Collective Intelligence: On the Promise and Reality of Multi-Agent Systems for AI-Driven Scientific Discovery

1. Introduction

“Science is a collaborative effort. The combined results of several people working together is often much more effective than an individual scientist working alone.”

—John Bardeen1

Automating scientific discovery has evolved through technological epochs driven by advancing artificial intelligence reasoning capabilities. Pioneering systems like Adam [1] proposed closing hypothesis-experiment cycles through robot scientists, while deep learning breakthroughs produced landmark achievements including AlphaFold [2] for protein structure prediction and AlphaProof [3] for mathematical reasoning, drastically accelerating discovery across diverse domains by solving previously intractable problems with high accuracy and efficiency.

The rise of large language models have further catalyzed a paradigm shift where AI systems evolved from assistive tools [4,5] toward autonomous agents [6,7,8] emulating independent researchers. These systems advance research across physics [9,10], biochemistry [11,12,13,14], causal discovery [15], social sciences [16,17], and clinical diagnosis [18,19], demonstrating broad AI-driven scientific capabilities that integrate domain knowledge with adaptive reasoning to tackle multifaceted challenges.

Recent breakthroughs of Grok-4-Heavy [20] and Gemini-DeepThink [21] explored multi-agent schema [22,23] to mirror collective reasoning dynamic of human research teams, achieving leading performance on challenging benchmarks including the International Mathematical Olympiad2 and Humanity’s Last Exam [24]. This progress signals a promising transition from single-agent systems toward MAS architectures reflecting the collaborative intelligence underlying human scientific discovery, where emergent group dynamics enable superior problem-solving through division of labor and iterative refinement.

Despite these advances, existing efforts remain fragmented across domains and isolated tasks, lacking a holistic view of MAS potential in end-to-end research workflows. We aim to address this gap through a comprehensive analysis that delineates MAS advantages in collective reasoning, confronts current limitations in practical deployment, and outline a roadmap of future work directions to bridge these gaps towards transforming MAS from promising prototypes into reliable co-scientists as research companion.

Organizational Structure 

We introduce a comprehensive application-oriented taxonomy structured around three core analytical dimensions: first, we examine the advantages of multi-agent versus single-agent systems across five key stages of the research workflow (sec:masworkflow); second, we analyze the current reality and fundamental bottlenecks limiting MAS deployment in scientific discovery (sec:realitybottlenecks); and third, we outline strategic future work directions toward realizing the full potential of MAS for science (sec:futuredirections).

Paper Selection 

Given the rapid evolution of MAS, we prioritize recent studies highlighting their unique advantages and challenges in scientific applications. We focus on works that compare multi-agent and single-agent systems, ensuring relevance by continuously updating our analysis to reflect the latest advancements in AI architectures and their scientific applications.

2. Advantages of Multi vs. Single Agent Systems in Scientific Research Workflow

We propose an application-oriented taxonomy, illustrated in Figure 1, that maps MAS capabilities to five canonical research workflow stages: literature review (Section 2.1), hypothesis generation (Section 2.2), experimental planning (Section 2.3), experimental execution (Section 2.4), and peer review (Section 2.5). In this section, we map the unique potential brought by MAS in comparison to conventional single agents to the 5 key stages.

2.1. Literature Review

The literature review stage establishes foundational knowledge by systematically identifying, evaluating, and synthesizing existing research to map current understanding and pinpoint knowledge gaps. With rapidly growing scientific papers published annually, this process overwhelms traditional human-centered approaches due to the sheer volume and complexity of interdisciplinary data.

Parallel Knowledge Processing

The fundamental advantage of MAS over single-agent systems lies in their ability to decompose the cognitive burden of literature analysis across specialized components, thereby achieving unprecedented scale and depth. This distributed architecture enables MAS to orchestrate retrieval agents with domain-specific strategies through multi-agent systematic reviews [92], while fact-checking agents ensure cross-validation accuracy [86]. Knowledge integration agents construct semantic networks revealing interdisciplinary connections, such as linking bacterial quorum sensing to neural network synchronization via knowledge graphs [84]. Scientific multi-agent systems demonstrate this capability in computational biology, enabling concurrent analysis of molecular dynamics, gene regulatory networks, and phylogenetic relationships [22]. The distributed processing architecture mitigates inherent biases through knowledge-enhanced frameworks [88] and delivers insights unattainable through sequential processing via simulated expert discussions [89].

Cross-Domain Knowledge Integration

Beyond parallel processing capabilities, MAS excel at synthesizing knowledge across disciplinary boundaries where single agents typically fail due to domain-specific training limitations. This synthesis advantage stems from specialized agents maintaining distinct perspectives while collaborating on unified integration tasks. Graph-based representations facilitate insight integration from earth science applications [90] to drug discovery [91], uncovering emergent patterns invisible to single-domain approaches. Coordination frameworks ensure comprehensive coverage through distributed teams [73], creating cohesive knowledge bases that bridge disciplinary boundaries. This interdisciplinary synthesis establishes the foundation for creative hypothesis generation by providing a unified knowledge base that transcends traditional academic silos.

2.2. Hypothesis Generation

Hypothesis generation involves formulating testable propositions to advance theoretical understanding by synthesizing diverse information sources and identifying novel research questions. This stage requires divergent thinking to explore innovative possibilities and convergent reasoning to ensure theoretical coherence, balancing creativity with rigor. Effective hypothesis generation integrates insights from multiple disciplines to propose hypotheses that are both original and empirically grounded.

Structured Theoretical Validation

MAS provide a systematic approach to hypothesis refinement through adversarial validation that single agents cannot replicate due to their inherent confirmation bias. This validation advantage emerges through debate systems that rigorously challenge propositions using large language model debates [43], while conditional effect evaluation refines hypotheses by testing diverse assumptions [45]. Adversarial frameworks emulate distributed peer review during creative phases [78], iteratively refining constructs through cooperative reinforcement learning [79]. The systematic validation process produces theoretically robust hypotheses that withstand rigorous multi-perspective critique, preparing them for interdisciplinary synthesis applications.

Interdisciplinary Knowledge Synthesis

The breakthrough potential of MAS emerges when they synthesize theoretical frameworks across previously isolated domains, creating novel scientific hypotheses impossible for single-domain agents. Building on structured validation capabilities, MAS achieve breakthrough hypothesis generation by deploying specialized ensembles that maintain theoretical rigor while exploring novel cross-domain connections. Physics agents combine quantum field theory, relativity, and condensed matter physics through multi-agent code generation [80], while principle-aware frameworks maintain coherence through critic-refine cycles [81]. Drug discovery applications demonstrate collaborative hypothesis generation through agent swarms [82] and autonomous molecular design [83]. This theoretical synthesis creates foundations for complex experimental planning by generating testable hypotheses that span multiple scientific domains.

2.3. Experimental Planning

Experimental planning transforms hypotheses into actionable protocols by designing procedures, allocating resources, and coordinating timelines across potentially multiple institutions. This stage involves selecting methodologies, determining parameters, and managing complex interdependencies, with challenges scaling exponentially as variables and constraints increase. Effective planning requires optimization under uncertainty to ensure feasibility and efficiency.

Distributed Resource Optimization

MAS fundamentally transform experimental planning by decomposing complex optimization problems that overwhelm single agents, particularly when dealing with multi-institutional and multi-domain experiments. The distributed optimization advantage stems from specialized agents handling different aspects of resource allocation simultaneously. MULTITASK [68] frameworks enable efficient allocation across heterogeneous laboratory environments, while chemical coordination systems manage complex workflows through collaborative processing [13]. Astrophysical applications [4] demonstrate adaptive resource allocation capabilities through OpenMAS platforms [69], which enable efficient resource re-allocation capable of dynamic response to experimental demands.

Real-Time Adaptive Coordination

The key advantage of MAS in experimental planning lies in their ability to maintain multiple contingency plans simultaneously and adapt instantly to changing experimental conditions. Building on distributed optimization with multiple agents, MAS achieve superior planning through redundant coordination agents that enable continuous adaptation to uncertainties that overwhelm single-agent systems, which effectively serves as autonomous error-correction in the latent space. Autonomous frameworks respond immediately to experimental changes through parallel planning systems [70], while multi-agent reinforcement learning maintains experimental resilience [59]. Fault detection MAS [27] monitor and dynamically adjust resource allocations across complex experimental pipelines, which ensures output while offering robust contingency planning that maintains experimental integrity under uncertainty.

2.4. Experimental Execution

Experimental execution implements planned protocols by coordinating instrumentation, computational resources, and data collection systems to generate empirical evidence. This stage demands precise control of experimental conditions, real-time monitoring, adaptive responses to anomalies, and integration of diverse data streams for interpretation.

Multi-System Integration

MAS provide unique advantages in experimental execution by coordinating heterogeneous systems through different model communication protocols enabled by different agents. DeepMind [54,93,94,95,96,97] demonstrate how specialized agents can leverage code generation to excel in highly specified niche tasks, while self-reflective frameworks coordinate distributed components [55]. Paper-to-code systems bridge theoretical predictions with experimental validation [44] and aim to further handle multimodal data streams [56]. El Agente [6] showcased comprehensive experimental automation in quantum chemistry via hierarchical memory framework that enables flexible task decomposition, adaptive tool selection, results analysis, and maintain detailed action trace logs across various systems.

Fault-Tolerant Execution

The distributed nature of MAS naturally enable fault tolerance through redundant agents and automatic backoff mechanisms that that addresses workflow vulnerabilities systematically. Biomedical MAS [12,57] can coordinate robotic-based lab instrumentation and analysis systems while maintaining experimental continuity even if some component collapsed during this process. Knowledge conflict [58] with inconsistent data can be resolved through discussion-driven mutual reasoning. In summary, MAS sets a foundation for fault-tolerant execution that produces more reliable experimental data with real-time peer review with error-detecting agents and provide clear logs to tract any failures to ensure experimental rigor.

2.5. Peer Review

Peer review serves as a critical quality control for scientific research by mutually evaluating methodology, analysis, contributions, and ethical compliance within the same community. This stage assesses the rigor of experimental design, statistical analysis and result interpretation. With rapidly increasing research volume and complexity in the field of artificial intelligence, the heavy pressure on human reviewers can potentially be alleviated by training trustworthy MAS for preliminary screening while leaving the most vital decision-making to seasoned experts as area chairs.

Expert Review Panels

MAS revolutionize peer review by deploying domain experts simultaneously while eliminating the scheduling and coordination challenges that plague human review panels. The specialized panel advantage emerges from MAS deploying multiple domain experts who evaluate research from diverse perspectives simultaneously, eliminating cognitive biases inherent in individual review processes. Agent-based systems coordinate methodology and expertise assessments [39], while deep review systems leverage parallel evaluation for comprehensive research scrutiny [40]. Dynamic frameworks adapt review protocols based on research complexity [41]. This specialized review approach establishes foundations for redundant validation by providing comprehensive multi-expert assessment that surpasses traditional review limitations.

Discussion-Driven Cross-Validation

The ultimate strength of MAS in peer review lies in their ability to provide multiple independent validations simultaneously, creating a level of scrutiny impossible with sequential single-agent approaches. Building on specialized panel infrastructure, MAS enhance quality control through cross-validation that systematically verifies research claims beyond single-agent capabilities. Insights from such panel discussion can further inform iterative refinement as shown in CycleResearcher [42]. Furthermore, the approach implements robust quality assurance measures, ensuring that the knowledge generated meets rigorous standards of scientific research. By meticulously scrutinizing the data and methodologies employed, the quality assurance protocols contribute to the reliability and validity of the research outcomes, thereby enhancing knowledge reliability for future cycles.

3. Current Reality and Key Bottlenecks of MAS in Scientific Discovery

Coordination and Communication Failures 

MAS face fundamental coordination challenges manifested as both naturally arising semantic incoherence and potential communication vulnerabilities in the face of adversarial injection. The coherence problem emerges from semantic fragmentation where agents trained on highly specialized niche corpora develop drastically different conceptual frameworks, leading to misinterpretation of cross-domain terminology and fragmented meta-analyses [30]. These inconsistencies could create many incompatible knowledge bases lacking semantic unity necessary for reliable interdisciplinary knowledge synthesis [58].Communication vulnerabilities further compound this challenge when adversarial agents or prompt injection exploit coordination protocols, manipulating the equal footing of multi-agent discussion mechanisms to skew research propositions through sophisticated attacks [38,77]. This conflict boils down to the fundamental trade-offs between achieving collaborative benefits from mutual discussion and robust system reliability through centralized decision-making. One direction for future investigation might explore assigning different weights to the opinion of different agents based on their robustness score against adversarial attacks and vulnerability score in the face of misalignment temptation.

Security Risks 

MAS with distributed architecture could lead to expanded attack surfaces that expose scientific workflows to sophisticated manipulation attempts absent in centralized systems. Malicious agents can inject misinformation that propagates through collaborative networks, exploiting trust mechanisms to bias literature selection and hypothesis generation processes [37,58]. The sophistication of potential attacks extends to domain-specific vulnerabilities, as demonstrated in materials research where adversaries exploit specialized knowledge to compromise research integrity [63]. Adversarial challenges manifest when malicious agents create deliberate deadlocks that manipulate resource allocation processes [65], while expanded communication infrastructure creates multiple attack vectors [67]. Defending against such attacks requires cryptographic verification and behavioral monitoring systems that approach the complexity of the scientific applications themselves [66], creating significant computational overhead while the distributed nature provides adversaries with multiple entry points for system compromise.

Integration and Scalability Limitations 

MAS struggle with highly complex theoretical integration and multi-scale dependency management that often make distributed approaches less efficient than single-agent alternatives. The integration challenge stems from coordination complexity in reconciling diverse theoretical models, producing superficial knowledge aggregation rather than genuine synthesis necessary for scientific breakthroughs [29,31]. Current architectures lack frameworks for theoretical unification [75], while coordination bottlenecks prevent deep integration [33]. Multi-scale dependency management compounds these issues when local optimization decisions create global inefficiencies that undermine experimental protocols [31], with heterogeneous resource coordination introducing complexities beyond capacity for simpler single-agent to manage concurrently [68]. Coordination conflicts can create cascading failures that propagate through distributed architectures, which is a key bottleneck to unify conceptual understanding necessary for interdisciplinary investigation.

Interoperability and Transparency Deficits 

MAS suffer from interoperability issues and decision opacity that undermine scientific accountability standards. Heterogeneous systems could create persistent inconsistencies in scientific data processing, with platform-specific variations challenging critical high-stake application fields like healthcare diagnostics [18] and biomedical research [11,12]. These inconsistencies contrast with centralized control consistency [31], requiring standardized protocols that remain an open challenge. Error detection limitations compound when distributed architectures lack concentrated domain expertise necessary for identifying subtle scientific flaws [34,35], while security integration creates additional complexity [37,52]. Decision transparency suffers from coordination complexity that makes decisions difficult to trace and audit [36], while potential coordinated bias injection compromises transparency [19]. The distributed processing that enables collaborative analysis simultaneously obscures decision tractability and methodological clarity.

4. Future Directions Towards Multi-Agent AI Scientists for Scientific Discovery

Organizational Integration 

Future MAS requires foundational organizational frameworks that transform separate agents into coherent collaboration through structured protocols [26], where knowledge graphs [84] provide promising prospects for optimization of agent network structure based on organizational theory [25] and offer grounds for co-evolutionary learning [71,79]. This organizational infrastructure enables cross-task learning based on experience [61] with critic feedback systems [72], which generates integrative hypotheses that transcend disciplinary boundaries through adaptive organizational structures that mirror human team dynamics [59]. Domain generalization capabilities [48] facilitate knowledge transfer while they preserve domain-specific expertise, supported by autonomous research-to-paper frameworks [49] and cross-team orchestration mechanisms [73]. Critical future agenda include the implementation of standardized frameworks for cross-domain integration, establishment of hierarchical validation protocols, and creation of adaptive organizational structures that balance different perspective with robust mechanism to reach consensus. These organizational foundations provide the structural basis for subsequent security infrastructure development.

Security Protocol and Infrastructure 

Comprehensive security architectures must address vulnerability challenges through consensus mechanisms while they preserve collaborative functionality essential for distributed research. The security layer should integrate peer-guard protocols against backdoor attacks [28] with fact-checking MAS that provides additional cross-validation through multiple-layered verification performed by various agents [86]. Zero-trust architectures limit attack surfaces through continuous authentication [85] with differential privacy techniques that protect sensitive research information [62]. Advanced behavioral analysis systems provide real-time anomaly detection [63], supported by deadlock prevention mechanisms that enhance system resilience against adversarial attacks [64]. Privacy-preserving mechanisms [52] integrate with manipulation safeguards to create tamper-resistant validation frameworks that maintain scientific integrity without compromising collaborative functionality [66]. Essential development initiatives include the implementation of cryptographic consensus protocols, development of behavioral monitoring systems that detect subtle manipulation attempts, and creation of security frameworks that scale with research collaboration complexity while they preserve trust relationships.

Robust Cross-Validation 

Secure infrastructure enables future MAS scientists to implement comprehensive validation that fosters mutual reasoning through active discussion and constructive critique [98], while reproducibility benchmarks ensure empirical alignment across diverse scientific methodologies [74,99]. Domain-generalization frameworks enhance robustness testing capabilities [48], which address integration challenges through knowledge synthesis protocols [47] and full-spectrum research quality evaluation frameworks [29] on output quality, efficiency and robustness across different agentic capacities that span theorists, experimentalists and computational scientists. Cross-domain knowledge discovery approaches [100] complement virtual scientific idea generation systems [101] to create comprehensive evaluation frameworks. Key development priorities include establishment of multi-modal validation frameworks that integrate theoretical consistency verification with experimental validation, and creation of automated reproducibility-checking agents that detect subtle inconsistencies across diverse scientific methodologies while they provide transparent audit trails. This validation infrastructure directly informs adaptive resource allocation through quality metrics for operation optimization decision-making.

Adaptive Resource Allocation 

Resource allocation MAS for experimental execution should employ parallel simulations that model multiple experimental protocols to identify potential conflicts and bottlenecks [59], while various agents can provide insights to inform generalizable allocation strategies across diverse experimental contexts based on cross-validation feedback [61]. Real-time monitoring enables dynamic adjustment through virtual laboratory environments [60], which ensures efficient experimental plans through comprehensive coordination [47] that surpasses single-agent dependency handling [68]. Agent-oriented planning frameworks [102] complement parallel simulation systems [103] to create robust resource management architectures. The system integrates validation quality metrics to prioritize high-confidence research directions while it maintains resource efficiency across multi-institutional collaborations. Research initiatives must focus on development of predictive resource allocation algorithms that anticipate experimental dependencies and conflicts, implementation of real-time adaptation mechanisms that dynamically rebalance resources based on changing experimental conditions and validation outcomes, and creation of virtual laboratory frameworks that enable comprehensive experimental protocol validation before physical resource commitment. This adaptive allocation system provides the foundation for standardized execution across heterogeneous environments.

Standardized Execution Frameworks 

Resource-optimized allocation enables universal integration standards that facilitate seamless coordination across heterogeneous research environments through comprehensive interoperability protocols which address system inconsistencies. The standardization framework implements semantic interoperability mechanisms that handle vendor-specific differences while adaptation protocols adjust to system variations [46]. Comprehensive cross-domain knowledge bases [48] support integrated execution systems [47] that enable failure-resistant operation, which establishes robust alternatives for complex biomedical applications [12]. Self-reflective multi-agent frameworks [55] enhance code generation capabilities while paper-to-code systems bridge theoretical predictions with practical implementations [44,56]. Fault detection systems predict and mitigate potential disruptions [27], while deadlock detection mechanisms enhance overall system resilience [65], which builds on resource allocation optimization to prevent coordination conflicts. Privacy-preserving data protection mechanisms [53] should integrate with distributed verification multi-agent reinforcement learning [52] to provide standardized interfaces that maintain consistency across diverse scientific computing environments. Development priorities include creation of universal semantic translation protocols that enable seamless communication between heterogeneous scientific systems, implementation of standardized fault tolerance mechanisms that maintain experimental continuity across system failures, and establishment of comprehensive interoperability testing frameworks that ensure consistent scientific results across diverse computational platforms.

Enhanced Error Detection and Benchmarking 

Standardized execution frameworks enable comprehensive evaluation systems that leverage distributed domain expertise for enhanced error detection which addresses transparency and reliability limitations. Detection system should integrate deep domain expertise with multi-dimensional evaluation frameworks that encompass theoretical, methodological, and empirical assessment criteria, which builds upon standardized execution protocols to ensure consistent evaluation across platforms. By synthesizing questions from real-world research papers, future benchmarks should test diverse agentic capacities [29] in high-fidelity research scenarios with experimental platforms that evaluate practical research performance [31] and multi-modal tasks that incorporate data streams of various modalities [104]. Research-based evaluation suites [30] integrate with comprehensive reproducibility assessments [32,99] bear the potential to automate executable validation to enhance reliability of research publications as a whole. Principle-aware scientific discovery frameworks [81] complement conditional effectiveness studies [45] to enhance rigor of validation results. Future MAS should establish domain-specific error detection protocols that identify subtle scientific flaws by integrating expert opinion from human reviewers [105], implement transparent decision auditing systems that provide clear explanations for human-AI collaboration [106], and create comprehensive benchmarking frameworks that measure MAS effectiveness across complete scientific research pipelines. This integrated approach further close the evaluation-verification-optimization cycle where benchmarking validation results subsequently inform iterative refinement, which ultimately establishes a robust MAS4Science ecosystem capable of providing genuine scientific discovery on open-ended research questions.

5. Vision and Conclusion

The evolution from single-agent to multi-agent systems for scientific discovery represents a foundational paradigm shift from AI as only tools to collaborative intelligence networks that take inspirations from how human researchers attempt to expand knowledge frontiers into the unknowns. As these systems mature from current promising prototypes into actually functional research ecosystems, they promise to unlock capabilities that transcend current single-agent limitations of memory and context window to perform open-ended reasoning through discussion-driven mutual reasoning and parallel exploration of vast state spaces. The vision extends beyond mere automation toward enabling new forms of human-AI collaborative inquiry where AI agents serve as autonomous co-scientists with refreshing perspective alongside human researchers.

6. Limitations

This survey provides an overview of the current reality and future prospects of MAS for scientific discovery (MAS4Science). However, certain limitations in the scope and methodology of this paper warrant acknowledgment.

References and Methods 

Due to page limits, this survey may not capture all relevant literature, particularly given the rapid evolution of both multi-agent and AI4Science research. We focus on frontier works published between 2023 and 2025 in leading venues, including conferences such as *ACL, ICLR, ICML, and NeurIPS, and journals like Nature, Science, and IEEE Transactions. Ongoing efforts will monitor and incorporate emerging studies to ensure the survey remains current.

Empirical Conclusions 

Our analysis and proposed directions rely on empirical evaluations of existing MAS frameworks, which may not fully capture the field’s macroscopic dynamics. The rapid pace of advancements risks outdating certain insights, and our perspective may miss niche or emerging subfields. We commit to periodically updating our assessments to reflect the latest developments and broader viewpoints.