MAS$^2$: Self-Generative, Self-Configuring, Self-Rectifying Multi-Agent Systems
Authors: Kun Wang
Guibin Zhang
ManKit Ye
Xinyu Deng
Dongxia Wang
Xiaobin Hu
Jinyang Guo
Yang Liu
Yufei Guo
Venue: ICLR 2026 Poster (2026)
Keywords: Multi-Agent System
LLM Agent
Primary area: foundation or frontier models, including LLMs
TL;DR: we introduce MAS$^2$, a paradigm predicated on the principle of recursive self-generation: a multi-agent system that autonomously architects bespoke multi-agent systems for diverse problems.

Abstract: The past two years have witnessed the meteoric rise of Large Language Model (LLM)-powered multi-agent systems (MAS), which harness collective intelligence and exhibit a remarkable trajectory toward self-evolution. This paradigm has rapidly progressed from manually engineered systems that require bespoke configuration of prompts, tools, roles, and communication protocols toward frameworks capable of automated orchestration. Yet, dominant automatic multi-agent systems, whether generated by external modules or a single LLM agent, largely adhere to a rigid \textit{generate-once-and-deploy} paradigm, rendering the resulting systems brittle and ill-prepared for the dynamism and uncertainty of real-world environments.
To transcend this limitation, we introduce MAS$^2$, a paradigm predicated on the principle of recursive self-generation: a multi-agent system that autonomously architects bespoke multi-agent systems for diverse problems. Technically, we devise a ``\textit{generator-implementer-rectifier}'' tri-agent team capable of dynamically composing and adaptively rectifying a target agent system in response to real-time task demands. Collaborative Tree Optimization is proposed to train and specialize these meta-agents. Extensive evaluation across seven benchmarks reveals that MAS$^2$ achieves performance gains of up to $19.6\\%$ over state-of-the-art MAS in complex scenarios such as deep research and code generation. Moreover, MAS$^2$ exhibits superior cross-backbone generalization, effectively leveraging previously unseen LLMs to yield improvements of up to $15.1\\%$. Crucially, these gains are attained without incurring excessive token costs, as MAS$^2$ consistently resides on the Pareto frontier of cost-performance trade-offs.

Published as a conference paper at ICLR 2026
MAS SELF-GENERATIVE, SELF-CONFIGURING, SELF-RECTIFYING MULTI-AGENT SYSTEMS
Kun Wang V Guibin Zhang 31 ManKit Ye
Xinyu Deng", Dongxia Wang "
Xiaobin Hu Jinyang Guo Yang Liu V Yufei Guo
NTU NUS USTC ZJU BUAA PKU Equual Contribution 1 Corresponding
Corresponding: wang. kun@ntu. sg, yfqucêpku edu. en, dxwange ju. edu.
ABSTRACT
The past two years have witnessed the meteoric rise of Large Language Model (ooM)-e multi-agent systems (MAS), which harness collective intelli- gence and exhibit a remarkable trajectory toward self-evolution. This paradigm has rapidly progressed from manually engineered systems that require bespoke configuration of prompts, tools, roles, and communication protocols toward frameworks capable of automated orchestration. Yet, dominant automatic multi- agent systems, whether generated by external modules or a single L.M agent, largely adhere rigid seeeeratee-once-nddddelloy paradigm, rendering the re- sulting systems brittle and ill-prepared for the dynamism and uncertainty of real- world environments. To transcend this limitation, we introduce MAS?,, a paradigm predicated on the principle of recursive self- self-generation multi-agent system that autonomously architects bespoke multi multi-agent systems for diverse problems. Technically, we devise pepeneratoriimplementer----ee ble of dynamically composing and adaptively rectifying a target agent system in response to real-time task demands. Collaborative Tree Optimization is pro- posed to train and specialize these meta-agents. Extensive evaluation across seven benchmarks reveals that MAS? achieves performance gains of up to 19.6% over state-of-the-ar MAS in complex scenarios such as deep research and code genera- tion. Moreover, MAS? exhibits superior cross- booss-backbone generalization, effectively leveraging previously unseen LLMs to yield improvements of up to 15.1% 15. Cru- cially, these gains attained without incurring excessive token costs, as MAS" consistently resides on the Pareto frontier of cost cost-performance trade-offs. The source codes are available https: https:////hhubb github..bbb/ggg//ggg com/ http:/eyyyyyaaaaee2.e..
tri-agent team capa-
INTRODUCTION
Inspired by the collective intelligence observed in human societies (Minsky, 1988; Li et al., 2023a), large language model (LLM) based multi-agen systems (MAS) have evolved into intricate ecosys- tems composed of multiple LLMs, tool integrations, memory modules, and communication pro- tocols (Zhuge et al., 2024; Tran et al., 2025). In contrast to single-agent paradigms that seek a monolithic, all-powerful model capable of addressing every task in isolation, multi-agent systems harness the virtues of specialization and cooperative interaction among heterogeneous agents, with substantial advances across a spectrum of task domains, including scientific discovery (Ghafarol- lahi & Buehler, 2024; Ghareeb et al., 2025), deep research (Zhang et al., 2025d; Hu et al., 2025a), complex report generation (Yi al., 2025). and collective reasoning (Ye al., 2025a).
The evolution of MAS has unfolded as clear progression from mamual design to full automation, (I) manually configured: Early frameworks such as AutoGin (Wu at., 2023), MetaGPT (Hong al., 2023), and AgentVerse (Chen et al., 2023) relied entirely on hand-crafted specifications, including prompts, agent roles, tool integrations, and communication topologies. (II) partially automated: Subsequent efforts introduced partial automation: for example, GPTSwarm (Zhuge et al., 2024) and G-Designer (Zhang et al., 2024b) automated the design of inter-agent communication topologies, while MasRouter (Yue et al., 2025) and LLM-Selector (Chen et al., 2025a) focused automating LLM routing. (III) fully automatic: Recently, the community has converged on fully automated MAS whose configurations are synthesized end-to-end and adapted online domain and instance

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Published as a conference paper at ICLR 2026
Scarch
-based
Graph-based
GPTSwarm,
SFT-based MAS- CPT, CPMSyGPT
Neol-for-Stong Scoreflow
RL-bused
Dynomi
Adjist
ADAS,
AgeniPrune,
Aflow.
G-Desigeer
AgentSquare
ExoFlow
MaAS,
External Module
Agent generates MAS
MAS generates MAS
MASS
MermnidFlowr
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Geneute
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Figure 1: The paradigm shift of automatic multi-agent system design: from external module-based MAS generation (e.g.. by GNNs, evolutionary algorithms, and search algorithms) and agent-based generation (i.e., FlowReasoner (Gao et al., 2025) and MAS-GPT (Ye et al., 2025b)) MAS where MAS recursively generates another MAS.
alike, as exemplified by ADAS (Hu et al., 2025b), MaAS (Zhang et al., 2025b), and AFlow (Zhang al., 2024c). This shift has inaugurated a phase of rapid, genuinely transformative advance.
Nevertheless, not all autonomously constructed MAS are alike. Early approaches often relied on external modules such as Bayesian optimization (Li al., 2023b), Monte Carlo tree search (MCTS) (Zhang et al., 2024c; Liang et al., 2025), or graph neural networks (Zhang et al., 2024b). While effective to some extent, these methods were confined a predefined search space of atomic operators (e.g., Chain-of-Thouu tural innovation. More recently, researchers have shifted toward enabling agents themselves to construct MAS, employing techniques such as SFT (Ye et al., 2025b), DPO (Wang et al., 2025b), and GRPO (Nie et al., 2025). These advances allow task-level adaptivity, whereby distinct sys- tems can be customized for each problem instance. Yet most of these methods still adhere "generate-onee conce-and-deplonn
(CoT)), Reflexion, Debate), limiting their capacity for architec-
paradigm, where a system is instantiated once per task and executed unchanged (Zhang al., 2025b), regardless of success failure. This paradigm proves funda- mentally inadequate, as real-world interactions between multi-agent systems and their environments are inherently dynamic and error arror-prone (e.g., network failures, tool crashes, file loss) (Cemri al., strategies are highly susceptible to collapse from
2025). Consequently, single unforeseen disruption and lack the capacity to adapt beyond their initial instantiation.
Having surveyed the evolution and inherent limitations of both external module- and agent- driven automatic agent systems, we propose a third new paradigm that inherently enables self- generation and self-adaptation of MAS. Specifically, we envision a framework in which a multi- agent system autonomously constructs another multi-agent system, which we refer to as MAS?. Unlike previous approaches, MAs² orchestrates a specialized, I.LM-facilitated meta-agent team tasked with generating, configuring. and rectifying systems in a task-adaptive and progress-aware manner. By internalizing distinct construction responsibilities across dedicated meta-agents, MAS" transcends the creative constraints of external modules and overcomes the rigidity of traditional "generate-once-ant wone--nnd-deplle aed-depooy" strategies.
In this paper, we introduce a meta multi-agent system (meta MAS) designed to instantiate our pro- posed MAS? paradigm. This system is built upon tri-agent architecture comprising + generator, an T implementor, and a rectifier, each undergoing specialized training to internalize its dis- tinct meta-generative function. Specifically, for any given query, the generator agent architects high-level, multi-agent workflow template, which outlines the sequence of agentic operations. Sub- sequently, the implementor agent instantiates this template by populating each procedural step with a concrete LLM backbone, rendering the workflow fully executable. During runtime, the rectifier agent actively monitors the execution state and environmental feedback, issuing timely corrections to the system for adaptiveness to dynamic conditions.
To facilitate training and data efficiency. we devise an offline reinforcement learning (RL) strategy for trajectory collection and optimization, termed Collaborative Tree Optimization (CTO). Within the CTO framework, the three agents collaboratively expand a decision tree representing diverse MAS configurations and execution pathways. Upon reaching terminal state, final environmental feedback signal is obtained. We then employ a path credit propagation mechanism to attribute the final outcome to the upstream decisions made by each agent. This process yields role-specific pref- erence data, which is then leveraged through relative reward-base based preference alignment algorithms to effectively specialize the distinct meta-generative function of each agent. In this way, MAS? mate-
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Published as a conference paper at ICLR 2026
rializes fully autonomous and self--daptive multi-agent system pipeline, affording robustness and precision commensurate with the demands of intricate, song-horizon task.
In brief, our contribution can be summarized as follows:
We introduce the MAS² paradigm, wherein multi-agen system itself is employed to generate diverse multi-agent systems, thereby achieving greater creativity and task adaptiveness than previous external module or agent-based generation pipelines.
Paradigm Formulation:
We instantiate MAS" through a tri-agent framework, and design collaborative tree optimization (CTO) procedure to enable efficient specialization and training of meta-agents.
Methodology Proposal:
implementor-rectifeee
Comprehensive experiments across six benchmarks demonstrate that MAS achieves (I) superior competence, surpassing state-of-the-art multi-agent systems such as MaAS and ScoreFlow by up to 9.3%; (II) Pareto-optimal cost-performance. achieving the highest performance along the cost perfoorrfrraacc frontier; and (III) cross-backbone gen- eralization, effectively leveraging unseen LLMs deliver improvements of up to 15.1%.
Empirical Evaluation:
RELATED WORK
Automating Agent Systems concerns multi-agent systems whose configurations (including agent prompting, communication protocols, tool integration, and LLM backhone selection) are automat- ically determined without substantial human intervention. Early efforts primarily relied on ex- ternal control modules: for instance, GPTSwarm (Zhuge et al., 2024) and AgentPrune (Zhang et al., 2024a) employed parameterized adjacency matrices; G-Designer (Zhang et al., 2024b) and GraphRouter (Feng et al., 2024) utilized graph neural networks; while MasRouter (Yue et al., 2025) adopted a variational autoencoder. A broader class of systems, such as ADAS (Hu et al., 2024a), AgentSquare (Shang al., 2024), AFlow (Zhang et al., 2024c), MaAS (Zhang al., 2025b), MASS (Zhou et al., 2025), and MermaidFlow (Zheng et al., 2025), similarly follow this paradigm. However, the generative capacity of external modules proved inherently constrained, giving rise to model-driven approaches for constructing MAS. MAS-GPT (Ye et al., 2025b) employs large- scale query-workflow pairs for supervised fine-tuning of system-generating agent, while Score- Flow (Wang et al., 2025b) adopts a similar strategy under DPO. FlowReasoner (Gao et al., 2025) and Weak-for-Strong (Nie al., 2025), leverage GRPO-styLe online RL (Guo et al., 2025) to train meta-agents for MAS generation. In contrast, our proposed MAS² transcends the limitations of the ggenerate-once--ndddeellooo paradigm by enabling self-adjusting and resilient construction of MAS.
Meta LLM Agents. Meta agents typically denote LLM-based coordinating entities that pro- vide meta-level guidance or generation for agent teams (Xiong et al., 2025). Most existing meta agents are instantiated directly from off-the-shelf, powerful L.MMs and are pervasive across do- mains. For instance, in deep research systems, Camel's OWL (Hu et al., 2025a), Skywork's Agen- 1Orchestra (Zhang et al., 2025d), ByteDance's s AIME (Shi al., 2025), Tencent's Cognitive Kernel- Pro (Fang et al., 2025), and several other frameworks (Bahdanau et al., 2024) all rely on a meta agent to allocate and regulate tasks among sub-agents. Analogously, in software engineering sys- tems, manager or "CEO" agents are widely adopted (Hong et al., 2023; Qian et al., 2023; Hu et al., 2024b). More recently, smaller body of work has explored explicitly training a meta agent as a leader (Estornell et al., 2025; Gao et al., 2025). Distinct from these efforts, MAS² elevates the concept further by transforming the meta agent from a single controlling entity into a meta-MAS.
RL for MAS. RL has been witnessed propel the agentization of LLMs at an unprecedented pace (Zhang et al., 2025a), substantially advancing their capabilities in reasoning, tool utilization, memory management, and beyond. Within the specific context of multi-agent systems (MAS), existing research on RL for MAS can be broadly categorized into three streams: (i) training external modules, which leave the internal parameters of agents untouched while exclusively training external control modules via RL (Zhang et al., 2025b; Wang et al., 2025a;d); (ii) training partial agents, which selectively train a subset of agents while keeping the remainder fixed, as exemplified by MIPO (Estornell et al., 2025); and (iii) comprehensively training all agents that jointly update and evolve all constituent agents, such as Sirius (Zhao et al., 2025), MALT (Motwani et al., 2024), MaPoRL (Park et al., 2025), MARFT (Liao et al., 2025), among others (Wan et al., 2025; Wang et al., 2025c; Xia et al., 2025; Liu et al., 2025).
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(a) MAS for Automatically Constructing Multi-Agent Systems
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Figure 2: The framework overview of our proposed MAS?
METHODOLOGY
Figure presents the overall workflow of MAS? inference time, MAS accepts each task query as input: the generator agent produces MAS template, the implementer instantiates its components, and the rectifier continuously monitors and adapts execution in real time Section 3.1). These meta-agents are trained under our collaborative free optimization (CTO) framework, which collects preference signals through path-level credit propagation (D Section 3.2), and subsequently leverages them for targeted meta-agent training (> Section 3.3).
3.1 META-SCHEDULING MULTI-AGENT SYSTEMS
The effective deployment an MAS for novel, complex task necessitates a departure from static, pre-defined architectures. Real-world problems are inherently dynamic, demanding framework capable of composing and adapting an MAS on-the-fly. Therefore, we introduce our MAS" to dy- namically construct bespoke MAS tailored the specific demands of a given task query, Q. We begin by formalizing the constitution of a target MAS M as four core operational components:
M PTT,),
(1)
where R = fri..... } denotes a set of allocated agents, P = {pij ri, E R} defines the communication protocol by specifying the permissible message structures Ptg between agents, T (t....., | is set of available tools (e.g., python interpreter, playwright browser), and B = (b. + rihre assigns concrete LLM backbones by to each agent.
Generator.
The process commences with the generator agent Agen. which acts as the system chitect. Given the query Q, the generator formulates a strategic blueprint for the MAS by producing workflow template, Miemp. which abstracts away the final computational resources:
Mtemp P.
Tgen (-1Q),
where geen is the generator's policy, Equation (2) provides a complete yet uninstantiated MAS plan.
Implementor.
Next, the implementor agent Aimp translates the template Miemp into a fully exe- cutable system. It defines an assignment policy, ó. that maps each role concrete LLM backbone from candidate L.M pool IL = -b...... )
6: R Th rs by(e) where Φ Timp II, Q);
(3)
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where Timp is the implementor's policy and j(i) indexes the selected backbone for agent 74. The final MAS is then composed as
M Memp @ {(rs ((r:)) ry E R),
(4)
where @ denotes the composition that integrates the backbone assignments into the workflow tem- plate, producing fully instantiated, executable MAS. We provide illustrative examples of the multi- agent systems generated by MAS in Appendix C.
Rectifier. With the MAS M instantiated. it is deployed to begin execution. The rectifier agent Arec assumes an online monitoring role to ensure system resilience. The agent is invoked if its trigger function An. which monitors the execution state 8t, evaluates to
An(s)) = 1 [C(+)) > 0c Ost)) Failure).
(5)
where 1[:] is the indicator function. The rectifier triggered if either the cumulative resource con- sumption C(s.) (e.g., token count, execution steps) exceeds budget 0c. or if the operational out- come (s)) results in an explicit failure (e.g., search engine failure, code execution error). Upon activation, the rectifier intervenes by generating modification to the current system configuration, Mr. This can range from local adjustments (e.g., re-assign proper tools, modify agent prompts) to global architectural changes (e.g., revise workflow codes). The rectifier's intervention results in:
M (-IMM, 8c),
where Tree is the rectifier's learned policy. The system then resumes execution from state 8, using the updated configuration M We give illustrative examples of how the rectifier modifies an erroneous MAS in Appendix This synergistic process ensures that the generated MAS is not only custom-buie for the task but also possesses the capacity for real-time self-correction, a key advantage for tackling long- morrzon, unpredictable problem domains.
3.2 TRAINING TRAJECTORY CURATION
To enable the specialization of our meta-agents, we introduce our Collaborative Tree Optimiza- tion (CTO) framework to construct collaborative decision tree, which serves as the formal basis for our data curation. Formally, we represent the structure as a rooted, directed tree 9q = (V, E) associated with task query Q. The vertex set V contains the query root node eq. layer of genera- tor nodes {rG}. a layer of implementor nodes {2y}. rectifier nodes { that may appear adaptively during execution, and terminal leaf nodes {Ur}. The tree is populated through sequential sampling process: the generator branches out K candidate templates from the root, the implementor expands each with N executable instantiations, and the rectifier may introduce further branches by adjusting the current MAS. A trajectory is defined as unique path from the root uq to terminal node upi
Agn
Ame
Ave VF).
(uQ
UG
To evaluate such trajectories. a performance-only metric is inadequate, as it ignores the resource effi- ciency of the constructed MAS. We therefore introduce conditional, cost-sensitive reward function R(T), which assigns zero reward to failed trajectories and evaluates successful ones by their nor- malized resource consumption:
C(T)
R(T) = [[] (7) Cnorm (T) = ITI ETET C(TY)'
(8)
where RT( E {0, denotes the success status of trajectory т, and C(T) measures raw resource consumption (i.e., LLM API cost) and the denominator rescales costs relative to the empirical mean across the trajectory set T. After assigning terminal rewards to the leaves of Go. we propagate credit backward to intermediate decision nodes via path credit propagation. The value of node e E V is defined as the expected reward of all trajectories that pass through it:
V(u) = (r s
Σ R(T).
(9)
[T(O)
TET(')
where T(u) denotes the set of trajectories traversing This Monte Carlo estimate attributes terminal outcomes to upstream decisions, thereby providing each node with principled value signal. The
resulting annotated tree Go forms structured dataset of preference-labeled decisions, which is subsequently used to train our meta-agents.
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3.3 TRAINING AND SPECIALIZING META AGENTS
With the collaborative decision tree G6 fully annotated with value estimates from the above curation process, the final objective to specialize the meta-agent policies. A naive preference alignment treating all winning actions equally would discard the rich, quantitative information captured by our node values. To leverage this, following previous relative reward-based preference methods (Gao et al., 2024; Wang et al., 2025b), we leverage a value-guider optimization process to guide the training of the meta agents within MAS?.
Value-Guided Preference Construction. First, translate the value-annotated tree into pref- erence data. For each non-terminal node v E Go representing a decision context cr. we form preference tuples. Unlike standard binary preference pairs, our tuples incorporate the quantitative margin of victory:
Dr - Owin; Wlose AV) (v, (win); ) (v, (lase) E F., }
(10)
AV = V(v')-
where of and v" are the successor nodes of awin and Ojose respectively. The term AV represents the "preference strength," a crucial signal indicating how much better one decision was than another.
Policy Specialization via Value-Scaled Optimization. Our training process refines reference policy Tee (the vanilla LLM) into a specialized policy x (a specialized generatoriimllementtrrccc tifier). Our loss function designed to incorporate the preference strength AV to modulate the gradient during training. This ensures that the model learns most from high- confidecn preference pairs where the value difference is significant, while treating low-margin pairs as less influential. The optimization objective for each policy is to minimize the following value-scaled loss:
To (awina
To (a (c)
(To) Tee) = - AV) AV log o (Blog
B log
Teet (rer( (c)
Teet (Aose (c)
(i1)
where (-) is the logistic function and ,3 is a temperature parameter. By weighting each term in the expectation by AV, our loss function directly prioritizes learning from the most unambiguous and impactful decisions identified during trajectory curation.
This optimization procedure applied independently to the generator, implementor, and rectifier, yielding three specialized policies, Tgen+ Timp- and Tec- Together, these components establish unified methodology that not only enables the dynamic construction of bespoke multi-agent systems, but also ensures their continual refinement through curated training and value-guided specialization.
EXPERIMENTS
4.1 EXPERIMENTAL SETUP
Benchmarks. To provide a comprehensive evaluation of MAS?, we employ eight benchmarks
spanning four domains: multi-hop search, including HotpoiQA (Yang et al., 2018), Bam- boogle (Press et al., 2022), and Natural Question (NQ) (Kwiatkowski et al., 2019); deep research, represented by BrowseCompt (Chen et al., 2025b), enhanced variant of OpenAl's BrowseC- omp (Wei et al., 2025) that corrects erroneous cases and enables more stable offline evaluation; code generation, comprising HumanEval (Chen al., 2021) and MBPP (Austin al., 2021 )); and mathematical reasoning, assessed with MATH (Hendrycks et al., 2021).
Baselines. The basclines against which we compare MAS? can be broadly grouped into three cate- gories: vanilla LLMs, where single models are directly evaluated, alongside prompting strategies such as CoT (Wei et al., 2023) and Self-Consistency (Wang et al., 2023a); ; handcrafted multi- agent systems, including MedPrompt (Nori et al., 2023), MultiPersona (Wang et al., 2023b), LLM- Debate (Du et al., 2023), and DyLAN (Liu et al., 2024); (partially) automated multi-agent systems, comprising ADAS (Hu et al., 2025b), MaAS (Zhang al., 2025c), AFlow (Zhang et al., 2024c), and ScoreFlow (Wang et al., 2025b). For multi-agent system baselines, we instantiate them using the relatively highest- serfoes-p QwQ-32B and GPT-4o and report the average scores.
Parameter & Model Configurations. The LLM pool available for MAS? is specified as GPT-4o and GPT-4o-mini (Liu et al., 2024), Oween.5--72biinsrrece (Qwen et al., 2025), Qwen3-14b (Yang

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Table 1: Performance comparison across 8 benchmarks across 13 baselines. Each cell reports the average of three random runs. The best/second- results are bolded/underlined.
Multi-hop Search
Deep Research
Code Generation
Math
Model
HotpotQA
Bamboogle
NQ
BrowseCompt HumanEval
MBPP
MATH
Qwen3-14B
62.2
34.4
51.9
13.1
75.6
40.8
61.5
GPT-4o-mini
64.1
26.4
70.7
13.7
87.8
46.0
51.2
QwQ-32B
64.9
39.2
61.5
2.8
51.8
82.2
65.6
Qwen-2.5-72B
66.8
31.2
63.0
2.6
82.3
76.0
55.3
GPT-4o
69.5
49.6
71.1
13.2
89.6
73.4
56.5
Avg. (Above)
65.5
36.2
63.6
9.5
77.4
63.7
58.0
CoT (GPT-4o)
66.2
52.8,16.6
68.7.6.1
16.1-7.6
90.8p13.4
75.4
58.9
SC (GPT-4o)
66.4po.9
54.4/18.2
73.6,10.0
11.8-2.3
96.3
15.1
62.0p4.0
MedPrompt
72.4pu.9
48.0p11.8
66.1.2.5
12.0~2.
92.1
69.2
59.7
MultiPersona
71.1
50.2
68. 68.4-4.8
10.1.0.6
92.9,15.5
70.4
53.7
LLM-Debate
66.911.4
54.4,18.2
70.8
15.
88.7
70.3
67.3
DyLAN
80.8,15.a
59.7728.5
72.1
15.8+a.3
90.4p13.u
77.3
65.7
ADAS
78.5,13.0
50.8,14.6
65.9-2.3
7.0,2.6
88.8,11.4
68.775.0
51.7
MaAS
83.6118.1
62.0
76.0p12.4
14.0-4.5
92.8
70.1 12.2
AFlow
77.9
59. 59.2,23.0
74.5
10.0
92.9p15.5
82.9
68 68.5+10.5
ScoreFlow
86.0
64.8
76.4
10.4-o.9
95.9
84.7
64.4
MAS
89.3,
67.2,
31.0
79.1,
15.5
19.7,
97.0,
19.6 85.1,
21.4 71.3, 13.3
et al., 2025b), and QwQ-32B (Team, 2025). We access the above models via the OpenRouter API. The backbone LLM for the generator, implementer, and rectifier in MAS are consistently set as Qwen3-8B (Yang et al., 2025b). For data curation in Section 3.2, the generator expands into four candidate Vg nodes, and each ag is further instantiated twice by the implementor, yielding two corresponding Vy nodes per branch. In the RL training in Section 3.3, set epoch_num 2, learn rate- = 5 x 10-5, and gradient wrradient.cccmul/l accumulat ionttion.iii sttattstsssssssssssssssess s = 4. We finetune the base Qwen3-8B via LoRA, with rank- and alpha 16.
4.2 MAIN RESULTS
Table compares MAS? against all single-LLM baselines within the LLM pool, as well eight representative MAS baselines, from which we distill the following key observations:
Obs. O: Existing automated MAS fail to generalize across domains. Table shows that current MAS baselines perform inconsistently across tasks. For example, MultiPesoona performs well on HumanEval (92.9%) and Bamboogle (50.2%) yet drops MATH (-4.3%) compared with single LLMs. LLM-Deeate excels on Math (67.3%) and Bamboogle (54.4%) but lacks BrowseCompt coverage. Stronger baselines like ADAS and MaAS show mixed performance: ADAS scores 78.5% on HotpotQA and 88.8 on HumanEval but falls on deep research (merely 7.0% on BrowseCompt) and MATH (51.5 11)7%), while MaAS is strong multi-hop search (83.6% HotpotQA) and code generation (92.8% on HvmanEval) yet remains weak on BrowseCompt (14.0%). Overall, these results indicate that existing MASs often excel some domains but fail to generalize consistently.
Obs. Ø: MAS outperforms both handcrafted and automated multi-agent systems. (I) Com- pared to handcrafted MAS, MAS² obtains SOTA performance across multiple domains, such as mathematics, code, and deep research. Concretely, MAS² achieves 89.3% on HotporQA, surpassing the best manual setting DyLAN by 8.5%. and outperforming McdPrompt by 16.9%. In MATH, MAS? scores 71.3%. slightly outperforming LLM-Debate by 4.0%. (II) MAS? also excels when compared automated MAS. In the multi-hop search tasks (HotpotQA, Bamboogle, NQ), MAS? achieves an average of ~ 78.5%, surpassing ScoreFlow and MaAS by 2.8% and 4.7%, respectively. In code generation, MA.S scores 97.0% on HumanEval, outperforming AFlow by 4.1%. In the MATH task, MAS² achieves 71.3%, improving by 6.9% over ScoreFllow and 2.8% over AFlow. In summary, MAS" consistently demonstrates strong performance across multiple task domains, vali- dating its robust multi-task adaptability and effectiveness.

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Obs. @: MAS generalizes to and ex- ploits previously unseen LLM back- bones. Table 2 shows that MAS ef- fectively integrates these unseen LLM backbones, significantly improving per- formance. For instance, using the vanilla Qwen3-Coder, performance on MATH is 69.7% (cost $0.54) and 32.8% on Bamboogle (cost $0.06). When MAS integrates Qwen3-Coder and GPT-5- Mini, MATH performance improves to 88.6% (cost $2.22) and Bamboogle to 82.5% (cost $0.53). Further integration with GPT-5-Mini and Gemini-2.5-Pro boosts MATH performance to 90.6% (cost $16.14) and Bamboogle to 84.0% Published as a conference paper at ICLR 2026
4.3 GENEALIZATION STUDY
Although MAS was trained using a fixed LLM pool, including models such as GPT-4o, we aim to demonstrate that its capabilities extend beyond previously seen LLMs and can effectively lever- age unseen models. To evaluate this, we augment the LLM pool at inference with three stronger, previously unseen LLMs (Qwen3-Coder (Yang et al., 2025a), GPT-5-Mini, and Gemini-2.55 (Co- manici et al., 2025)) while retaining the original models.
Table 2: Performance results when integrated with stronger unseen LLMs. "Vanilla LLM" denotes merely using one LLM backbone for testing. Checkmarks are used to denote the LLMs used.
Stronger LLMs
Dalasels
Method Qwen
GPT.5.
Gemini- 2.5-Pro
MATH
Bambooglo
Coder
Mini
(erf(%) Cost(S) Perf((%) Cosi(S)
0.54
32.8
0.06
Vanilla LLM
14.48
2.27
87.3
1.22
80.8
0.21
x
x
71.3
0.74
67.2
0.15
xas
/
88.6
2,22
82.5
0.53
x
84.8
15.47
84.2
2,57
x
90.6
16.14
84.0
2.73
(cost $2.73), offering significant gains over the vanilla LLM baseline while maintaining reasonable cost increase. These results con- finm that MAS² not only generalizes to new LLM backbones but also harnesses their strengths to achieve higher accuracy, without requiring additional fine-tuning. This validates the flexibility of ? for practical deployment.
4.4 COST ANALYSIS
To evaluate the practical viability of our method, we analyze its economic efficiency by exploring the performance-cost trade-off. As shown Figure we compare MAS against wide range of baselines to demonstrate its ability to achieve superior results without incurring prohibitive costs.
Obs. O: MAS establishes new cost-performance Pareto frontier. Figure demonstrates that MAs (indicated by the sym- bol) consistently achieves a state-of-the-art balance, delivering higher accuracy than methods of similar cost (e.g. ScoreFloow,, and significantly lower cost than methods with comparable accuracy. For instance, on Bamboogle, MAS? achieves a 12.8% higher pass rate than the expensive SC (GPT-40) baseline while being over 25x cheaper. Similarly, on NQ. it surpasses the strong ScoreFlow baseline by 2.7% in accuracy at a nearly identical cost, while simultaneously outperforming SC (GPT-40) by 5.5 points and cutting costs by over 20 times. This Pareto efficiency mainly stems from MAS?'s ability to operate dynamically at (1) the LLM level, assigning simpler tasks to smaller LLMs and reserving larger, more capable LLMs for complex reasoning, and also (2) system-level, configuring lightweight multi-agent systems for easy problems while deploying more sophisticated system architectures for challenging ones, achieving both accuracy and cost-effectiveness.
Bamboogge
3
Veats Actimies
Dueniy
Anenge Compee Qaary (5) Logi
Avrrgg Dnd per Quecy (8) Log todl
Figure 3: Cost-performance trade-off on the NQ and Bamboogle benchmark.
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Published as a conference paper at ICLR 2026
w/o Generatee
w/o Rectifier
w/o Implementor
MAS
85.6
$733
89.3
79.0
50.6
71.3
03.3
M42
45:3
MBPP
HotpotQA
MATH
Figure 4: The ablation study of MAS?.
4.5 FRAMEWORK ANALYSIS
Ablation Study. We conduct ablation studies on MBPP, HorporQA, and MATH to assess the con- tribution of each MAS component. Specifically, we consider three ablations: w/o Generator and w/o Implementor correspond to using the untrained Qwen3-8B model as the generator and imple- mentor, respectively, while w/o Rectifier denotes the removal of the rectifier, which monitors and modifies the MAS dynamically. As shown in Figure 4, removing the generator (w/o Generator) results in substantial performance drops (from 85.2% to 79.0% on MBPP, from 89.3% 86.6% on Ao)potQA). Similarly, w/o Implementor also causes notable degradation, with scores of 80.4% on MBPP, 87.3% on HotpotQA, and 65.3% on MATH, demonstrating that the trained implementor agent effectively learns to allocate LLMs optimally for different tasks. w/o Rectifier leads to in- termediate drops 3.5% on MBPP and 6.6% on MATH, indicating that the rectifier is crucial for real-time self-adjustment during interactions with the environment. These results confirm that all three modules are indispensable, and omitting any component leads to clear performance degrada- tion across multiple domains.
Case Study Figure 5 shows task-specific multi-agent systems designed by MAS" that strategically allocate different LLMs, each with customized complexity and workflow configurations.
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Figure 5: A visualization of task task-specific multi-agent systems designed by MAS on Bamboogle, HumanEval, and BrowseComp+ benchmarks.
The explanation of each devised multi-agent system is as follows:
In QA tasks, the designed MAS utilizes multiple models (e.g., Qwen series) to generate candidate answers from different perspectives. Subsequently, a premier model (c.g., Gemini-2.5-pro) takes on the critical role of evaluating these candidates deliver a final verdict. (II) HumanEval (Coding): For code generation, the MAS design embodies a division of labor. More economical, lightweight models are assigned to generate and test multiple code solutions in parallel. Finally, a top-tier model (c.g.. GPT-40) is responsible for reviewing these initial proposals and synthesizing final, high-quality program.
(I) Bamboogle (QA):
(III) BrowseComp+ (Research): the MAS designed for complex research tasks, cognitive roles are clearly demarcated. The most powerful model (e.g., GPT-4o) focuses on high-level cognitive activities such as reflective reasoning, strategic planning, and information synthesis. Meanwhile, more cost-effective models are employed to execute the mechanical, high-throughput tasks of searching and retrieving information.
Results showcase how MAS? flexibly designs heterogeneous MAS tailored to Nomain-speciif de- mands. In the QA scenario, various models generate diverse initial answers, while flagship model like GPT-4o is reserved for the final, decisive judgment. In contrast, for code generation, economical models handle parallel generation and testing, with the results ultimately being refined and elevated

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Published as a conference paper at ICLR 2026
by more powerful model. In the research tasks, MAS? dynamically coordinates models of dif- ferent tiers to handle retrieval, summarization, and reflective reasoning. is noteworthy that the MAS presented here generated solely by the generator and implementor agents. In Appendix D, we further illustrate potential errors encountered by these MAS and demonstrate how the rectifier dynamically corrects them, thereby validating the adaptive capabilities of MAS?
CONCLUSION
This work advances the frontier of automatic MAS by introducing MAS?, paradigm grounded in recursive self-generation. Unlike prevailing approaches that follow a static "gencrate-once-and" deploy" doctrine, MAS? instantiates meta-level tri-agent architecture, comprising generator, imple- menter, and rectifier agents, that dynamically composes and adaptively rectifies task-specific MAS in response real-time demands. Through the proposed Collaborative Tree Optimization, these meta-agents are trained and specialized, enabling robust adaptability and heightened efficiency. Em- pirical studies across seven benchmarks demonstrate that MAS not only achieves consistent gains over state-of of tite-oof-hh-arr the-art MAS but also scales synergistically with stronger LLM backbones.
ACKNOWLEDGMENT
This work is supported by the National Research Foundation, Singapore, and DSO National Labo- ratories under the AI Singapore Programme (AISG Award No. AISG2-RP-2020-019) and by the National Research Foundation, Singapore, and the Cyber Security Agency of Singapore under the National Cybersecurity R&D Programme (NCRP25-P04-TAICaN) This research also part of the IN-CYPHER Programme and is supported by the National Research Foundation, Prime Minister's Office, Singapore, under its Campus for Research Excellence and Technological Enterprise (CRE- ATE) Programme.
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Rui Ye. Shuo Tang, Rui Ge. Yaxin Du, Zhenfei Yin. Siheng Chen, and Jing Shao. Mas-gpt: Train- ing Ilms to build Ilm-based multi-agent systems, 2025b. URL https: /ttp//aavvvv////////////a org/ / 2503. 03686.
Ziruo Yi, Ting Xiao, and Mark V. Albert. A multimodal multi-agent framework for radiology report generation, 2025. URL https: arxiv, wwt./ogg//////aeeeee 09787.
Yanwei Yue, Guibin Zhang, Boyang Liu, Guancheng Wan, Kun Wang, Dawei Cheng, and Yiyan Qi. Masrouter: Learning route iims for multi-agent systems. arXiv preprint 1cXiv:2502.1113: 2025.
Guibin Zhang, Yanwei Yue, Zhixun Li, Sukwon Yun, Guancheng Wan, Kun Wang, Dawei Cheng, Jeffrey Xu Yu, and Tianlong Chen. Cut the crap: An economical communication pipeline for llmebased multi-agent systems. arXiv preprint arXiv:2410.02506. 2024a.
Guibin Zhang, Yanwei Yue, Xiangguo Sun, Guancheng Wan, Miao Yu, Junfeng Fang, Kun Wang, Tianlong Chen, and Dawei Cheng. G-designer: Architecting multi-agent communication topolo- gies via graph neural networks. arXiv preprint arXiv:2410. 11782, 2024b.
Guibin Zhang, Hejia Geng, Xiaohang Yu, Zhenfei Yin, Zaibin Zhang, Zelin Tan, Heng Zhou, Zhongzhi Li, Xiangyuan Xue, Yijiang Li, Yifan Zhou, Yang Chen, Chen Zhang, Yutao Fan, Zihu Wang, Songtao Huang, Yue Liao, Hongru Wang, Mengyue Yang, Heng Ji. Michael Littman, Jun Wang, Shuicheng Yan, Philip Torr, and Lei Bai. The landscape of agentic reinforcement learning for 1mm: A survey, 2025a. URL https: //tax/ivvv.///////////////.aaeva org/ abs/ /2//22500 2209 02547.
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Wentao Zhang, Ce Cui, Yilei Zhao, Rui Hu, Yang Liu, Yahui Zhou, and Bo An. Agentorchestra: A hierarchical multi-agent framework for //arxiv.org/ abs/ 2200 12508.
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A UTILIZATION OF LARGE LANGUAGE MODELS
In the preparation of this manuscript, LLMMs are utilized to refine the language and formatting of initial drafts, aid in literature review and data visualization, and facilitate information retrieval.
B PROMPT SET
This appendix details the system prompts used instruct the three core meta-agents within the MA.s framework: the Generator, the Implementer, and the Rectifier.
Prompt for the Generator Agent
aulli-agent Description: prompt instructs Generator agent. objective design high high-level, agnostic multi agent system (MAS) workflow template based on given user query.
You expert in designing multi-agent system (MAS) workflows, Your task generate Python script that defines workflow for solving a given problem. The encapsulated within
werkelew
"Kerkflow",
constraint.s:
svClasa 8t ructureet:: The entire worktlow logic must be concained within single Python class named 'Werkflew',
2. +vOutyut Formater: Your entire response
enclosed within '<graph>' and 1</ graph>> XML tags. Bo not include any text or explanations outside of these tags. execution logic
A.Core Logica::
be within 'asyae sun_werkflow
self; methed inside the 'Workflow' class.
Operaatrsse:: You muat use the predef red operators
below to corstruct
workClew, Do not invent new operators.
5. K-LLM Placeholderav: For any operator that requires an 11M, use the string placeholder ""lm__yybbo"":
specific model names like "gpt-4o".
Available Operators:
You can use the folloving operators to build workflow, They are avoilable under the "operator" module.
'operater. Ceceraatr.CCsttmmii Custom casttom(1m_cnnfii instruction, catput_type tee: general-purpese ogerater that takes instruction and produces text.
*v "operator. (llm_config, euery_Instruct ton, consext_inatructio tax: Ar operater desigaed perform seacches.
** ""operator.Prograate
sped monnffg instruction) 'v+: special ised operator for
gemersting code.
kv Reveeetter.Reieeerr (lln_config, instruction) 'ax; operator for reviewing and critiquing code er text.
toperator. Sctnrernnnaeeeeeee (1 instruction, aandidates)
operator for generating multiple candldate colutions and selecting the best one (Sell- Censistencyl.
xv 'operrterr ToroatorrTooll (tool_name)) 'vx: An operator for execucing specitic tool, like Python interpreter ("tooL_ame="pythho""
* Example Workflow Strucsure: 'python
All secessary imports are provided automatically.
claas Workflow:
def
_nett_seeff, problem: str, vakwargs):
self.problen problem
nefine your speratora here
self.planner operator, (*
Instruction=*Dreatt plan.")
self.coder operator.PPogrameer (": ("_ln_symbol", instrucsions ion the code.")
asyпс def www_work[lww(sell)
Implement the workflow logic here by calling the operstors self.planner proohle)))lem) code await self. self.coder (plan) code Now, based on these instructions, please generate workflow for the following problem.
plan
Problem:
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#F PRIMPT" (End the Frompt) xx
Chis part provides the final instruction to start generating the code.
'pyyhoo
<graph>
Remamber to start your Python code here, beginning with the class definition.
Prompt for the Implementer Agent
Description:
This prompt provided Implementer agent. the workflow template Generator and
instantiates into an executable system by assigning specific LLM backbone to each agent role from a predelined pool of available
You are an expert model selecto: for AI workflows.
+F Goal
primary goal replace every string placeholder "Lim_aymbol" in operator constructors with the most suitable model name for that operator.
+DDO NOT modiry Function signaturea add extra keyword arguments like 111m oz 'model-' to method calls.
VAGNLYAN change the first argument of operator constructors. For examplet 'operator. Custom ("Ie__("____ymmbc,", ...) should become 'operatcr.Custom (aapttto""ptttomiiii ...)
Available
Bere the available LLMs you choose Irom:
*>gpt-do-minix *xppt-4o<x
Siwwen/qwen225-2225 5-722-inssrrcct44 -*quen/qwq-300
Reference LLM Catalog
This catalog provides brief descriptions of each model to help you make informed decisien:
vogpt-4o-minivx: GPT-4p Mini snaller, faster veriant OpenAI's GPT-4o multimodal model. Tt'a optimi for lewer latency and is best suited for peak performacee.
speed prieritized **gpt-Ao+e: GPT-4p is OpenAt's flagahíp miltimodal modal, offering except lonal performance in complex reasoning, including mathematical prooIs and multi-sa multi-step derivations, Its loag-context capebilities make it ideal for preh-siniision evalustions.
Lightweigat
Swwen//qen-2.5-2266 5--52--nsssrrcttu This is 12-billlor-paranete instruetion-tuned model that excels in advanced nathematical reasoning, theorem veriflcation, and leng ferm derivatioas.
powerful open source for high
stakes reasoning tasks.
sxquen/qug 32b4к; This is medium sized, reasoning optimized model from Qwen that strong step-by-step multi-hep reasening and
Rescriptions flere's ' what each operator does:
+=Customr:; Generaces
step-by-step analysis reasoning factual qenstiens, potentially using provided context.
Annseeseenerttee Directly generates concise final ansvers for fectual QA tasks based evidence and reasening.
Scnnseebblee:: Evaluates multiple answer candidates and selects the most accurata
for factual
Revieewes: Critiques and refines sclutions for factual quessions, ensuring accuracy completeness.
Critical Instructions
.+Only replane "lim_aymbol" stringsve;
change "sperator. paths.
++Repp the exact same operato" patha** as in the Input code. prefixes.
add any Inport statementatv
Your output +-must be valid Python code.v that can be executed. final cutput *amuss only code+s, starting with 'ggraph>' ending graph>>.
Workflow Code Modify <graph>
Workflow:
def init _nit___seees (self, problem) >> Bone:
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self-prablem problem
seli.custom operater. www.scr....tmmm/o.. ("Iin self-peoblem)
self.as_ensemble operater Seerator.Scanaeeee Scansenble ( ("1in_yymba"", self.problem)
del (ur_kork[low(sel11)
This is workflow graph for the dataset.
Analyze questien anolysia = await celf.cuatom[ rinttructioon you analyze this question and provide relevant information from context?") Ensemble multiple reasoning approaches finsl_snswer await self.sc_ensemble (sclutions
context
return ("solution"; final_ansee _answer)
</graph>
Prompt for the Rectifier Agent
Rectifier agent executing encounters (e.g.. execution API timecut) or enters non productive state. The agent's lask is to anilyze the runtime context and emorr logs propose comcrete medification workflow code.
Description: This prempt
You are an expert AI workflow debugger. Your task is to analyze and fix broken Python workflow script based the provided log.
:dentify the root cause of the error in the *Brcken workflos" and correct the cade. The corrected code muat adhere to the operator datinisions provided the oor Template."
Critical instructions
AxAnalyze the Erroras: Carefully read the "Error Tagg to understand why the workčlow failed.
2. +*Consult the Tamplate*:: Use the "Norkflow Template" as strict reference for the correct operator usage, including nethod names and argument formats. exCorrect the Codevx: Modify Axonlysa the necessary lines in the "eun_workflow"
method of the "Broken Workflew" to resclve the error. Do not change the '_ini_" definitions.
mothod
4. AvOutput Formatax: Your final output must avonly.a be the corplete, corrected 'Korkflow" class, enclosed within 'egraph> "</graph> Laga Do not include explanstions, comments, or apo apologies.
PyLhon
#F# workflow Template Operator Guide
<graph>
Wockflow:
def
init _nitt_ (self, problem) None:
self, self.problem problem
self.code_generate wpwrator..ustoooeeeee....e.. CustemmoodeGeneestee ("Iim_ayrbol", self.problen
self self.problem) self.teat operacor, Test iIn_symbol", self.probler)
self.an_ensemble oparstor. ScEnsemble
asyne wut_workflowsseed))
implementation
pass </graph>
only operators you *+CustomCcceGenerraeee Generates code.
**sormat**: 'code_generase
ste)
2. *=Sensembleww: Selects the best solution from list.
#Pormet**: "sc_ensemble (soluttions List(str)) +> sezt
3. T-Tsst*:: Modifies solution using cases. **Format*+: ttet (solution st*l >> str
*+ Broken Workflow
worklwo
### Error Tog (error_log)
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C EXAMPLES OF GENERATED MULTI-AGENT SYSTEMS Published as a conference paper at ICLR 2026
tit Corrested Workflow
This section presents 5 representative multi-agent systems (represented in code) generated by MAS? for tasks of varying complexity and domain.
Workflow for Multi-hoy Question Answering (HotpotQA)
class Workflow:
_init_
self,
problem
-> Note:
sell-problem problem
self.cuatom operator. Custom ("qwen/qwen 2.5 72b instruct", self, probbem)
se sellfaa_ensemble operator sgerator.SoFnserblee ScFngenble "quen/quq-32h: self.problen
gelL.answer_generaaee operalor AnswerGenerate ("gpt mini", self.problem) self.review Reevie ("gpt- do-mini", self self.prcbler))
asyne def run (on_workfllo((se1f))
This is workflew
Generate sultiple candicate solutions with different instructions
instructions
oesesaalldd.ppllaatttion... reasoning.",
"Give a aocccccocaeeeeeea anawer with watperr_wtthheaarrrreeeeeeeeeeee htw.ih.ireo..eiimiiii....eees
solutions [awai: self, custtom
for instruction in instructions]
Use ensemble select the best solution
best caat_solution await sollf.se_enembl (solutions)
Review the best solution to improve it revienss_pelution await relfereview (dest_sclutios)
reviewed solution final_answe await self.answes mellf.anxer_ggrertte
Generate answez
return Cinal_answes
</>
Workflow for Code Generation (HumanEval)
class Workflow:
def
init
gelf,
problem
> Noze:
self.problen problem
self.code_gee [ell:cooe_generate___ operator. operalor...sttonooeeeeee... CustomnCodeGeneraleee 40 mini", self. problemm
self.code_gsnerate.
besssttor..ctttmmccddeeeeeeeeee wwwee//wwe3neeeeeeee self.
problem)
sel self.code_ 1tff.cod_generrte____ operator. www.ttrrccattmmooofaaeeeeaa ("p::rattt"g:ooooooaaa ("pp:-4o-mini* ael f. probll em) self.so_ensemble operator ScEnsemble ("epl self.psoblem!
self.test = operazor. ("gpt- to-mini", self, prable))
async def (or_
This workflow graph. solution for in (3);
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solution = await seff.ccoo_gggerreee.. (instructi onr.rrerriiso=eaasee.o.. analyza, problen_step_by, wttp:yyyysseee______eee selution_list. wtw..ist...dddooottiooooooo
ensembled soluutioo_oottion awalt ae_f.ac, aeif.ac_ensemble how.r//ooeeooooiooooooooooooooo
tested_solution awai: selé. fest ( if 'tested_sclutior"
in locals () else ceweebbled_oouuttehe
return cclutinn
Workflow for Deep Research (BrowseCompt)
Rockflow:
def
_intt!
self,
problem
None:
self.problem problem
self.custom Dearater.Custmm"ppre self.prebler)
operater. Search "p-pp----mmmi""" self.prcbler)
self.browee cperator.Erowиet Browser (nopserriggt-eeee ("get-do-mini,, sels self.problem)
581 aelf.anaxor_generaa
("apterrrrteeetptteeee ("spt-4a", self.probless)
async def run www.oorkloowwseell).. search_history set
collectted_evidence
max_iterations
iteration = quary self.probler
while iteration max_iterations:
iteration
$ soflections plan and rawrite query to optimize search reflectios promptt [ peceningaal...vvee.rrrrrr.....
repeatin previous, queriess |lis: (search (search_history) (tattooy):://ttt s/thinkks" rewritten_query await rewritten_query cewristen_querysstig.
if not revritsen_query fallback to originai problem if revriting fails or repeata
rewritten_gues
marrc_hhistory
zewtillen_quety query
mearch_history.aom (arrrrttnnn
Search search_results await searfsearch (parysrorrittteeepppyy top_k=5)
not search_results: No results found, break early
break
Extract docids and browse for fuli content bew_evidence [] for result search_remitts:
docid
("docid",
if ducid and docld not in Search_history
self.browser (decidrdocid)
if content and str(oontent) strrp(()
new_evidence, append (etr (content).strippt satr
www.evideee:
loo new evidence found, break icop break
Accumuiate evidence
col collectad_eviden "inin" join (ne__evidense) "\nin"
ze refflctiioncheee = sufficient evidence collected
Reflection:
[iteration]: ttw.ea,tth.ccculaateee
Jai, sufficiant, tociiciinntttt,,,neeee answer the, question?,,15 ye,y yes,, stop,, serchiigg__._..o..eeieee...itt >icolleeteeLeeiieeneee tttpttttte_ggeeeeeeeee querryfes__eeee sext__teeration.. </thinko Ansseerch
decision self.custon (isstmttttoooooolllllll _h_ckcck)
drcision_lower = pocisloo..ower/
"yes" decision_lover "eufficient" decision *step"
decisioo_llwer:
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break
Vinal answer generation
col seweteddevieeeeeeeeeeooo attripp))
solution await self.answergeeerräte lcontext coeee (1)
else:
solutien
"information, round, Centext"
solution
Workflow for Mathematical Reasoning (MATH)
class Workflow:
init_
self,
problem
self-problem problem
self.gid
IMPORTANT: each operator MUST be initielized with model piaceholder string "spt mini"
self.customi cperator, Custom("pt--oominn) self, gid, selé.problem)
solf.custom2 operotor Ceerotor.Custon
self.gid, polf.problem) peeatorr,Pogrammn Programmer ("qwen/qwen3-come
self.progrmmmrr operator,
self.gid, self.
problem)
relier Aperater.Reviee ("spt-S-mini" S-mini", self.gid, self.prcblem)
self.as_ensamble operator Scansemble ("sptlle""ppsSpp"""e"" ("ept-S-min"", self.gid, self.problemi
asyne def (on
graph.
Step
probiem decailed steps reasoning
analysial await self.custom" runttioction= "on""Ca__oo,,, phapo__tia_prbllllae bn_by_ each_stop?")
aaclaaaadd___ee and_exxlaintng the_reesoninng
probiem independent second anslysis) analysis2 self.custom2 (striiiiiitttttttioosssssaaaaa how toivel,the_prcblen, with_clear_ catiaarrrrssonteee nga ng far_each_step.11
Scep Explain
zeasoning
step
programmer write and execute code based first analysis poogram_soluiion - awalt velt.programmer tanalysla-analyaisl)
Scep 4: use programmer to write and execute code based second analysis progran_solurienz await gelf.pprogramer (amalyst.=-aalllatt)))
Step 5: Ensemble the two program selutions to select the best one
ensembl ad__slutionn scogran_sllution2]
f.ag_ensemble (solut (prograaaa
Step 6: Review the ensembled solution regenerate improved solution Linal_solute await self, wewierevwwwwppp (pre_solp__ooiooo ioneansemb por__soootttot-eeeeee
led_solut ion)
return final_solusion
Workflow for Natural Questions (NQ)
Workflow:
def
init
self, problem
>> Nore:
sell.problen problem
self.customi operator. Custrr ("veet//eee222222555 1-----ii2t-2---tttuett self.problem)
self.custom2 operator, Custon ("opttoo""ptttmmmii self.problem)
seiessc_esemble operater ScEnsenble ( ("gpt- 4o" self.preblem)
reviereview operator, Review wwwgw////gggg/ee
self.prablem)
aelffanaver_geneee operator. Anwwerconerate onerata...nveerrrrrreee (ngptr--mnnttttt----iii self.problem)
asyne zus_wockflow/sellee
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This is workflew graph. ene
Cenerate multiple candidate solutions with different instructions
candidatel awats self foustonl aaarerate_a_dette led_answer, th. candidate2 awall self.ouston2 ", diet, answer,
insemble select the best candidate best caat__aalution owait self.aa_ensemble [candidatal, condidate2))
Reviey the best solution to improve it peviewed_solute awalt self. rev: coest_solution
Generate final answez besed on the reviewed solution Hinal_answer awitt www..enswwr_.eeeeeeeeeeeeeeeeeee ansser_generrtee
return final
D CASE STUDIES OF WORKFLOW RECTIFICATION
We present two case studies demonstrating the Rectifier agent's capability to diagnose and repair failures in real-time.
D.1 CASE 1: HANDLING FAILED ENSEMBLE SELECTION CODING TASK(MBPP)
Original Workflow Code (Before Rectification)
Listing 1: Original workflow that crashes when ensemble selection fails.
fOriginal Python code from the "generated_ workflow "eoneratedwworrfflon" log.]
Flast workflow implicitly assumes that "sc_ensemble"" always return valid, non-empty solution.
IF the selection process fails end returns an empty string, the subsequent WeyError' crashes the system.
class Workflow:
_init_
self,
problem
None:
self.problen problem
self.code_generate. operator. Castom/icdeGsmeratee ("1m__aybbol pelf.problem)
se15.code_geuerate = www.attt...stttooeeeeeeeeeee ("1im_syaboi", problem)
sels.code_generate__ operater. wos.oooooooddeeeeeeeeeeeee ("imm_ateeem_mmmaaaaaa"aa self.problem)
sel selffaa_ensamble operator, ScEnsemble (*1lm_mmmnaaaa self ppoff.roblem)
self.teat operacor. Test "il__symbol", gell.probler)
asyne def rur run_workflow(seeef) solution_lise for in r3):
self.code_ _tt.ooooeeeate_e_l_______ (instruucionn aiaorrrrrorrss...eee.oaaaaaaaeaa
problen,careful wwl.aarffllly..ae w_g.geeate_theeecccm collte.eoo..leeooooooooo wwt.ootp..g..stteeeee
aaluiinn teolution)
collution await stfff.sseeeseebb (solut ionsssoutioooo ctsoouttttoootst)) tested_aolut
felettest (solut snnaansem led__o sod_soolttloe
return cestad_aclution
Error Message
Listing 2: Error log showing KeyError during ensemble execution.
[Reievant traceback
"error" log-]
Anslysis: The trsceback indicstes "KeySrror: mr, This within the
sc ensemble operator
when underlying selection mechaniam faila choose candioata and empty string,
which
dictionary key, ceusing
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Craceback (most recent call last): www.kkflwwexecuttrrcccoooooo
138, execute fxenute_from_cext result com_uuntilllcccm.eeee (imstancce)) ().
Pile "Ump_werk work:low.pp"" Line 26, in run_MorkTloo ensembled exait te File "eperator.py" line 231, in _call raturn lonu Keyäzror:
mappingganswer
Revised Workflow Code (After Rectification)
Listing 3: Workflow code after being corrected by the Rectifier agent.
(Revised Python code with added robustness. The Rectifier
identified that the workflow lacked failback mechanism.
4 It introduced validation step to ensure the program continves even the ansemble operator fafls.
class Wock[low:
self,
problem
self-prablem problem
self.code_gee set.-cod__genrate__. operator, CustooncodeGeeerate [in__symbo"", problem)
self.code_generatee = operatur. www.tuuuoeeeeeuunnoeoeoo ("1im_syabol", self.problem)
self.code_generate__ operator Custommocceeaneeate ("lim_e""mmm_p__"" "lim_symboi", self.problem)
self.ac_ensenta operator ScEnsemble ("1im_symbol" self.problem)
self.test operazor. Fest "llm_eymbol" self.
aayne def run wowkflow(se (self):
solution_lis for in r3)g
sall..codd_geerrastt [inatruerionn=22eeee sallye,tth,
lsst:sendist.spppedd (solutionn)
enaembled sollution await aelf. seiffaa_ensemblle (solut ion-solutiion
RECTIFIEN'S FIX START
potential ensemble fadiure. If the ensembled solution is invalid (e.g., None empty), it rabustly falls back to the first gonerated candidate. ensembled_dd in [0lution_-ist(0]
validation step to
ensemaled rehemaled_eet a
ensembled_solue
RECTIFIBR'S
teated_soll solusooouttttt awats sel test ionsansemb led_sol solutiion
return ceatad_aolution
D.2 CASE 2: RECTIFYING AN OPERATOR WITH MALFORMED STRUCTURED OUTPUT
Original Workflow Code (Before Rectification)
Listing 4: Original workflow expecting structured data from Review operator.
(Originai Python
"oonkrated_wwekff1or"" log.]
Plaw: This workflow azsumes that the 'reviewl and 'review2" operators will always return a valid structured object with specific keys. lacks a mechenism to handle where 8 returns ma formed or expty data, teading to a hard creah during data va voaiiotoon
def init
self, problem
None:
self.problem problem
self.gid None
self.customl operatoz, Custon ("1in_symbolk self.gid, proble))
self.custom² cpesator.Custon
Custon (* ("1nn_yymbo"", self self selblproblem))
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self. gelf.programmer self.reviexl opezator Revies("11s__smbool" self.gid, self.problem) self. sperator..eviex sell.aa_ensemble operator seerator.ScEngembll ScEnsemble
salf.gid, self.problem)
self.gid, self.probles
self.gid, sel pmpobleem
asyse def rur_workflow
analysis await self.customl tinstruction=....)) refined_analysite owsit self.custam2 (Instruction=". progran await self.programmer (amalysisranalysis))
This line expected to reture strectured object but fatia to do so.
tevienes_soluttinn avall self.reviewl [pp..polltiioopprrreaaoooooolod.
progrom_coluusion await pelf.programmer hnt.:m/rmmmmmlsssrrree.ooo....a. reviewad_so uo on2 awalt se r.review2 (pre_ao ao hotp_/oolsttoopprggra....ooolll on2)
dinalssolution = await self, sc_ensemb (solut lons= (reviewed solutien, reviewed_solut [on2]] return final_solu:ion
Error Message
Listing Error log showing Pydantic validation error.
8 [Raievant traceback from the "errort log.]
Analysis: error 'pydantic. velidationseror". clearly states received
fzom the 'Review" operator is missing mandatozy fields: "reviaed_ and thought".
+ this indicates that the 118 powering the operater failed to generate its outout in the
$ required structured format.
validation error for ReviewOp_Mr
Value ecror, Missing fields: ["revised_solutionn "thought") (type-value_errom input_value="l: iaput_type-dict] For further informstion visit http:://arorra.yyanti.... http/////..1//////oorrr////
Revised Workflow Code (After Rectification)
Listing 6: Workflow code after the Rectifier agent bypassed the faulty operator.
[Revised Python code demonstrating
rectification streegy.
The Rectrfier agent diagnosed that the 'Review" operators weze unzeilable. of trying bypass
the operator itself, adepted the workflow's iogic
the faulty zeview steps, ensuring the overall process can complete succesefuliy. class Workflow:
def _ini_!!
problem
self.problem problem
self.gid Nose
self.customi cperator, Luston
self.gid, self.preblem)
self.custom2 operotor
Crrrator.Custonttt Custon (ustton self.gid, self.problen)
self.programmer peeratoo.Poorammerr ("]ln_eymbol** self.gid, selt.problem)
self.reviewl Reecator.Review (eviee((1ls_symbol) self.gid, self.problem)
self.gid, self.problen
self.review2 operator, sperator.Neview(" www.eeeweieenneeeeeeee ("ireew"li..ymmbo2",
sels.sc_eneebbee operator, ScEnsemble "1in_symbol", self.gid, seoblproblem)
sayne def run_eoorfflowtteel)))
analysia = await ael f.oustot iinatrratior="...... redined_analyele await self.custem? (ineLzuction- program_solu:ionl - await self, programmer tanalysiemanalyste progron_solurient
prof.progsammes (
--- RECTIFIEN'S FIX START
The Roctifier identified that the 'raview' steps foiling format validation errora.
zecovery strategy, sodified workflow bypass unzellable review operators
24

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Published as a conference paper at ICLR 2026
f and directly pasa the initial programmatic solutions to the ensemble seage.
reviexed solutioni await self.reviest httt::/www....egg.ll.oooooooooo. Rypsased
revieved_ solutiin2 awalt self wtw.eiiiwwipreeeooooooooooooooo ptt.eeeeewi..oooooooooooooooooaa Bypassed
Final Final_soluta solution = await sel f. wew..se_aneeeeeeeeeeeeeeeeeesees www.esseaaeeeeeeeeeeeeeeeeeeeeee (solut lons- oor.gooooooo.gooooooooootto
zzogran_golation
AECTIFIER'S FIX END
return Cinal_solu.ion
D.3 CASE 3: DYNAMIC EXTENSION OF SEARCH BUDGET IN INFORMATION RETRIEVAL
complex information retrieval tasks, rigid iteration limit often prematurely terminates search process before the target information is located. In this case, the Rectifier detects that the sys- tem failed to retrieve the answer due to an exhausted search budget and dynamically extends the max iterations ion parameter allow for deeper exploration.
Original Workflow Code (Before Rectification)
Listing 7: Original workflow with restrictive iteration limit.
"yenerated_wozkkllo"" log.] Flaw: The "max max_itterationa is set to conservative value of 3. For obscure quartes, 3 rounds ineurfictent to traverse the
[Originai Python
$ necessary 1ink depti,, leading to *Not Found" reault,
class Workflow:
def _init (self, problem)
sel self-prablem problem
operator.Custom "ouetoom""ptto"", self.probler)
self.asarch operater Search ( ("qwen/"wg-32"", sel self.problem)
self.browser operator.Browser Browser
self-problem)
self.anever_genezatt
AnswerGenerate ("qwen/qwes 2.5 72b instruct",
self.problem)
aayne def run run_work low search_histor s)t
col leactad_evidennc
FAILORE POINT: iterations too sew for this complex query max itx_itarations
iteration
query self.prcbler.
while iteration max_iterations:
iteration ==
Reflection: pian and resrite quory to optimizo search refllcction prompt mnchhnkotteraat (ittattionntttraaaaaa (iteracionn s .lan.../tthine>>
rewritlen_query await custom (tw.tttttttoooeeeeeeeeeooooo
(Code logic for search and browse omitted for brevityl
search_results await seaffseareh seerrrereerr.....e.eee.e.rrr top_k=5) if not search_reaulta: break
Extract docids and browse for fuli conteat new_evidence
for zesult search_reeuils: .... [Browsing logic] ... pass
Accomulate
if new nev_evidence evidence:
collectedLevidenee "\nin", jjin (nsw_ovidence; "inkn"
seflection: check if sufficient evidence collected
decision
self.custom (isttrattttt "tri"............. wtt..le.tttoufflloooo...
if *ye** in dectston. (): break
Final answer generation
25

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Published as a conference paper at ICLR 2026
if wtwpeeeeeedeeectttrrreee.. solution ttt.e:/weeeaaisttteeeeeeeeeeeeee
else: sol iolution "In Bott ponntexxt
return solution
Runtime Log
Listing 8;
Runtime log showing "Information Not Found" due to timeout.
[Reievant from the execut ton envi ronmentt.
[System] Max iterations (3) reached, Terminating loop.
:Result] "infermation_Me__C" ta httpo/oetttttoddnnnnooooeeee
|Rectirier Diagnosfa)
Task failed due to inaufficient evidence. The agent was making progress but ran out stepa. Actions search budget.
Revised Workflow Code (After Rectification)
Listing 9: Workflow code with extended iteration limit.
[Revised Python coda.]
ffer agent fdentified that evidence insuffictent and the $ loop Lerminated due to Lhe iteration count. It increased mex__teeaation provide wider temporal window exploration.
class Workflow:
def _init isiit__seff,, problemi >> None:
self.problen problem
self.custom operator.Custone Customm"ggtt-4o" self.prohler)
operator.
""qwen/quq3225 pelt.problem)
self.browser opecator.Browsee Browser ("spt 4o mini", self.problen)
ae2l.answer_genezat
aperator.Answereeee
"ween/gwen 2.5-72b 1---2--innstuuet
self.prablem)
asyne def (ot.lllllloo
search_ history
s)t()
collected_evidenn
RECYIFIER'S
Increased max iterationg from 3 to 6 to allow deeper search.
max max ite_iirrsssons
RECTIFIER'S END
iteration
query self self.prcbler.
while iteration max_iterstlons:
iteration
(Standard search and browse loop continges]
paes
answer generation
if col dettteddeedeeeoeeattrrreee solution awit self.answer_gereeeee ott.:aarrrrccccttttttoooooeeeeoe strnc
else:
solution
return solution
D.4 CASE 4: UPGRADING MODEL CAPACITY TO HANDLE CONTEXT OVERLOAD
In deep research tasks involving extensive information retrieval, lightweight models often fail to generate valid response due token limits or an inability to filter noisy web content. In this case, the Rectifier diagnoses a silent failure ("Empty Response") and applies a dual-fix strategy: upgrading the LLM backbone and injecting strict prompt constraints improve robustness.
26

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Published as a conference paper at ICLR 2026
Original Workflow Code (Before Rectification)
Listing 10: Original workflow using a lightweight model for final answer generation.
joriginai Python
"generated pererratdd__orrriooo workrlow* log.)
Flawr The workflow assigns "got-4o mini" to the "AnawerGenerate" operačoz, SronseComps,
accumulated evidence t that everwhelm smaller madels, causing them to return empty respenses. Workflow:
contains noisy artifacts
def _intt (self, problem) -> None:
self-prablem problem
self.cuatom Ceerator.Custommgpgte
self.probler)
self.search operator. Search ("spt 4o mini", self. self.probler)
seli.browser
self.problem)
FATLORE POINT: Neak madel assigned to final generation
self. aett-aaaaerr_eeerdee
seerator.AnaerrGeneree ("ppaatiipgtttomnnt" self-problen)
aayne def run workf (sel)))
... [Initialization omicted) ...
[allected_evideneee
[Search and browsing loop omitted)
Final generation
fails silently context
sclution await self self.aaswer_geneeste (context reottssttolleeetsseeeeeeeee stripp
solution "In"ormation Nottattooonnnneoooee wtw.rddddddcomme
return solution
Runtime Log
Listing 11: Runtime log capturing the silent failure of the LLM API.
[Reievant from the execution enveentttt..
(Ansvartenarata] Using model: gpt-4o-mini with max max_tokens: 4095
Annserrene NARNTNC:
returned empty response!
SystemBcroz] ineErros
generation failed. Received empty response.
Revised Workflow Code (After Rectification)
Listing 12: Workflow code after Rectifier intervention (Model Upgrade + Prompt Fix).
[Revised Python code with Rectifier intervention.]
$
The Rectifier agent identified the foilure as cspacity issve. It applicd two fixes: Upgraded backbone to 'apt-4a'.
8 2. Modified the input context with strict prompt wzagpez to handle noise.
Workflow:
def init inntt_ (self, problem! -> None:
self.problen problem
self.custom operator.Custem ("gpttdd""" self.problem)
self self.asarch = Seerator.Searde ("gpt-4o-miri", self.probler) (*quen/qwen3-144"" selflproblem)
self.browser operator,
Switchedd from 'gpt 4o mini' to 4o' for better long context hendling, ("p selfeproblem)
self. anl..aaaneee_eeeeeee
RECTIFIER'S FIX END
agyne [Search loop omitted]
(self):
Final generstion
eeidence. if coliecteeeeeiidenne striip))
RECTIFTER'S FTX PROMPT REFINENENT
$ The Rectifier injected wrapper to heip the mode! focus.
refined [einne__conext-
*IMPORTANT, INSTRUCTION LtgpsssSCCII....ogggooaaL all, kLn, tag,,,an irrelevant, headers.."" irreievant,heeders...
27

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Published as a conference paper at ICLR 2026
"Foous, only, on, factuni,elatss.. \nkn" 2 wolllecee_evidenneeeeei...
solution oe else:
selution MubforattioooNee_PPardd......
return solution
28


Reviews:
[{"review_id": "W0zjCM5Je7", "reviewer": "Reviewer_u18B", "summary": "This paper introduces a novel paradigm for multi-agent systems (MAS) that enables recursive self-generation, automatic configuration, and real-time rectification to handle real-world uncertainties beyond traditional \"generate-once-and-deploy\" approaches.\n\nContributions include:\n\n- Shifts from the traditional \"generate-once-and-deploy\" paradigm to a dynamic process that generates and adjusts MAS configurations iteratively, enabling adaptation to real-world uncertainties.\n- Introduces a three-meta-agent architecture: Generator for creating high-level workflow templates, Implementer for assigning LLMs to roles, and Rectifier for real-time monitoring and fault correction.\n- Proposes the Collaborative Tree Optimization (CTO) framework, an offline reinforcement learning method using decision trees, reward propagation, and preference alignment to train and optimize the meta-agents for enhanced performance and efficiency.", "questions": "- Section 3.2 notes that training the CTO requires efficient value function computation. I am concerned that the current approach to preference dataset construction may not be scalable. I suggest providing a more detailed explanation of the sampling strategy to clarify how computational overhead was managed, especially with an eye toward scaling the dataset size.\n- In section 3.2 equation 11, the role of \\delta V in adjusting the per-sample loss requires further clarification. It would be important to understand the theoretical motivation or mathematical derivation behind this design.", "limitations": "", "rating": 4, "confidence": 3, "soundness": 3, "presentation": 3, "contribution": 3, "strengths": "- A paradigm for dynamic and scalable MAS: A self-generative, adaptive multi-agent system that evolves iteratively is presented as an alternative to existing MAS frameworks, aiming to address their noted limitations in scalability and adaptability.\n\n- Generalization via CTO: The experimental results report that meta-agents optimized through the CTO framework exhibit generalization to unseen LLMs, with performance improvements of up to 15.1%, suggesting a degree of practical transferability.\n\n- A concise three-agent architecture: The system is built around a three-meta-agent design (Generator, Implementer, and Rectifier), which is characterized by its modularity and is proposed to be amenable to future extensions.", "weaknesses": "Writing\n- In section 3.1 you defined $\\mathcal{T}$ as the set of available tools, but in section 3.2 $\\mathcal{T}$ means the trajectory set\n- To better assess the reward function design in Section 3.2, a more complete definition of $C(\\tau)$ (\"raw resource consumption\") would be beneficial, as the current article and its appendix lack the necessary details and examples.\n\nUnderstanding of the paper:\n\n- The lack of information regarding the training datasets undermines the evaluability of the reported benchmark results. I think it would be beneficial if the authors provide a detailed description of the source and composition of all problem datasets used in their training data.\n- In Table 2 of Section 4.3, the score of 68% achieved by Gemini 2.5 Pro on the MATH benchmark appears notably low. Given that the official Gemini reaches 21.6 on HLE and 88 on AIME [1], there is a discrepancy that requires clarification. Further details regarding the evaluation of Gemini 2.5 Pro need to be clarified. \n\n[1] https://deepmind.google/models/gemini/pro/", "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": "", "reasons_to_reject": "", "questions_to_authors": "", "extra_scores": {}}, {"review_id": "0F19RLsp6q", "reviewer": "Reviewer_Bhu1", "summary": "This paper introduces MAS2, a novel multi-agent system (MAS) framework endowed with self-generation, self-configuration, and self-correction capabilities for dynamic construction and optimization of multi-agent systems to address complex and evolving task scenarios. MAS2 employs a recursive approach involving a team of agents—generators, executors, and correctors—to build multi-agent systems, thereby overcoming the rigidity of traditional \"generate-once-and-deploy\" paradigms. Additionally, it leverages collaborative tree optimization (CTO) to enhance the task adaptability and efficiency of meta-agents. MAS2 demonstrates exceptional generality and efficiency across multiple benchmark tasks, particularly in complex domains such as code generation, deep research, and mathematical reasoning.", "questions": "1. While the paper demonstrates MAS2's effectiveness on academic benchmarks, it lacks real-world deployment or interaction scenarios. Is it possible to verify the effectiveness of the method in a real-world dynamic environment, or to provide some examples of real users using multi-agent systems?", "limitations": "", "rating": 8, "confidence": 3, "soundness": 3, "presentation": 3, "contribution": 4, "strengths": "1.The MAS2 paradigm introduces a novel approach to multi-agent systems through recursive self-generation and self-adaptation. The use of a generator-implementer-rectifier tri-agent architecture is a significant advancement over traditional \"generate-once-and-deploy\" frameworks, enabling dynamic and robust system design.\n2.The paper conducts extensive experiments across seven benchmarks, demonstrating MAS2's superior performance in diverse domains such as multi-hop search, deep research, code generation, and mathematical reasoning. The performance improvements (up to 23.8% over SOTA) and cost-efficiency analysis provide strong evidence for the practical utility of MAS2.\n3.MAS2 effectively integrates unseen large language models (LLMs) without additional fine-tuning, showcasing its flexibility and scalability. This feature is especially valuable for real-world applications where new models are frequently introduced.", "weaknesses": "1. The tri-agent architecture and collaborative tree optimization (CTO) framework, while innovative, introduce significant complexity. The practical challenges of implementing and maintaining such a system, especially in resource-constrained settings, are not thoroughly addressed.\n2.Although the rectifier agent is designed to handle runtime errors, the paper provides limited quantitative analysis of how often and effectively the rectifier resolves failures.", "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": "", "reasons_to_reject": "", "questions_to_authors": "", "extra_scores": {}}, {"review_id": "DcMpX0CtAC", "reviewer": "Reviewer_UFEM", "summary": "This paper introduces MAS2, a new paradigm for automatic multi-agent system (MAS) construction powered by large language models (LLMs). Unlike prior methods that generate a system once and execute it statically, MAS2 enables a meta MAS to recursively generate, configure, and rectify MASs.", "questions": "CTO calibration: How are Rₚ(τ) (success condition) and θ₍C₎ (cost threshold) determined? Are they fixed, learned, or task-adaptive? What is the effect of varying them?", "limitations": "", "rating": 4, "confidence": 4, "soundness": 3, "presentation": 3, "contribution": 2, "strengths": "1. Novel Recursive MAS Paradigm: The paper introduces MAS2, a self-generative, self-configuring, self-rectifying multi-agent paradigm, moving beyond the dominant “generate once and deploy” paradigm. This conceptual shift is original and timely for dynamic real-world settings.\n2. Agent Meta Architecture: The generator–implementer–rectifier division of labor provides clear modularity: generator → architectural workflow; implementer → LLM backbone assignment; rectifier → online adaptation\nThis creates specialization, interpretability, and controllability.", "weaknesses": "1. In the Introduction, the authors claim that previous methods based on Bayesian optimization, GNNs, and MCTS suffer from the limitations of predefined search spaces and atomic operators. However, in Figure 2, the workflow template of MAS² also appears to be composed of similar atomic operators such as \"manager\", \"coder,\" and \"summarizer.\" This design seems structurally indistinguishable from earlier works, weakening the claimed motivation and novelty.\n2. The paper lacks crucial implementation details: How large is the dataset used to train the generator, implementer, and rectifier agents? Were any of the training tasks reused during evaluation, or are they strictly separated?\n3. On the BrowseComp+ benchmark, MAS² shows only a 3.6% improvement over the CoT baseline, does it suggests limited effectiveness when handling high-difficulty, complex reasoning tasks.\n4. Inconsistent Benchmark Reporting: The number of benchmarks is contradictory — abstract (7), §4.1 (8), and contribution summary (6), while Table 1 actually contains seven columns. This inconsistency undermines clarity of experimental coverage.", "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": "", "reasons_to_reject": "", "questions_to_authors": "", "extra_scores": {}}, {"review_id": "LLvAky6ZwI", "reviewer": "Reviewer_3oGm", "summary": "This paper proposes MAS², a \"recursive self-generative\" multi-agent paradigm built on a tri-agent architecture: a generator designs high-level workflow templates based on tasks, an implementer assigns specific LLM backbones to turn templates into executable systems, and a rectifier monitors real-time operations and dynamically rectifies configurations. The paper also introduces the Collaborative Tree Optimization (CTO) framework, which specializes meta-agents by collecting trajectory data via a collaborative decision tree, generating preference signals through path credit propagation, and optimizing policies via value-guided preference alignment. Experiments show the effectiveness of the proposed model.", "questions": "1. For the rectifier’s threshold θ_C, could you clarify whether it is set manually or adaptively, and provide sensitivity analysis on how θ_C adjustments affect system performance?\n\n2. Regarding the CTO framework’s fixed parameters (4 generator templates, 2 implementer instantiations), could you explain the rationale for these values and test results of other parameter combinations?\n\n3. Since experiments lack dynamic interference simulations, could you add tests (e.g., random tool failures) to verify MAS²’s robustness in unstable environments?\n\n4. For engineering deployment, could you provide details like meta-agent training resources  and executable MAS generation latency?", "limitations": "", "rating": 6, "confidence": 4, "soundness": 3, "presentation": 2, "contribution": 3, "strengths": "1. The Collaborative Tree Optimization (CTO) framework addresses meta-agent training challenges (e.g., ambiguous rewards, low data efficiency) via collaborative decision trees, path credit propagation, and value-guided alignment. It attributes terminal feedback to upstream decisions and prioritizes high-impact preferences, enabling efficient specialization of each meta-agent.\n\n2. Experiments cover 8 benchmarks across 4 domains (multi-hop search, deep research, code generation, mathematical reasoning) and 13 baselines. \n\n3. The overall framework seems reasonable and effective.", "weaknesses": "1. Rectifier Threshold Lacks Clear Justification: The rectifier’s trigger relies on a resource budget threshold θ_C (for excessive consumption), but the paper does not explain how θ_C is determined, whether it is set manually or adaptively. No sensitivity analysis is provided to show how θ_C adjustments affect system performance, leaving uncertainty about its practical flexibility.\n\n2. CTO Parameter Selection Lacks Optimization Proof: The CTO framework uses fixed parameters (e.g., 4 candidate templates for the generator, 2 instantiations for the implementer) in experiments. The paper fails to justify why these values are optimal or how changing them (e.g., more templates) might impact training efficiency or trajectory quality.\n\n3. Dynamic Interference Scenarios Are Undertested: While MAS² claims to handle real-world dynamism (e.g., tool crashes), experiments only use static benchmarks. No tests simulate dynamic disruptions (e.g., random tool failures during execution), making it hard to fully verify its robustness in actual unstable environments.\n\n4. The paper lacks key engineering insights for real-world use, such as the computational resources (GPU type, training time) needed for meta-agent training or the latency of generating executable MAS. These omissions hinder practical deployment planning for industrial users.", "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": "", "reasons_to_reject": "", "questions_to_authors": "", "extra_scores": {}}]

Author rebuttal:

Decision: Accept (Poster)