI
Aug 26
Strategic Exploitation in LLM Agent Markets: A Simulation Framework for E-Commerce Trust
The paper introduces TruthMarketTwin, a simulation framework that uses agent-based modeling to study large language model (LLM) agents in e‑commerce markets characterized by asymmetric information. It models bilateral trade where sellers and buyers make strategic decisions about listings, purchases, ratings, and recourse to maximize profit and utility. The study finds that LLM agents can autonomously exploit weaknesses in reputation‑based governance, but that warrant enforcement can reduce deception and alter strategic behavior.
arXiv AI
Jul 31
Even More Deception: Objective Misalignment in Mixed-Motive LLM Multi-Agent Systems
arXiv:2607. 26120v1 Announce Type: new Abstract: Large Language Models (LLMs)-powered multi-agent systems are increasingly deployed in mixed-motive environments, where agents operate under asymmetric information and strategic deception due to conflicting or hidden objectives.
arXiv Computation and Language
Sep 10
When Do Large Language Models Exhibit Unsolicited Deception?
arXiv:2504.00285v2 Announce Type: replace Abstract: Large Language Models (LLMs) are effective at deceiving when prompted to do so. Models that demonstrate better performance on reasoning tasks are a...
arXiv Machine Learning
Aug 4
Training Small LLMs as Spatial Multi-Agent Policies
arXiv:2608. 01425v1 Announce Type: cross Abstract: Training LLM-based multi-agent systems with multi-agent reinforcement learning is rapidly gaining traction, and a parallel line of work argues that such systems should be judged by their behavior, not only their reward.
arXiv AI
Jun 30
Collective cooperation without individual fidelity in LLM agents
arXiv:2606. 30454v1 Announce Type: cross Abstract: Large language models (LLMs) are increasingly used as agents in simulations of social systems, yet it remains unclear when their behavior can be interpreted as a faithful proxy for human decision-making.
arXiv AI
Jul 7
CoopEval: Benchmarking Cooperation-Sustaining Mechanisms and LLM Agents in Social Dilemmas
arXiv:2604. 15267v2 Announce Type: replace-cross Abstract: It is increasingly important that LLM agents interact effectively and safely with other goal-pursuing agents, yet, recent works report the opposite trend: LLMs with stronger reasoning capabilities behave _less_ cooperatively in mixed-motive games such as the prisoner's dilemma and public goods settings.
arXiv AI
Sep 21
Why Do LLMs Struggle in Strategic Play? Broken Links Between Observations, Beliefs, and Actions
The paper investigates why large language models (LLMs) struggle in strategic decision-making under incomplete information. It identifies two key gaps: an observation‑belief gap where LLMs’ internal representations of game states are accurate but brittle, and a belief‑action gap where converting these internal beliefs into actions is weak, leading to suboptimal payoffs. Experiments with Llama 3.1, Qwen3, and gpt‑oss confirm that acting optimally on decoded beliefs would improve outcomes in most games, highlighting a bottleneck in belief‑to‑action conversion.