MeMAD: Structured Memory of Debates for Enhanced Multi-Agent Reasoning
Authors: Shuai Ling
Lizi Liao
Dongmei Jiang
Weili Guan
Venue: COLM 2025 (2025)
Keywords: Multi-Agent Debate
Memory Augmentation
TL;DR: We propose Memory-Augmented Multi-Agent Debate (MeMAD), which systematically organizes and reuses past debate transcripts to  improve performance on complex reasoning tasks without requiring parameter updates.

Abstract: Large Language Models (LLMs) demonstrate remarkable in-context learning capabilities but often struggle with complex, multi-step reasoning. Multi-Agent Debate (MAD) frameworks partially address these limitations by enabling iterative agent interactions. However, they neglect valuable historical insights by treating each new debate independently. In this paper, we propose Memory-Augmented MAD (MeMAD), a parameter-free memory-augmented MAD framework that systematically organizes and reuses past debate transcripts. MeMAD stores structured representations of successful and unsuccessful reasoning attempts enriched with self-reflections and peer feedback. It systematically retrieves them via semantic similarity at inference time to inform new reasoning tasks. Our experiments on challenging mathematical reasoning, scientific question answering, and language understanding benchmarks show that MeMAD achieves significant accuracy gains (up to 3.3\% over conventional MAD baselines) without parameter updates. Our findings underscore structured memory as a pivotal mechanism for achieving deeper and more reliable multi-agent reasoning in LLMs. Code is available in ~\url{https://github.com/LSHCoding/MeMAD}.

Published as a conference paper at COLM 2025
MeMAD: Structured Memory of Debates for Enhanced Multi- Agent Reasoning
Shuai Ling!.3*
Lizi Liao?
Dongmei Jiang
Weili Guan
Harbin Institute of Technology (Shenzhen)
2Singapore Management University
Pengcheng Laboratory
24b952037@stu.hit.ooo Izli aoesuu. edu. sg honeyguan@gmail.com com
j jianddm@pcl.ac. cn
Abstract
Large Language Models (LLMs) demonstrate remarkable in-context learn- ing capabilities but often struggle with complex, multi-step reasoning. Multi-Agent Debate (MAD) frameworks partially address these limitations by enabling iterative agent interactions. However, they neglect valuable his- torical insights by treating each new debate independently. In this paper, we propose Memory-Augmenter MAD (MeMAD), a parameter- frree memory- augmented MAD framework that systematically organizes and reuses past debate transcripts. MeMAD stores structured representations of successful and unsuccessful reasoning attempts enriched with relf-reflections and peer feedback. It systematically retrieves them via semantic similarity at infer- ence time to inform new reasoning tasks. Our experiments on challenging mathematical reasoning, scientific question answering, and language un- derstanding benchmarks show that MeMAD achieves significant accuracy gains (up to 3.3% over conventional MAD baselines) without parameter updates. Our findings underscore structured memory as pivotal mech- anism for achieving deeper and more reliable multi-agent reasoning in LLMs. Code available in nttps://gg thub. com/LLSHCodinng/eMM..
1 Introduction
LLMs have significantly advanced the field of natural language processing, exhibiting remarkable in-context learning capabilities (Brown et al., 2020; ÖpenAI, 2024; Zhao al., 2024; Wei et al., 2022). However, despite these successes, LLMs often encounter difficulties when dealing with complex reasoning tasks that necessitate multi-step reasoning. Many approaches to enhance reasoning capabilities typically rely on parameter updates through methods such as fine-tuning, but these methods demand extensive high-quality data, sub- stantial computational resources and are susceptible to catastrophic forgetting. On the other hand, the impressive in-context learning ability LLMs has motivated a parameter-fre learning paradigm, enabling adaptation to diverse tasks through prompt engineering rather than parameter modifications (Min et al., 2022; Dong et al., 2024).
Under this parameter-fres paradigm, existing research primarily explores two streams: Single-agent methods, employ specialized prompting techniques (Zhou et al., 2023; Besta et al., 2024) such chain-of-thowe thought reasoning (Wei et al., 2023), multi-path sampling (Wang et al., 2023), and iterative self-refinement (Madaan et al., 2024; Shinn et al., 2023) improve reasoning performance. Alternatively, Multi-agent methods simulate human-like collaborative problem-solving by facilitating iterative interactions among multiple agents, thereby enabling mutual correction and knowledge exchange (Chan et al., 2023; Hong
"Work done during an internship in SMU.
1 'Corresponding authors

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et al., 2024; Chen al., 2024b). Among these, Multi-agent Debate (MAD) is effective representative (Du et al., 2023; Li et al., 2024). Although MAD has demonstrated promise in enhancing reasoning, current implementations typically treat each debate independently, discarding valuable debate transcripts once an answer is finalized. This practice neglects the rich reflections and iterative corrections embedded in historical interactions. It severely limits the system's 9 capacity for continuous improvement, especially in real-world scenarios where similar problems repeatedly arise under slightly varied contexts.
Recently, memory mechanisms have emerged as powerful tools for enhancing LLMs' abil- ities in long-term knowledge retention and dynamic adaptation (Modarressi et al., 2025; Zhong et al., 2024; Packer et al., 2024; Wang et al., 2024a). It signals viable way to further improve multi-agent reasoning. For example, Xu al. (2025) proposed an agentic memory system that significantly improves long-term interaction capabilities and performance complex tasks such as multi-hop reasoning. Despite their effectiveness, existing memory- augmented approaches predominantly emphasize general memory operations (e.g., reading, writing, and retrieval) and overcome the limitations of context window size. However, they often neglect transforming historical debate processes, especially valuable agent in- teractions, into structured, reusable experiences. This oversight constitutes a critical gap that hinders the systematic accumulation and reuse of valuable reasoning insights within MAD frameworks. Consequently, motivates targeted exploration of memory mechanisms specifically tailored for multi-agent debates.
To bridge this critical gap, we propose PennoryAAugented MAD (named as MeMAD), parameter-free frame- work that organizes and reuses de- bate experiences to guide future multi- agent interactions as illustrated in Fig- ure 1. MeMAD stores evidence of successful and failed reasoning path- cays-complete with self- and peer- feedback-in structured memory that prioritizes both the context of each problem and the insights gleaned from the debate process. During inference, retrieves and injects relevant experi- ences into agent prompts via semantic matching, ensuring that past lessons directly inform current debates without requiring any parameter updates. This design allows for contin- uous knowledge accumulation, enabling LLMs to learn from and build upon collective historical experiences. We evaluate MeMAD on challenging tasks across diverse domains and our experiments demonstrate that MeMAD significantly outperforms baselines without requiring parameter updates.
Memory
Generate Experience
Bank
A
Reflection Module
Agents
Agents (*Memmyy)
Debate
f Test Dataset
Training Dataset
Figure Memory enhancement without parame- ter update.
In summary, our contributions are threefold: 1) We propose a parameter-free framework that systematically stores and reuses past multi-agent debate experiences to enhance complex reasoning. 2) Our approach enriches debate records with self-reflection and peer-generated insights, capturing both successful and failed reasoning pathways for better usage. 3) Experiments on diverse complex reasoning tasks show consistent and significant accuracy improvements, demonstrating the value of reusing debate experiences.
2 Related Work
2.1 Multi-Agent Debate
The Multi-Agent Debate (MAD) framework leverages collaborative interactions among agents to enhance reasoning, inspired by the "society of minds" concept. Building on the foundational work of (Du et al., 2023), recent studies have explored two key directions to improve MAD: differentiated agents and mechanism refinement. Differentiated-agem meth- ods either assign distinct roles and expertise to agents (Chan et al., 2023; Liang et al., 2023; Li et al., 2023) or employ diverse pre-trained models to broaden the scope of debates (Chen
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et al., 2024b; Wang et al., 2024c). Mechanism refinement focuses on optimizing debate flow and reducing redundancy to improve efficiency (Liu et al., 2024; Li et al., 2024; Sun et al., 2024). However, existing MAD frameworks generally treat each debate independently, overlooking the structured accumulation and reuse of historical debate experiences. In con- trast, our method transforms the debate process into reusable parameter-free experiences, enabling cross-task improvements that traditional MAD frameworks have yet to achieve.
2.2 Memory Augmented LLMs
Recent works on memory augmentation for LLMs have explored various mechanisms to enhance long-term knowledge retention and dynamic memory updating (Modarressi et al., 2025; Zhong et al., 2024; Packer et al., 2024; Wang et al., 2024a). Some approaches employ ex- plicit read-write memory architectures or parameter fine-tuning techniques to permanently integrate new knowledge (Modarressi et al., 2025; Wang et al., 2024d; Zhong et al., 2024), but these methods often incur high computational costs. Alternatively, non-parametric methods leverage external knowledge retrieval (Qian et al., 2024) or compression-based storage strategies (Wang et al., 2025; 2024a) to dynamically update and efficiently retrieve relevant information. Additionally, cognitive-inspired frameworks introduce selective forgetting and memory reinforcement mechanisms (Zhong et al., 2024) to further enhance long-term interaction capabilities. Nonetheless, existing memory-augmentttion methods predomi- nantly focus on general memory operations (reading, writing, retrieval) and overcoming the limitations of context window size, largely neglecting the structured transformation of multi-agent debate interactions into reusable reasoning experiences. Our MeMAD frame- work addresses this limitation by systematically structuring and reusing debate transcripts enriched with reflective insights, thereby significantly enhancing multi-agent reasoning without parameter updates.
3 Problem Setup
To systematically describe the process of MAD and establish a unified notation system for subsequent methodological discussions, this section formalizes the core elements of the MAD framework. Consider set of N agents A {A1, AN) engaged in a debate about a given question Q. The debate process consists of T iterative rounds. At each round t E (1, Т}, each agent A; generates response O₂, based on the question Q and the debate history up to the previous round, denoted as H4,-1 (where the initial state is O,,,,,,,,
H4,0 = Ø). The debate history is subsequently updated as HIp = which serves as the contextual information for the next round for agent A,
a feedback mechanism is incorporated, agents provide both self-feedback and peer-feedback at the end of each round. Specifically, after all agents produce their responses (O,, ON, } in round t, each agent A, generates self-feedback SF,, for its own response and feer-feedback PFij. for the response of agent Aj. These feedback signals are then appended the debate history, updating it to Hi, = (H4,-1,
ON,, SF,, {PFj..)) and enriching the context for the next round of debate. After T rounds, all agents participate in voting process to determine the final answer, resulting in the debate outcome R Rebate-
4 The MeMAD Method
We propose Memory-Augmented Multi-Agent Debate (MeMAD) overcome the challenge of underutilized experiences in traditional MAD systems. As depicted in Figure 2, MeMAD integrates two key
ceooonnnts-epprieevee accumulation and memory weirien the debate loop. In the experience accumulation phase, each debate is augmented with both self- and peer-feedback, which are systematically stored in a structured memory to capture reasoning trajectories and reflective insights. In the inference phase, the memory retrieval mechanism leverages these structured records to guide ongoing debates, facilitating more informed and efficient multi-agent reasoning. Section 4.1 delves into the dual-level feedback
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Figure Overview of the MeMAD, consisting of two components: (1) Experience Accumula- tion (top), featuring a Dual-level Feedback Mechanism with Relf-Reffection and Peer-Reflecten Modules, followed by an Experience Storage Module that categorizes reasoning attempts as positive or negative examples; and (2) Retrieval and Inference (bottom), where relevant past experiences are retrieved from the Memory Bank to inform new debates.
and structured storage process, while Section 4.2 details how these accumulated experiences are retrieved and applied to enhance performance across diverse reasoning tasks.
4.1 Experience Accumulation: Construction of Experience-driven Memory Bank
The experience accumulation phase is a pivotal component of the MeMAD framework. As depicted in Figure unlike traditional MAD frameworks, MeMAD introduces an innovative dual-level feedback mechanism and a structured experience storage system during this phase. The primary objective of this phase to construct a memory bank comprising rich, high-quality, and well-structured debate experiences, thereby laying solid foundation for subsequent experience retrieval and reasoning. achieve this goal, MeMAD employs dual-level feedback mechanism, which enables the multi-agent system to generate high- quality, reusable structured experiences from two complementary dimensions: self-reflection #Self-Feedbade and mutual assistance Feebbakk.
4.1.1 Dual-level Feedback Mechanism
To facilitate effective learning and experience accumulation during the debate process, the MeMAD framework employs a dual-level feedback mechanism. This mechanism evaluates and provides feedback each agent's debate performance at the end of every debate round by leveraging the ground truth answer to the question as an external supervision signal. Specifically, at the conclusion of each debate round f, the ground truth answer is used to assess the correctness of each agent A/'s response Og, generating a binary supervision signal C₁ [True, False). Building upon this binary supervision signal and the ground truth answer, the MeMAD framework incorporates self-feedback and peer-feedback mechanisms, which enable the generation of agent experiences from two complementary perspectives: intrinsic self-reflection and external peer evaluation.
Self-Feedback. Each agent A; engages in self-feedback process upon receiving the supervision signal C,,, which involves thorough reflection its reasoning mechanism. The self-feedback, denoted as SF,,, , is a textual output generated by agent A, based on its historical context Hi, and the supervision signal, guided by predefined self-feedback

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prompt template P.f (see Appendix E.1 for details). The primary objective of this process is to analyze and identify the underlying factors contributing to the successes or failures observed during the current round of debate. This analysis facilitates the formation of experiential knowledge that can be generalized to future scenarios. The formal definition of i is given as:
S = Ai (Psy(Q, Oi, Ci,), Hip-1)-
Here, P, G,) is the self-feedback prompt constructed based on the question Q the ground truth answer Y, the agent output Oi and the correctness signal Ci
Peer-Feedback. To effectively facilitate collaborative learning and knowledge sharing among agents, the MeMAD framework incorporates peer-feedback mechanism. When agent A, provides correct answer in a debate while agent A; answers incorrectly, A, is responsible for offering peer feedback A, This feedback involves identifying deficiencies in A;ss response and providing actionable suggestions for improvement.
The peer feedback, denoted as PFj,,,, is generated by the correct-answering agent Aj based its historical context Hi;--1 and supervisory information. The generation process is guided by predefined peer-feedback prompt template Ppf (see Appendix E.1 for details), which takes as input the question Q, the ground-truth answer Y, the output O₁, of agent A,, and the output Oj of agent A,. Formally, the peer feedback is defined as:
PF Aj - (ptt(Q.
4.1.2 Experience Storage Module
At the end of each debate round t, the key information generated by each agent A, is organized into an experience tuple E,,, which is subsequently stored in the agent's dedicated memory bank Mr,- The memory bank is composed of a positive set M. and a negative set Mi The specific storage location is determined by the agent's response: successful experiences are stored in M while failed experiences are stored in M,, The structure of the experience tuple E is formally defined as:
E =< Q, O Refi, >
Here, Refi, refers to the reflective experience, which integrates both self-feedback and peer-feedback, aiming to provide effective guidance for future debates. Specifically, the construction Refi, is defined as:
SF (PF
if Cj = True,
Refi,
if Cj, False
The memory bank Mi, serves as a dedicated storage space for each agent, systematically accumulating all structured experiences generated during the experience accumulation phase. As the number of debate rounds increases, M,, is continuously expanded, laying solid foundation for efficient experience retrieval and utilization during the testing phase.
4.2 Retrieval and Inference
After the experience accumulation phase, the memory bank is utilized during the multi- agent debate process. As shown in Figure 2, a key innovation of MeMAD compared to traditional MAD is its integration of structured memory. At the start of each debate round, agents retrieve relevant historical experiences from their dedicated memory banks based on the current question. These retrieved experiences agents to leverage past knowledge to enhance reasoning capabilities and debate efficiency. The memory bank is implemented using Chroma, open-source AI application database.
then injected into the prompt, enabling

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During the testing phase, at the beginning of each debate round for given test question Q each agent A; retrieves relevant experiences from its memory bank My, The MeMAD framework employs a semantic similarity-basee retrieval mechanism to identify pertinent experiences. The detailed steps
follows:
1. The test question Qlest and the agent's historical answer Oz (empty in the initial round) are semantically encoded into query vector qi using Sentence-BERT encoder (Chen et al., 2024a):
4 Encoder((Q 1)).
2. For each experience Ei, = (Q', Yt ,
i, Reff,) in the memory bank Mi, = M U i its corresponding question @* and answer O are encoded into a vector Mi
ff. The cosine similarity between qi and vf, is then computed.
3. With similarity scores, the top-K experiences are selected from both the positive memory set Mi and the negative memory set M,, forming the retrieved set:
pk,t, El.- Everiievdd = {E ,.. Eit L y.
pk - Eit
Once the relevant experiences Exetrieved are retrieved, they are injected into the agent's prompt to guide the debate process:
O₂ = A (Q Evetrieved) ), Hif--)-
By leveraging retrieved experiences, the MeMAD framework enables agents to avoid repeating past mistakes and adopt more effective reasoning strategies. This significantly improves both the efficiency and accuracy of the debate process during the testing phase.
Experiments
5.1 Experimental Setup
Datasets. evaluate our method across four datasets spanning mathematics, science, law, and economics. Specifically, we use (1) MATH500 (a subset of the MATH benchmark with advanced math problems) (Lightman et al., 2023), (2) GPQA (graduate-leve multiple-choice questions in biology, physics, and chemistry) (Rein et al., 2023), and (3) two specialized subsets of MMLUPro (we denote as Law and Economics correspondingly) (Wang et al., 2024e). Each dataset is split into training and test sets to ensure sufficient coverage and diversity. Detailed descriptions and selection criteria are provided the Appendix B.
Baselines.
The baselines for comparison can be categorized into three main groups:
Vanilla single-agent methods. We employed OpenAl's GPT-4o-mini, ChatGPT, and the open-source Qwen2.5-145 model (Yang et al., 2024). All tasks were performed using the same task prompts as those adopted in the MeMAD benchmark.
Advanced single-agent methods. Such methods go beyond single pass of generation by employing more nuanced mechanisms for feedback and exploration. (1) Self-Refine iteratively improves its output by generating internal feedback and refining its own responses (Madaan et al., 2024), while (2) Self-Consistency samples multiple reasoning paths and aggregates the final answers to mitigate the impact of any single flawed chain of thought (Wang et al., 2023). (3) Reflexion complements these approaches by reinforcing decision-making through linguistic feedback, enabling agents to reflect on and learn from their own outputs (Shinn et al., 2023).
Multi-agent methods. We utilized two multi-agent-based methods as baselines: Multi- Agent Debate (MAD) (Du et al., 2023) and -n-eens-of-Agents (MoA) (Wang et al., 2024b).

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Implementation Details. Considering the inherent cost associated with API-based models and in line with established practices in prior research, we adopted a dataset sampling strategy in this study Chen et al. (2024b). For the Law and Economics datasets, we randomly sampled a portion of the data as the test set. For MATH500, we selected the most challenging problems as the test set (with level of 5). For GPQA, we used the GPQA Diamond subset as the test set. All agents were built using the GPT-4o-mini model provided by OpenAl.
5.2
Main Results
Category
Method
MATH500
GPQA
Law
Economics
Average
GPT-4o mini
0.485
0.379
0.395
0.683
0.486
Vanilla single-agent
ChatGPT
0.179
0,281
0.342
0.550
0.338
Qwen2.5-14B
0,500
0,429
0,370
0.673
0.493
Advanced
Self-Refine
0.537 (10.55)
0.438
(-0099
0.383 (<0012)
0.692 (+0.00))
0.513 (40.027)
Self-Consistency
0.560 (10.075)
0.402
(+0.023)
0.411
(+0.016)
0.673 (-0.010)
0.512 (+0.026)
single-agent
Reflexion
0.560 (+0.075)
0.414 (+0.035)
0.412 (+0.017)
0.713 (+0.030)
0.525 (+0.039)
MAD
0.552 (+0.067)
0,409
(-0.030)
0.425
(+0.030)
0.701
(+0.016)
0.522
(-0.036)
Multi-agent
MoA
0.537 (+0.052)
0.419
(-0.40))
0.435
(+0.040)
0.763 (+0.080)
0.539 (-0.053)
MeMAD (ours)
0.590 (10.105)
0.460 (s0.081)
0.457
(v0.062)
0.782 (40.099)
0.572 (00.066)
Improvement
t 0.030
0.022
0.022
1 0.019
0.033
Table 1: Performance comparison of different methods across datasets. Green text indicates improvements over GPT-4o-mini, gray text signifies declines compared to GPT-4o-mini, and red text highlights MeMAD's advancements over other top-performing methods.
MeMAD consistently outperforms all baseline methods. Table 1 presents comprehensive experimental results across multiple reasoning tasks. Overall, MeMAD achieves SOTA performance, surpassing the strongest baseline, MoA, by 3.3% and improving upon the MAD framework by 5%. These results highlight the effectiveness of structured memory augmentation in multi-agent debates. Specifically, MeMAD achieves performance gains of 3.0%, 2.2%, 2.2%, and 1.9% on the MATH500, GPQA, Law, and Economics datasets, respectively. This consistent improvement across domains demonstrates the generalizability.
Advanced single-agent methods show incremental but limited improvements. Within the Advanced single-agent category, Reflexion achieves the best overall performance, out- performing Self-Refine and Self-Consistency on three out of four datasets. Reflexion's advantage lies in its memory mechanism, which enables iterative reasoning and feedback reuse, allowing it to capture and utilize historical insights. In contrast, Self-Refine relies on iterative refinement strategies but lacks the memory-driven enhancements of Reflexion, limiting its effectiveness. Self-Consistency, which employs multi-path sampling akin to simplified multi-agent debate, demonstrates modest gains but fails to fully leverage collabo- rative reasoning. These results suggest that while advanced single-agent methods improve upon vanilla approaches, their lack of structured memory integration constrains their ability to tackle complex reasoning tasks.
Multi-agent methods demonstrate superior performance. The Multi-agent methods con- sistently outperform single-agettapprocches with MeMAD achieving the highest accuracy across all evaluation tasks. The significant improvements over MAD validate our core hypothesis that structured experience reuse enhances multi-agent reasoning, Importantly, our parameter-fres framework for experience accumulation and retrieval can be readily integrated with other multi-agent architectures, offering promising direction for future research in collaborative reasoning systems.
5.3 Detailed Analysis and Ablations of MeMAD
Comprehensive Memory Components Enhance Reasoning Performance. To systemati- cally evaluate the impact of different memory components on MeMAD's performance, we conducted an ablation study on the Economics dataset. Starting from a minimal memory configuration containing only questions and solutions (Q Y), we incrementally incorpo-
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rated additional components: agent responses (O), self-reflection (SR), and peer-reflection (PR). The results, as shown in Table 2, clearly demonstrate the cumulative benefits of these components.
The baseline configuration (Q + Y) achieved accuracy of 75.4%, providing founda- tional understanding of the problem and its solution. Adding agent responses and self- reflection (Q Y O + SR) led to a 1.4% im- provement, reaching 76.8%. This highlights the value of incorporating iterative reason- ing and self-assessment into the memory structure. The full configuration, which in- tegrates both self- and peer-reflections (Q
Memory
Acc
0.701
Q+Y Y
0.754 (+0.053)
Q+ Y O SR
0.768 (+0.067)
++ (SR PR)
0.777 (+0.076)
Table 2: The impact of different experience compositions on performance: question (Q), solution (Y), agent response (O), self-reflection (SR), and peer-reflection (PR).
+ (SR U PR)), achieved a further im- provement, reaching 77.7%. These results validate that optimal performance requires both complete contextual information and multi-perspective feedback, aligning with our framework design in Section 4.
Embedding-bassed retrieval surpasses token-overlap-based retrieval. To evaluate the effectiveness of different retrieval methods within MeMAD (Li & Li, 2024; Lee et al., 2024), we conducted comparative analysis of token-overlap-based and embedding-baas based ap- proaches, as summarized in Table 3. Overall, embedding-basse approaches outperform the traditional loken-overlap-basee BM25 method, underscoring the importance of semantic expeeeeeeeeeeeee experiences.
understanding in retrieving debate
Among the embeddinggbased methods, nomic-embed-tex achieved the best performance on the Economics (0.782) and Law (0.457) datasets, showcasing its strong ability handle domain-specific tasks that require nuanced semantic understanding. On the other hand, bge- m3 excelled on the MATH500 (0.590) and GPQA (0.460) datasets, indicating its robustness in mathematical reasoning and scientific question answering tasks. Notably, bge-m3 emerged as the best pest-performing method overall, with an average accuracy of 0.567, outperforming nomic-embed-teex (0.553) and mxbai-embed-larre (0.548). Additionally, BM25 performs comparably to bge-m3 on MATH500 (0.590), suggesting that the mathematical reasoning dataset may rely more on lexical matching than deep semantic representations.
Category
Method
MATH500
Economics
GPQA
Law
Average
Token-overlap
BM25
0.590
0.739
0.419
0.424
0.543
bge-m3
0.590 (-0.00)
0.777 (+0.035)
0.460 (-0,01)
0.440 (-0.016)
0.567 (+0.024)
Embedding
nomic-embed-text
0.360 (-006)
0.782 (v0.033)
0.414 (0.05)
0.457 (G0,3))
0.553 (+0.010)
mxbai-embeddlarge
0.582 (0.00s)
0.744 (10.055)
0.455 (-0006)
0.409 (a.01)
0.548 (10.005)
Table 3: Impact different embedding methods. Green text indicates improvements over BM25 and gray text signifies declines compared to BM25.
MeMAD outperforms other experience selection strategies. To systematically evaluate the impact of different experience selection strategies and memory configurations on MeMAD's performance, we conducted comprehensive ablation studies on the Economics dataset. We compared three experience selection strategies: (1) Random, which selects N cases randomly from the memory bank; (2) Similarity, which retrieves the top-N cases with the highest semantic similarity to the current problem; and (3) Diversity, which first clusters the experiences using K-Means and then selects one semantically similar case per cluster to ensure diversity. To ensure fairness, all experiments were constrained to select the same number of experiences. Additionally, we examined the contributions of successful (M) ) and failed (M ) debate experiences, as well as their combination (M U M ), to assess the role of memory bank composition. Table 4 summarizes the results, which clearly demonstrate that memory augmentation consistently enhances reasoning accuracy across all methods, validating the importance of leveraging historical debate experiences.
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In this paper, we introduced Memory-Augmented Multi-Agent Debate (MeMAD), a novel parameter-free framework that systematically captures and reuses debate experiences to enhance complex reasoning. By integrating a structured memory system with dual-level effectively pre- Published as a conference paper at COLM 2025
The results in Table 4 high- light several key findings, First, memory augmentation consistently improves perfor- mance over the MAD across all selection strategies, affirm- ing the utility of historical de- bate experiences (Green col- ored numbers indicate the improvements over MAD).
Selection Strategy
M
M
M U.M
Random Similarity Diversity
0.730 (10.299)
0.720 (-am9)
0.739 (.0.038)
0.735 (40.044)
0.739
(-0008)
0.754 (-0.053)
0.739 (+0.038)
0.754 (-0053)
0.768 (-0.067)
Positive and Negative
0.777 (+0.076)
Table 4: Performance of different experience selection strate- gies on Economics. We use bge-m3 as it performed the best across datasets on average.
However, the choice of experience selection strategy significantly influences effectiveness. Random yields only marginal gains (2.9%), while Similarity retrieval achieves substantial accuracy improvements (4.2%).. Specifically, Similarity retrieval outperforms Randon by 1.3% on average, demonstrating the importance of selecting contextually relevant experi- ences. Second, we observe that Diversity, which balances semantic similarity with diversity, achieves better performance. Finally, our analysis reveals that our proposed Positive and Negative strategy, adding in both successful and failed debate experiences, yields the highest performance (0.777). Notably, methods over the negative set M sometimes perform com- parably or even outperform methods over the positive set M suuggesting that learning from failed reasoning attempts provides unique insights. Methods over the combined (M+ U. leverage the complementary strengths of both sets, achieving optimal results. These findings validate MeMAD's core design principle of learning from both successes and failures, underscoring the importance of a comprehensive and structured memory.
5.4 Transferability and Generality
MeMAD demonstrates cross-task knowledge trans- ferability. To evaluate the cross-task experience transferability of the MeMAD framework, we con- ducted an experience transfer experiment, sum- marized in Table 5. In this experiment, we utilized experiences accumulated from the MATH500 task to improve performance on the MMLUPro-Math task, which differs significantly in task format. To mini- mize the impact of format-specific features, the expe- rience repository was curated to retain only taskforrmatindependent content, specifically self-reflective and peer-reflective feedback. As shown in Table 5, MeMAD achieves a sub- stantial accuracy improvement of 4.0% on the MMLUPro-Math task (from 0.725 to 0.765) compared to the MAD baseline. This result strongly validates the cross-task generalization capabilities of the MeMAD framework, even when these tasks differ significantly struc- ture. Furthermore, this experiment indirectly corroborates the effectiveness of the dual-level feedback mechanism, which enriches the memory bank with transferable insights.
Method
Acc
MAD
0.725
MeMAD
0.765
Table 5: Evaluation of experience transfer performance.
MeMAD demonstrates generality across stronger LLMs. To evaluate the generality of the MeMAD framework, we designed a generality experiment, with the results summarized in the table 6. Specifically, we conducted experiments on the GPQA and Economics datasets, leveraging the experience accumulated by GPT-4o-mini to enhance strongger models, GPT- 4o and Deepseek-V3. V3.. Compared to MAD, MeMAD exhibited performance improvements across all models on both datasets, highlighting the generality of our approach to more advanced models. Additionally, we observed that even experience derived from weaker models can be effectively transferred to enhance the performance of stronger models.
Conclusion
baoback--comprittee both self-feedback and peer-feedback-Merd

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GPQA
Economics
Method
GPT-4o
DeepSeek-V V3
GPT-4o
DeepSeek-V3
Single Agent
0.490
0.523
0.798
0.768
MAD MeMAD
0.540 (+0.050)
0.535 (=0.012)
0.796 (-0.002)
0.787 (+0.019)
0.556 (+0.066)
0.540 (+0.017)
0.815 (-0.017)
0.815 (+0.047)
Table 6: Generality evaluation of MeMAD across stronger models on GPQA and Economics datasets. Green text shows improvements over Single Agent, while gray indicates declines.
serves the rich iterative reasoning process that traditional MAD frameworks tend to discard. Our experiments on challenging tasks such as mathematical reasoning, scientific question answering, and language understanding demonstrate that MeMAD yields significant im- provements in accuracy and robustness. In the future, we will investigate dynamic memory update strategies and explore the incorporation of external knowledge sources to further boost the system's 's reasoning capabilities.
Limitations
While MeMAD effectively reuses historical debate experiences to improve multi-step rea- soning, it introduces additional overhead for maintaining, retrieving, and updating the structured memory. In particular, ensuring data quality in both positive and negative axamples-along with their associated feedback-can be labor-intensive. Moreover, our approach assumes that tasks share enough similarity to benefit from prior debates, which may limit its impact on highly diverse or out-of-distribution tasks. Future work could explore more automated feedback generation, dynamic memory pruning, and adaptive retrieval techniques to further enhance MeMAD's scalability and generalization.
Acknowledgement
This research is supported by the National Research Foundation, Singapore under its National Large Language Models Funding Initiative (AISG Award No: AISG-NMLP-2024 002). Any opinions, findings and conclusions or recommendations expressed in this material are those of the author(s) and do not reflect the views of National Research Foundation, Singapore. This work is also supported by the National Natural Science Foundation of China under Joint Fund Project (U24A20328) and General Program (62476071), as well as the Guangdong Basic and Applied Basic Research Foundation (2025A1515011739)
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A Experimental Details
In this section, we provide a detailed explanation of the implementation of MeMAD. The model is configured with temperature parameter of 0.7, while other hyperparameters are set to their default values. Specifically, we employed the following model configurations: GPT-3.5-turbo-011 (referred to as ChatGPT), GPT-44--mini22024--77
(referred to as GPT- 4o-mini), and GPT-44-2022408-00 (referred to as GPT-4o) via the OpenAl API Deepseek-V (referred to as Qwen2.5 14B) via the Alibaba Cloud API 3, All MeMAD experiments were conducted with a configuration of three agents, with the maximum number of debate rounds set to 3. During the experience accumulation phase, all agents underwent the maximum number of debate rounds. In the retrieval and inference phase, the process terminated when all agents reached a consensus or the maximum debate rounds were reached. For the configurations of MAD and MoA experiments, we strictly adhered to the settings described in the original papers.
via the Deepseek API 2, as well as 2.wenn2.-14D--imstructfff
B Datasets
We conducted experiments on datasets covering a wide range of domains, including complex reasoning problems in mathematics, physics, chemistry, biology, law, and economics.
/https://openai.com/
Bntps://www.deppsek.com Phtps://wwww.aiyun.com/
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This dataset is subset of the MATH benchmark, consisting of 500 math- ematical problem-solving questions categorized by topic and difficulty (Lightman et al., 2023). In our study, the training set was constructed by randomly sampling 100 problems per category from the MATH training set, restricted to problems with difficulty level of at least 3. For evaluation, to ensure the challenge of the experiments, we selected 134 problems with the highest difficulty level (level 5) from the MATH500 dataset as the evaluation set.
MATH500:
GPQA: A dataset comprising 448 graduate-level multiple-choice questions span- ning biology, physics, and chemistry. All questions were validated by domain experts to ensure both objectivity and difficulty (Rein et al., 2023). For experi- ments, we used the GPQA Diamond subset (198 questions) as the test set, while the remaining questions from the GPQA Main (excluding GPQA Diamond) were used as the training set.
MMLUPro is comprehensive benchmark designed for multi- discipline language understanding and reasoning, spanning 14 domains (Wang et al., 2024e). For this study, we utilized the Law subset, which contains 1,101 high-quality multiple-choice questions. The dataset was randomly divided into training and testing sets in a 3:1 ratio, resulting in 826 questions for experience accumulation and 276 for evaluation.
MMLUPro-Laww
MMLUUPro-Economics Similarly, we also employed the Economics subset of MMLUPro (Wang et al., 2024e), which comprises 844 multiple-choice questions. Following the same 3:1 split strategy, we obtained 633 questions for training and 211 for testing.
C
Baseline Details
Here list more details about baseline models:
Self-Refine. It generates initial output with LLM, then the LLM gives feedback and refines the output iteratively. doesn't need extra training data or RL, and performs well across diverse tasks, serving benchmark for comparison (Madaan et al., 2024).
Self-Consistency: It samples diverse reasoning paths from a language model's decoder, then aggregates final answers by marginalizing out the reasoning paths. This unsupervised method improves language models' reasoning performance on various tasks (Wang et al., 2023).
Reflexion: It reinforces language agents via linguistic feedback. Agents reflect task feedback, store reflective text in memory, and use it for better decision-making It shows good performance in diverse tasks like decision-making, coding, and language reasoning (Shinn et al., 2023).
MAD: This approach generates answers through multi-round debates among multi- ple language model instances, which can improve reasoning and performs well in various tasks, providing a reference for comparative experiments (Du et al., 2023).
MoA: It employs layered architecture, with each layer comprising multiple LLM agents. Agents use outputs from the previous layer supplementary input, en- abling effective information flow. MoA has demonstrated notable performance across diverse range of tasks (Wang et al., 2024b).
D Additional Experiments
D.1 The impact of memory on different models
Memory boosts reasoning for single and multi-agent models. To systematically evaluate the effectiveness memory augmentation, we conducted comparative experiments on both single-agent and multi-agent baselines, with and without memory augmentation. For fair
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comparison, we implemented memory-augmentedd single-agent framework that mirrors our approach in memory retrieval mechanism-inccroraaaig -inorporaatnng both successful and failed experiences from the training set through semantic similarity matching.
Figure 3 presents the comparative results across different model configurations. The experi- mental outcomes demonstrate that memory augmentation consistently enhances reasoning performance across all agent architectures while maintaining parameter- operation. Notably, more advanced models like GPT-4o-mini and Qwen2.5 exhibit more substantial improvements compared to ChatGPT, suggesting that stronger models possess superior ca- pabilities leveraging structured experiences. Furthermore, the performance gap between MeMAD and MAD significantly exceeds that between Me+MoA and MoA, indicating that while these accumulated experiences can enhance MoA's performance, their effectiveness is particularly pronounced in cebate-based frameworks where interaction plays a crucial role.
Sancry
0.00
Chariyy
OPP-o mta
Owass
MAD
Charpty
CPT OPo-ormter
Mld
MAD
Charoyy
GPT T-le mto
Ohou2.
MAD
Chartry
GPT douiio
(hwed.
MAD
Figure 3: Performance of different methods with and without memory augmentation.
D.2 Token Usage Analysis
MeMAD enhances reasoning ability without increasing computational overhead. This experiment investigates the average token consumption per question for MAD and MeMAD across various datasets, analyzing both prompt and completion token usage. As illustrated in Table 7, MeMAD integrates structured memory into prompts, yet its overall token consumption remains comparable to or even lower than that of MAD. Notably, MeMAD consistently reduces completion token usage. This efficiency is achieved because MeMAD terminates the debate process as soon as all agents reach consensus, avoiding unnecessary token generation in prolonged interactions.
#Prompt Tokens
#Completion Tokens
Method
MAD
MeMAD
MAD
MeMAD
GPQA
17.81K
16.56K
3.61K
2.49K
MATH500
20.96K
25.39K
5.76K
4.11K
Law
12.48K
19.66K
1.65K
1.64K
Economics
10.06K
7.67K
1.68K
0.87K
Table 7: Comparison of token consumption between MAD and MeMAD.
E Prompts in MeMAD
E.1 Prompts in Dual-level Feedback Mechanism
The prompts for the self-feedback module and the peer-feedback module are shown in Table and Table 9, respectively.
E.2 Prompts for different Tasks
The prompts for different tasks shown in Table 10.
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Self-Feedback Prompt Template
You are tasked with analyzing a problem-solving example and generating transferable insights for future improvement solving similar problems.
Given Information:
<question>> {[question}} < </quesiion>>
ecorrect.souutions (correct correct.solutionn
<llm responsss (esmoses)osse) //llmrresponse>
<response.correction correctness>
Response Correctness is correct [or incorrect).
< response correctness>
Analysis Framework:
1. Compare the solutions: 1) Identify similarities and differences in approach; 2) Analyze which elements worked better and why; 3) Note any efficiency or clarity advantages
2. If correct: 1) Identify successful reasoning patterns; 2) Extract key decision points that led to success
If incorrect: 1) Tdentify the gap between response and correct answer; 2) Analyze where the reasoning went wrong. 3) Determine what knowledge or step was missing
Output Requirements:
Generate exactly 3 key learned insights in this format:
"Key Learning #[number]: [specific insight]
Application: [how apply this learning to future problems]"
Each learned insight must be: 1) Generalizable (applicable to similar problems) 2) Specific (clear action or thinking strategy) 3) Concise (one sentence for insight, one for application) 4) Focus on problem-solving strategies only
Note:
1) Do not restate the specific problem content or solution; 2) Only output the learned insights.
Table 8: Prompt template for self-feedback.
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Peer-Feedback Prompt Template
You are tasked with analyzing an incorrect LLM response by comparing it with both the standard solution and correct LLM response, to generate generalizable insights that can help LLMs better solve similar problems in the future.
Given Information:
<question>> {[question)) </question>>
<correct solution.> {[correct solution)) < correct solution>>
clorret_llm.response>>>>> [[correct_lm_resoonsss Imm
Ilm ulm_rrsponse>>> ((incorrect www. r </incorrect llm. responsse
Analysis Framework:
1. Error Pattern Analysis: 1) Identify where incorrect response deviates from both correct approaches; 2) Analyze the root causes of these deviations; 3) Detect patterns of misconceptions flawed reasoning.
2. Success Pattern Recognition: 1) Study how correct LLM response aligns with standard solution; 2) Identify key elements missing in incorrect response; 3) Extract successful reasoning patterns and approaches.
3. Improvement Opportunities: 1) Pinpoint specific areas where incorrect response could be enhanced; 2) Identify critical checkpoints that could prevent similar errors; 3) Formulate strategies to bridge the gap between incorrect and correct approaches.
Output Requirements:
Generate exactly 3 transferable insights in this format: "Learning Point [number]: Ispecific insight]; Strategic Application: [concrete strategy for future problem- solving]"
Each insight must be: 1) Focused on preventing similar errors in future; 2) Strategy- focused (not problem-specific); 3) Action-oriented; 4) Clearly articulated in 1-2 sentences.
Note:
1) Emphasize practical strategies for enhancement; 2) Ensure insights are applicable
to future problem-solving: 3) Avoid repeating specific problem details solutions; 4) Emphasize methodological improvements rather than content knowledge; 5) Only output the learned insights.
Table 9: Prompt template for peer-feedback.
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Prompts for different Tasks
MATH500:
Can you solve the following math question accurately as possible?
(rmestion//qquettoon)/////
Present your analysis concisely using only essential reasoning steps. Provide the final answer in double parentheses at the end of your response ((answer)
GPQA:
Please analyze the following question and solve it as accurately as possible.
<question><(qqeesiion
Present your analysis concisely using only essential reasoning steps. Provide the final answer in double parentheses at the end of your response: ((answer), where answer is A, B, C, or D.
MMLUPro-Law:
Please analyze the following legal question:
Present your analysis concisely using only essential reasoning steps and select the correct answer. Provide your final answer in double parentheses: ((answer)) where answer can be A, B, H,
MMLLUPro-Economicc:::
Please analyze the following economics question:
<
Present your analysis concisely using only essential reasoning steps and select the correct answer. Provide your final answer double parentheses: ((answer)), where answer can be A, B, D, G, or
Table 10: Prompt template for different task.
18


Reviews:
[{"review_id": "7IOOcBZ5xV", "reviewer": "Reviewer_84jq", "summary": "The paper introduces MeMAD (Memory-Augmented Multi-Agent Debate), an enhancement of the Multi-Agent Debate (MAD) framework that allows the agent to learn from past experiences without parameter update by storing and retrieving relevant structured information that is accumulated while answering previous questions. Specifically, both self-reflections (generated by the agent itself) and peer-reflections (generated for the agent by other agents) are collected, and agent leverages information from both successful and unsuccessful experiences via similarity-base retrieval.\n\nExperiments over several datasets that require complex reasoning show consistent improvement over single-agent and multi-agent baselines, using GPT-4o-mini as the agent model. Additional experiments validate the contribution of each component in the proposed method. The learned experiences are also shown to be transferrable between different tasks, and also benefit stronger models (GPT-4o and DeepSeek-V3). \n\nThe paper is very well written. The formal notations used in the paper make it easy to follow. The experiments are thorough and the experimental results are convincing.", "questions": "", "limitations": "", "rating": 7, "confidence": 3, "soundness": null, "presentation": null, "contribution": null, "strengths": "", "weaknesses": "", "quality": null, "clarity": null, "significance": null, "originality": null, "strengths_and_weaknesses": "", "was_revised": false, "final_justification": "", "rebuttal": "", "claims_and_evidence": "", "methods_and_evaluation_criteria": "", "theoretical_claims": "", "experimental_designs_or_analyses": "", "relation_to_broader_scientific_literature": "", "essential_references_not_discussed": "", "other_strengths_and_weaknesses": "", "other_comments_or_suggestions": "", "questions_for_authors": "", "summary_of_paper": "", "summary_of_recommendation": "", "technical_quality": "", "clarity_of_presentation": "", "potential_impact": "", "robotics_focus": "", "quality_of_limitations": "", "questions_for_rebuttal": "", "reasons_to_accept": "The paper makes a valuable enhancement to the MAD framework, which is shown to improve performance on complex reasoning tasks. The proposed method may be applicable to a wide range of tasks.", "reasons_to_reject": "NA", "questions_to_authors": "", "extra_scores": {}}, {"review_id": "QArlu6AAw4", "reviewer": "Reviewer_Ayn1", "summary": "This paper introduces MeMAD (Memory-Augmented Multi-Agent Debate), a multi-agent framework designed to improve multi-agent reasoning by systematically reusing prior debate experiences. While existing MAD approaches treat each reasoning episode independently, MeMAD captures and stores structured records of both successful and failed debates, enriched with self-reflections and peer feedback. MeMAD further retrieves semantically relevant past experiences and injects them into the agent prompts to inform future debates.", "questions": "", "limitations": "", "rating": 5, "confidence": 4, "soundness": null, "presentation": null, "contribution": null, "strengths": "", "weaknesses": "", "quality": null, "clarity": null, "significance": null, "originality": null, "strengths_and_weaknesses": "", "was_revised": false, "final_justification": "", "rebuttal": "", "claims_and_evidence": "", "methods_and_evaluation_criteria": "", "theoretical_claims": "", "experimental_designs_or_analyses": "", "relation_to_broader_scientific_literature": "", "essential_references_not_discussed": "", "other_strengths_and_weaknesses": "", "other_comments_or_suggestions": "", "questions_for_authors": "", "summary_of_paper": "", "summary_of_recommendation": "", "technical_quality": "", "clarity_of_presentation": "", "potential_impact": "", "robotics_focus": "", "quality_of_limitations": "", "questions_for_rebuttal": "", "reasons_to_accept": "1. MeMAD is a new architecture that systematically accumulates and reuses structured debate experiences\n1. MeMAD demonstrates good performance improvements across multiple complex reasoning benchmarks (e.g., MATH500, GPQA), outperforming both single-agent and multi-agent baselines with prompting.\n2. The approach shows strong cross-task transferability, indicating practical utility when compared with existing costly multi-agent works.", "reasons_to_reject": "1. Lacks a thorough investigation into scenarios where MeMAD fails or underperforms. Since some memory module is involved, it is unclear how robust the method is under noisy memory, domain shifts, or even adv. examples, which may raise concerns about “fitting” to benchmark settings.\n1. While MeMAD combines debate and memory, the core components (e.g., self/peer reflection, semretrieval) can be viewed as adapted from prior works [include but not limited to 1,2,3,4]. The novelty lies mainly in their integration rather than in new collaborations, which may limit the perceived conceptual advancement.\n2. Although experiments are made with MAD and MoA, the paper omits some potential baselines from memory-augmented single-agent systems (e.g., MemGPT) that could offer similar benefits without requiring multi-agent.\n3. Memory retrieval is entirely based on embedding similarity, which may introduce sensitivity to encoding quality and overlook some structures in reasoning. The paper does not explore or compare alternative retrieval strategies, potentially limiting robustness.\n\n[1] Encouraging Divergent Thinking in Large Language Models through Multi-Agent Debate https://aclanthology.org/2024.emnlp-main.992/\n\n[2] Improving Factuality and Reasoning in Language Models through Multiagent Debate https://arxiv.org/abs/2305.14325\n\n[3] Corex: Pushing the Boundaries of Complex Reasoning through Multi-Model Collaboration, In COLM https://openreview.net/forum?id=7BCmIWVT0V\n\n[4] Self-Refine: Iterative Refinement with Self-Feedback", "questions_to_authors": "Some part of the paper, especially in the part about memory retrieval and feedback prompts is a little bit unclear", "extra_scores": {}}, {"review_id": "Le6dLqI05T", "reviewer": "Reviewer_nAFs", "summary": "The existing Multi-Agent Debate frameworks typically treat each debate independently, discarding valuable debate transcripts once an answer is finalized. This severely limits the system’s capacity for continuous improvement. Thus, this work attempts to incorporate long-term memory into the MAD framework in a parameter-free manner. The authors' approach involves building a structured memory system specifically designed to store evidence of successful and failed reasoning pathways. This design enables continuous knowledge accumulation, allowing LLMs to learn from and build upon collective historical experiences.\n\nThe idea is novel and highly necessary. Experimental results demonstrate that their work achieves an average performance improvement of 3.3% across several datasets. However, the main weakness of this paper lies in the lack of justification for the necessity of designing a dedicated memory framework tailored for the MAD scenario. The authors do not provide comparisons with other methods such as LangChain or MemGPT, making it difficult to assess the actual **ratio of output to input** of their proposed solution.\n\nThe manuscript currently contains numerous unclear aspects that significantly hinder proper evaluation. While I acknowledge the possibility of misinterpretation on my part, I must emphasize that - given my substantial expertise in both MAD and LLM memory systems - the majority of these clarity issues likely stem from deficiencies in the manuscript itself rather than reviewer misunderstanding. In light of these unresolved clarity concerns, I have maintained a relatively neutral scoring position at this stage. However, **I remain fully prepared to adjust my evaluation score** accordingly during the discussion period, pending satisfactory clarification of these critical issues.", "questions": "", "limitations": "", "rating": 6, "confidence": 4, "soundness": null, "presentation": null, "contribution": null, "strengths": "", "weaknesses": "", "quality": null, "clarity": null, "significance": null, "originality": null, "strengths_and_weaknesses": "", "was_revised": false, "final_justification": "", "rebuttal": "", "claims_and_evidence": "", "methods_and_evaluation_criteria": "", "theoretical_claims": "", "experimental_designs_or_analyses": "", "relation_to_broader_scientific_literature": "", "essential_references_not_discussed": "", "other_strengths_and_weaknesses": "", "other_comments_or_suggestions": "", "questions_for_authors": "", "summary_of_paper": "", "summary_of_recommendation": "", "technical_quality": "", "clarity_of_presentation": "", "potential_impact": "", "robotics_focus": "", "quality_of_limitations": "", "questions_for_rebuttal": "", "reasons_to_accept": "1) The motivation is well-justified. A key limitation of existing MAD frameworks is that they require reasoning from scratch in every debate, despite the substantial computational cost involved. Enabling models to reuse debate experiences is thus a highly important and natural direction for improvement.\n\n2) The proposed method demonstrates reasonable feasibility. In fact, similar memory systems have already been deployed in several online products, equipping LLMs with near-infinite long-term memory capabilities.\n\n3) The method is parameter-free. Notably, recent research trends focus on Inference-Time Training to inject memory into model parameters. However, such approaches are only feasible for API providers with training access. MeMAD offers a distinct and practical alternative to address memory challenges.\n\n4) Experimental results confirm that MeMAD achieves strong performance in both accuracy and generalization.", "reasons_to_reject": "Indeed, most of my **Reasons to Accept** are also my **Reasons to Reject**. \n\n1) Need for a MAD-specific memory framework: Given the availability of many mature industrial-grade LLM memory frameworks (e.g., LangChain and MemGPT), is a specialized memory framework for MAD scenarios truly necessary? Unfortunately, the paper's analysis only briefly mentions in the Related Work section: \"Nonetheless, existing memory-augmentation methods predominantly focus on general memory operations (reading, writing, retrieval), largely neglecting the structured transformation of multi-agent debate interactions into reusable reasoning experiences.\" This argument is far from convincing to me.\n\n2) Comparison with Inference-Time Training approaches: Similar to the above point, numerous recent studies employ Inference-Time Training to store memories directly in model parameters. Personally, I find this approach more fundamental and natural. At the very least, the authors should include experiments or analysis demonstrating the orthogonality between MeMAD and Inference-Time Training methods.\n\n3) Clarity in experimental setup: After extensive searching, I couldn't find clear documentation in the Experiments section about how memories were established for the benchmark datasets before inference. Perhaps I missed it, but I believe it's the authors' responsibility to clearly present this crucial step to readers. As I understand it, the first sample in the test set would have no memory, while the nth sample would have debate memories from samples 1 to n-1. This suggests that in-benchmark order and cross-benchmark order could significantly impact the reasoning results - an analysis I didn't see addressed.\n\n4) Limited complexity reduction: Surprisingly, MeMAD demonstrates only marginal reductions in complexity, which is quite puzzling. This raises questions about the practical significance of reusing past reasoning experiences. If the MeMAD framework fails to meaningfully reduce inference costs, what compelling advantages does it offer to justify its adoption over existing solutions?", "questions_to_authors": "1) Regarding Weakness 1: I would like to understand what specific advantages the MeMAD framework offers compared to existing open-source memory frameworks (e.g., LangChain, MemGPT). Alternatively, could you explain why those existing frameworks are not suitable for MAD scenarios?\n\n2) Regarding Weakness 3: Could you please clarify how exactly you built the memory index for the benchmarks in your experiments? Furthermore, what was the impact of inference order on your results?\n\n3) Regarding computational costs and API usage: Did your cost calculations include both the memory generation costs and inference time costs, or did you only consider the inference time costs?", "extra_scores": {}}]

Author rebuttal:

Meta-review: This paper proposes a structured memory for multiagent debate. Reviewers appreciated the clarity of the writing in the paper as well as the empirical effectiveness of the results. Reviewers had some concerns over the extent of the difference in the approach in the paper compared to prior work in the multiagent debate area. After taking a read over the paper, the AC found the approach to be significantly different than prior works, and is an interesting idea that makes for a good poster at the conference.
Decision: Accept