Flout at Your Own Risk: LLMs Struggle with Pragmatic Cooperativity Under Epistemic Asymmetry
- URL: http://arxiv.org/abs/2607.11053v1
- Date: Mon, 13 Jul 2026 03:31:56 GMT
- Title: Flout at Your Own Risk: LLMs Struggle with Pragmatic Cooperativity Under Epistemic Asymmetry
- Abstract summary: We investigate the pragmatic reasoning capabilities of LLMs in a multi-party collaborative task under partial information conditions.<n>We empirically assess various LLMs' abilities to act cooperatively as both speakers and listeners, including both prompting and post-training strategies.<n>Our results show that while LLMs exhibit certain pragmatic capabilities in collaborative settings, and these can be elicited through prompting and post-training, they still face challenges in pragmatic communication with incomplete information.
- Score: 10.532912977406811
- License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
- Abstract: Fruitful collaborations rely on cooperative communications, including of contextual cues to incorporate into reasoning. The increasing use of LLMs in collaborative and agentic pipelines raises questions about the extent to which they exhibit these pragmatic capabilities, especially in scenarios where they may not have access to the same information as their collaborators. In this paper, we perform a novel investigation into the pragmatic reasoning capabilities of LLMs in a multi-party collaborative task under partial information conditions. We formalize a notion of collaborative epistemic asymmetry that explicitly connects objective task success to Grice's cooperative principle and empirically assess various LLMs' abilities to act cooperatively as both speakers and listeners, including both prompting and post-training strategies. Our results show that while LLMs exhibit certain pragmatic capabilities in collaborative settings, and these can be elicited through prompting and post-training, they still face challenges in pragmatic communication with incomplete information, and that certain failure modes do correlate with floutings of Grice's maxims that go unrecognized.
Related papers
- Task-Oriented Sensing and Covert Transmissions for Collaborative Multi-AUV Systems [67.63956000297922]
This paper proposes a Sensed Information Value Realization Multi-Agent Reinforcement Learning (SVR-MARL) framework to characterize the utility of information for cooperative tasks.<n>The potential of the proposed framework to improve collaborative task efficiency while reducing unnecessary communication and exposure risks is demonstrated.
arXiv Detail & Related papers (2026-07-15T14:29:25Z) - Evaluating Generalization Capabilities of LLM-Based Agents in Mixed-Motive Scenarios Using Concordia [100.74015791021044]
Large Language Model (LLM) agents have demonstrated impressive capabilities for social interaction.<n>Existing evaluation methods fail to measure how well these capabilities generalize to novel social situations.<n>We present empirical results from the NeurIPS 2024 Concordia Contest, where agents were evaluated on their ability to achieve mutual gains.
arXiv Detail & Related papers (2025-12-03T00:11:05Z) - Communication and Verification in LLM Agents towards Collaboration under Information Asymmetry [17.472005826931127]
This paper studies Large Language Model (LLM) agents in task collaboration.<n>We extend Einstein Puzzles, a symbolic puzzle, to a table-top game.<n> Empirical results highlight the critical importance of aligned communication.
arXiv Detail & Related papers (2025-10-29T15:03:53Z) - Learning "Partner-Aware" Collaborators in Multi-Party Collaboration [12.287537011305497]
Large Language Models (LLMs) are increasingly bring deployed in agentic settings where they act as collaborators with humans.<n>This paper builds on the AI alignment and safe interruptability literature to offer novel theoretical insights on collaborative behavior.<n>We propose Interruptible Collaborative Roleplayer (ICR)-a novel partner-aware learning algorithm to train CG-optimal collaborators.
arXiv Detail & Related papers (2025-10-26T00:05:48Z) - Let's Roleplay: Examining LLM Alignment in Collaborative Dialogues [11.888786446094057]
This paper examines how different alignment methods affect Large Language Models' effectiveness as partners in multiturn, multiparty collaborations.<n>Using a roleplay methodology, we evaluate interventions from differently-trained friction agents in collaborative task conversations.<n>Our results show that a friction-aware approach significantly outperforms common alignment baselines in helping both convergence to a common ground, or agreed-upon task-relevant propositions, and correctness of task outcomes.
arXiv Detail & Related papers (2025-09-07T00:58:10Z) - Corrupted by Reasoning: Reasoning Language Models Become Free-Riders in Public Goods Games [87.5673042805229]
How large language models balance self-interest and collective well-being is a critical challenge for ensuring alignment, robustness, and safe deployment.<n>We adapt a public goods game with institutional choice from behavioral economics, allowing us to observe how different LLMs navigate social dilemmas.<n>Surprisingly, we find that reasoning LLMs, such as the o1 series, struggle significantly with cooperation.
arXiv Detail & Related papers (2025-06-29T15:02:47Z) - Evaluating and Enhancing LLMs Agent based on Theory of Mind in Guandan: A Multi-Player Cooperative Game under Imperfect Information [36.11862095329315]
Large language models (LLMs) have shown success in handling simple games with imperfect information.
This study investigates the applicability of knowledge acquired by open-source and API-based LLMs to sophisticated text-based games.
arXiv Detail & Related papers (2024-08-05T15:36:46Z) - Cooperate or Collapse: Emergence of Sustainable Cooperation in a Society of LLM Agents [101.17919953243107]
GovSim is a generative simulation platform designed to study strategic interactions and cooperative decision-making in large language models (LLMs)<n>We find that all but the most powerful LLM agents fail to achieve a sustainable equilibrium in GovSim, with the highest survival rate below 54%.<n>We show that agents that leverage "Universalization"-based reasoning, a theory of moral thinking, are able to achieve significantly better sustainability.
arXiv Detail & Related papers (2024-04-25T15:59:16Z) - LLM-Coordination: Evaluating and Analyzing Multi-agent Coordination Abilities in Large Language Models [23.092480882456048]
Large Language Models (LLMs) have demonstrated emergent common-sense reasoning and Theory of Mind (ToM) capabilities.<n>This study introduces the LLM-Coordination Benchmark, a novel benchmark for analyzing LLMs in the context of Pure Coordination Settings.
arXiv Detail & Related papers (2023-10-05T21:18:15Z) - Exploring Collaboration Mechanisms for LLM Agents: A Social Psychology View [60.80731090755224]
This paper probes the collaboration mechanisms among contemporary NLP systems by practical experiments with theoretical insights.
We fabricate four unique societies' comprised of LLM agents, where each agent is characterized by a specific trait' (easy-going or overconfident) and engages in collaboration with a distinct thinking pattern' (debate or reflection)
Our results further illustrate that LLM agents manifest human-like social behaviors, such as conformity and consensus reaching, mirroring social psychology theories.
arXiv Detail & Related papers (2023-10-03T15:05:52Z) - Corex: Pushing the Boundaries of Complex Reasoning through Multi-Model Collaboration [83.4031923134958]
Corex is a suite of novel general-purpose strategies that transform Large Language Models into autonomous agents.
Inspired by human behaviors, Corex is constituted by diverse collaboration paradigms including Debate, Review, and Retrieve modes.
We demonstrate that orchestrating multiple LLMs to work in concert yields substantially better performance compared to existing methods.
arXiv Detail & Related papers (2023-09-30T07:11:39Z) - Building Cooperative Embodied Agents Modularly with Large Language
Models [104.57849816689559]
We address challenging multi-agent cooperation problems with decentralized control, raw sensory observations, costly communication, and multi-objective tasks instantiated in various embodied environments.
We harness the commonsense knowledge, reasoning ability, language comprehension, and text generation prowess of LLMs and seamlessly incorporate them into a cognitive-inspired modular framework.
Our experiments on C-WAH and TDW-MAT demonstrate that CoELA driven by GPT-4 can surpass strong planning-based methods and exhibit emergent effective communication.
arXiv Detail & Related papers (2023-07-05T17:59:27Z) - Examining Inter-Consistency of Large Language Models Collaboration: An
In-depth Analysis via Debate [41.949869545423375]
Large Language Models (LLMs) have shown impressive capabilities in various applications, but they still face various inconsistency issues.
To examine whether LLMs can collaborate effectively to achieve a consensus for a shared goal, we focus on commonsense reasoning.
Our work contributes to understanding the inter-consistency among LLMs and lays the foundation for developing future collaboration methods.
arXiv Detail & Related papers (2023-05-19T11:15:33Z) - The Machine Psychology of Cooperation: Can GPT models operationalise prompts for altruism, cooperation, competitiveness and selfishness in economic games? [0.0]
We investigated the capability of the GPT-3.5 large language model (LLM) to operationalize natural language descriptions of cooperative, competitive, altruistic, and self-interested behavior.
We used a prompt to describe the task environment using a similar protocol to that used in experimental psychology studies with human subjects.
Our results provide evidence that LLMs can, to some extent, translate natural language descriptions of different cooperative stances into corresponding descriptions of appropriate task behaviour.
arXiv Detail & Related papers (2023-05-13T17:23:16Z)
This list is automatically generated from the titles and abstracts of the papers in this site.
This site does not guarantee the quality of this site (including all information) and is not responsible for any consequences.