Learning to Interrupt in Language-based Multi-agent Communication
arXiv:2604.06452v1 Announce Type: new Abstract: Multi-agent systems using large language models (LLMs) have demonstrated impressive capabilities across various domains. However, current agent communication suffers from verbose output that overload context and increase computational costs. Although existing approaches focus on compressing the message from the speaker side, they struggle to adapt to different listeners and identify relevant information. An effective way in human communication is to allow the listener to interrupt and express their opinion or ask for clarification. Motivated by this, we propose an interruptible communication framework that allows the agent who is listening to interrupt the current speaker. Through prompting experiments, we find that current LLMs are often overconfident and interrupt before receiving enough information. Therefore, we propose a learning method that predicts the appropriate interruption points based on the estimated future reward and cost.
arXiv:2604.06452v1 Announce Type: new Abstract: Multi-agent systems using large language models (LLMs) have demonstrated impressive capabilities across various domains. However, current agent communication suffers from verbose output that overload context and increase computational costs. Although existing approaches focus on compressing the message from the speaker side, they struggle to adapt to different listeners and identify relevant information. An effective way in human communication is to allow the listener to interrupt and express their opinion or ask for clarification. Motivated by this, we propose an interruptible communication framework that allows the agent who is listening to interrupt the current speaker. Through prompting experiments, we find that current LLMs are often overconfident and interrupt before receiving enough information. Therefore, we propose a learning method that predicts the appropriate interruption points based on the estimated future reward and cost. We evaluate our framework across various multi-agent scenarios, including 2-agent text pictionary games, 3-agent meeting scheduling, and 3-agent debate. The results of the experiment show that our HANDRAISER can reduce the communication cost by 32.2% compared to the baseline with comparable or superior task performance. This learned interruption behavior can also be generalized to different agents and tasks.
Executive Summary
This article introduces HANDRAISER, a novel interruptible communication framework for multi-agent systems leveraging large language models (LLMs). Addressing the verbosity and computational overhead of current LLM-based agent communication, the authors propose enabling listeners to interrupt speakers, akin to human interaction. Initial experiments reveal LLMs' overconfidence in interruption timing, leading to a learned method that predicts optimal interruption points based on estimated future reward and cost. Evaluated across diverse multi-agent scenarios (pictionary, meeting scheduling, debate), HANDRAISER significantly reduces communication costs by 32.2% while maintaining or improving task performance. The framework demonstrates generalizability across agents and tasks, offering a promising direction for more efficient and context-aware multi-agent communication.
Key Points
- ▸ Current LLM-based multi-agent communication suffers from excessive verbosity, increasing context load and computational costs.
- ▸ The proposed HANDRAISER framework enables listening agents to interrupt speaking agents, mirroring human communication dynamics.
- ▸ Initial LLM behavior shows overconfidence in interruption, necessitating a learned approach to predict optimal interruption points based on future reward/cost.
- ▸ HANDRAISER achieves a 32.2% reduction in communication cost while maintaining or improving task performance across various multi-agent scenarios.
- ▸ The learned interruption behavior demonstrates generalizability to different agents and tasks, suggesting robust applicability.
Merits
Novelty of Approach
The concept of listener-initiated interruption in LLM-based multi-agent systems is a significant and intuitive departure from speaker-side message compression, drawing inspiration from effective human communication.
Empirical Validation and Cost Reduction
The reported 32.2% reduction in communication cost is substantial and directly addresses a critical practical limitation of LLM deployments, without sacrificing task performance.
Addressing LLM Limitations
The identification and subsequent learning-based mitigation of LLMs' 'overconfident' interruption behavior demonstrates a nuanced understanding of current model deficiencies and a practical solution.
Generalizability
Demonstrating that the learned interruption behavior generalizes across different agents and tasks speaks to the robustness and potential broad applicability of the HANDRAISER framework.
Demerits
Limited Scope of 'Reward' and 'Cost'
The article's abstract does not fully delineate the precise metrics or philosophical underpinnings of 'estimated future reward and cost,' which could be complex and context-dependent in real-world applications.
Potential for Misinterpretation/Misuse
An agent's 'overconfidence' in interrupting, if not perfectly calibrated, could lead to premature interruptions that disrupt coherence or miss crucial information, potentially degrading nuanced task performance in highly complex scenarios.
Scalability to Larger Agent Systems
While evaluated for 2-3 agent systems, the complexities of managing interruptible communication in larger, more dynamic multi-agent environments with varying roles and information asymmetries remain to be fully explored.
Expert Commentary
The HANDRAISER framework represents a significant conceptual leap in multi-agent communication, moving beyond mere message compression to embrace the dynamic, interactive nature of human discourse. The insight that LLMs are 'overconfident' in their initial interruption attempts is particularly astute, highlighting a fundamental difference between statistical language generation and pragmatic conversational intelligence. By integrating a learning mechanism based on reward/cost estimation, the authors effectively bridge this gap, demonstrating a sophisticated understanding of how to imbue agents with more nuanced social intelligence. While the abstract tantalizes with promises of generalizability, the true test will lie in the robustness of 'reward' and 'cost' definitions across highly diverse, real-world tasks and the potential for unintended strategic interruptions. This work strongly hints at a future where AI communication is not just efficient, but also adaptively intelligent and contextually aware, pushing the boundaries of what constitutes 'natural' interaction with machines.
Recommendations
- ✓ Future research should rigorously define and explore the various dimensions of 'reward' and 'cost' in different task contexts, including socio-linguistic and ethical considerations.
- ✓ Investigate the scalability of HANDRAISER to larger multi-agent systems and more complex, hierarchical communication structures.
- ✓ Conduct user studies to evaluate human perception and trust in interruptible AI agents, especially in high-stakes environments.
- ✓ Explore the theoretical underpinnings of optimal interruption timing using game theory or formal pragmatics to provide a more robust analytical framework.
Sources
Original: arXiv - cs.CL