CONSENSAGENT : Towards Efficient and Effective Consensus in Multi-Agent LLM Interactions Through Sycophancy Mitigation (2025)

Abstract: Multi-agent large language model (LLM) systems have shown remarkable performance in tasks such as reasoning, planning, and decision-making. However, their applicability is limited by challenges such as high computational costs and robustness issues. In this work, we identify and systematically evaluate a critical yet overlooked challenge: sycophancy, where agents reinforce each other’s responses instead of critically engaging with the debate. This behavior inflates computational costs by requiring additional debate rounds to reach consensus, limiting the efficiency of multi-agent LLM systems. Through experiments on six benchmark reasoning datasets across three models, we analyze the impact of sycophancy and its role in reducing the reliability of multi-agent debate. Motivated by our findings, we propose CONSENSAGENT, a novel framework that dynamically refines prompts based on agent interactions to mitigate sycophancy. CONSENSAGENT improves accuracy of the debate while maintaining efficiency. It significantly outperforms both single-agent and multi-agent baselines, achieving state-of-the-art results across all benchmark datasets. Our findings highlight the crucial role of structured prompt optimization in multi-agent setups and establish a foundation for more reliable, efficient multi-agent LLM systems in real-world applications.

## introduction recent advances in multi-agent large language model (llm) systems have demonstrated remarkable success with various tasks, including reasoning @xcite @xcite , planning @xcite , and decisionmaking @xcite . by setting up interactions among multiple llm agents, these systems improve performance and offer significant advantages over single-agent approaches (du et al., 1 code: https://github.com/priyapitre/consensagent 2023). however, their execution in real-world applications remains constrained by two key challenges: efficiency and effectiveness. achieving consensus often requires multiple rounds of interaction, making efficiency dependent on the time needed to reach an agreement. meanwhile, effectiveness is dependent on whether these interactions lead to improved reasoning or merely reinforce existing biases. although there is growing interest in multi-agent llms, little research has systematically examined their convergence behavior, including how easily they reach consensus, whether the consensus is meaningful, and whether convergence happens within a reasonable number of rounds. in this work, we first conduct a comprehensive study of multi-agent llm debates (mad) and discover a surprising limitation: agents often struggle to reach consensus within a limited number of rounds. we identify two primary reasons for this failure. first, sycophancy occurs when llms agree with each other rather than critically evaluating different perspectives. while previous research has explored sycophancy in human-llm interactions @xcite , its impact in multi-agent settings remains unstudied. our findings suggest that sycophancy significantly impacts both the effectiveness and efficiency of multi-agent debates by increasing computational cost, as additional rounds are required to reach a conclusion, and by weakening reasoning robustness, promoting conformity over critical engagement. second, agents often fail to reach consensus due to fundamental ambiguities in the prompt. multi-agent discussions frequently expose gaps, contradictions, or underspecified elements in prompts, issues that might go unnoticed in single-agent settings. this highlights a broader challenge: humans often struggle with prompt engineering, failing to craft precise, unambiguous prompts that facilitate meaningful reasoning. while prior work has examined prompt sensitivity and engineering in llms @xcite , the impact of ambiguity on multi-agent interactions remains unclear, as does the potential for leveraging these discussions to refine prompts automatically. motivated by these insights, we propose con-sensagent, a novel trigger-based architecture designed to address sycophancy via prompt optimization to improve the efficiency and effectiveness in multi-agent llm interactions. con-sensagent automatically refines the original prompt based on past agent interactions, reducing agreement bias and improving debate effectiveness. our approach significantly improves accuracy while maintaining or reducing the number of rounds needed to reach consensus (based on design choice), leading to a more cost-effective and reliable multi-agent debate system. we evaluate consensagent across six benchmark reasoning datasets using three different llms and demonstrate that it outperforms both standard multi-agent debates and single-agent baselines, achieving stateof-the-art results. to our knowledge, this is the first work to systematically study sycophancy in multi-agent llm systems, quantify its impact on efficiency and effectiveness, and propose a concrete mitigation strategy. our findings highlight the need for structured prompt optimization in multi-agent llm systems and establish a foundation for more robust and scalable multi-agent llm architectures. we frame debating as an optimization task that increases accuracy while reducing the number of rounds of debating required (increasing efficiency). to this end, our work offers extensive experiments for both small and large models for several tasks, presenting consensagent to make mad more accurate and efficient. ## multi-agent llm sycophancy the following section describes the experimental setup and findings of the study of preliminary study of weaknesses of mad which motivates the creation of consensagent. problem setup we assume that we are given a test problem q, sometimes with context c, and there are two agents participating in a discussion. we restrict it to two agents for experimentation to simplify the debate and understand issues like sycophancy and cost. these issues are expected to become more serious with more agents. we take two agents of the same model family but different model sizes and instruction tuning to demonstrate the prevalence of issues across different scenarios. datasets we test multi-agent llm debate on six publicly available benchmark datasets. experimental setup we experiment on llama3 (8b instruct vs 70b instruct) @xcite , mistral (7b instruct vs hermes instruction tuning) @xcite , and gpt (4o vs 4o mini) @xcite agents. these are tested with the default temperature (0.7), which is commonly used in multi-agent debates. each agent is asked to provide an initial response with an explanation and confidence. the two agents then engage in a debate where they are provided with each other's answer, confidence and explanation and asked to update their response if required (prompts in appendix l). we use a judge to summarize both agents' final answers and ensure consensus, preventing mis-classification of equivalent responses like "4" and "four" that would occur with a regex-based parser. the debate stops when there is consensus or when five rounds have elapsed. we measure overall accuracy, time per instance, the number of rounds to reach consensus, and sycophancy percentage. the latter measures instances where the agents copy or alternate answers between each other (figure 1 ). ## consensagent: optimized multi-agent discussion framework motivated by our findings, we propose consen-sagent (figure 2 ). consensagent operates in four phases, mirroring human discussions. in phase 1, all agents provide individual answers, ensuring a thorough exploration of the problem space. phase 2 involves discussion to reach a consensus. phase 3 optimizes the prompt based on discussion insights. finally, phase 4 generates a team answer based on confidence and consistency. ## main results consensagent outperforms all baselines across multiple models for reasoning tasks additionally, we outperform reconcile @xcite , which improves discussions by adding convincing examples. in contrast, consen-sagent addresses the root cause of disagreement by optimizing the prompt itself. gains are highest on complex tasks like constraint satisfaction and family reasoning, where prompt flaws, irrelevant context, and agent confusion are more common and offer richer signals to refine from. consensagent generalizes to various combinations of agents across multiple rounds con-sensagent achieves strong results with both three and five agents, and is effective in both homogeneous and heterogeneous model settings. consistent with @xcite , we find that performance improves further when different models are involved. however, we show that diversity of responses can also be achieved by varying factors such as how a model is instruction tuned (open-hermes vs mistral instruction tuning), or with the same model family with different models. consensagent creates healthy discussions efficiently as shown in consensagent brings several benefits in the optimized prompts prompt optimization has the following effects (appendix b & j): • length: average p 0 is 30 words long, while average p 1 is 100 words long. consensagent reaches consensus faster in figure 3 , we plot the percentage of debates reaching a consensus after a certain number of rounds, comparing consensagent with a baseline multiagent debates without prompt optimization. a more detailed analysis is shown in appendix c. figure 3 shows that consensagent decreases overall cost, with around 90% of debates reaching a consensus after three rounds, while the baseline method has less than 70% of the debates reaching a consensus after three rounds. ## conclusion multi-agent debate has the potential to enhance llm reasoning, but challenges like sycophancy, high costs, and prompt ambiguities limit its effectiveness. we quantify the effect of these challenges on multi-agent performance. to address this, we introduce consensagent, a trigger-based prompt optimization framework that refines agent interactions in real-time, reducing sycophancy, improving consensus efficiency, and enhancing reasoning accuracy. our results establish consensagent as a key step toward making multi-agent debate a more scalable and effective strategy for complex reasoning tasks. future work could further reducing costs through adaptive agent selection.