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Open research questions in Multi-Agent Systems and Negotiation

121 gap statements mined from Multi-Agent Systems and Negotiation papers in our 7.9M-paper local library, which holds 888 papers on the topic — drawn mostly from each paper's own stated research gap, future-work, challenge and limitation notes, and its abstract. The ones listed below are a selection still marked open; each names the study that raised it, with a DOI link where the paper has one.

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What the literature leaves open

  • Despite these advances, large-scale deployment remains constrained by a shared set of structural limitations. Current agentic AI systems rely heavily on non-deterministic, language-based reasoning that is opaque, difficult to audit, and poorly aligned with formal verification requirements. In high-stakes and regulated domains, this lack of interpretability is not a secondary concern but a primary barrier to adoption.

    Agentic AI: A Perspective on Architecture, Frameworks and Applications · 2026 · DOI

  • This review examined whether recurrent limitations in contemporary agentic systems point to a deeper structural disconnect between probabilistic reasoning, persistent state management, orchestration, and execution control. Methodologically, the study was conducted as a five-phase structural diagnostic review reported with reference to the PRISMA-ScR principles.

    The Governance Gap in Contemporary LLM-Based Agentic Systems: A Structural Diagnostic Review · 2026 · DOI

  • Finally, selected low-risk operational domains could be evaluated prospectively in live trials using predefined metrics such as task completion time, query-resolution time, protocol-deviation detection, data completeness, number of unresolved discrepancies, cost per operational task, audit-trail completeness, and user workload.

    Towards a multi-agent Clinical Trial Center framework to support modern clinical trials · 2026 · DOI

  • It remains an open question whether uncertain rules, our other main focus, can be applied so directly and fruitfully to real contexts. The results and open problems of this subsection are summarised in Figure 2. Expressivity results and open problems about ISLAFs are summarised in Figure 3. As for immediate future work, the current paper leaves many open questions.

    Expressivity Results for Structured Argumentation Frameworks with Uncertain Rules and Premises · 2026 · DOI

  • Future work will focus on expanding retrieval resources to include emergency and pediatric datasets, improving terminology normalization for medications and supplements, calibrating policy thresholds using clinician feedback, and conducting prospective studies in real clinical environments.

    TriAgent: An Adaptive Multi-Agent Architecture for Crisis Clinical Decision Support Under Incomplete Information · 2026 · DOI

  • Current agent systems research has focused primarily on agent reasoning capabilities, with less attention paid to the engineering challenges of building reliable production pipelines. The deployment of autonomous AI agents in enterprise environments presents significant architectural challenges.

    Design and Implementation of Agentic AI Pipelines for Enterprise Decision-Making Architecture Patterns for Production Systems · 2026 · DOI

  • The industry is recognizing the need for competing trust and authorization models, verifiable intent, and cross-platform orchestration. Autonomous systems are scaling faster than the coordination structures required to govern them.

    Agentic Convergence Infrastructure™ (ACI™) · 2026 · DOI

  • The paper identifies the need for governance by construction to ensure safety and compliance. The gap is the lack of a principled governance layer for generalist computer-using agents.

    Governance by Construction for Generalist Agents · 2026 · DOI

  • A deeper reflection would distinguish which subquestions remain unresolved, such as whether dp53 directly regulates dMyc transcription, whether dMyc regulates dp53, whether the observed relationship is limited to apoptosis or cell competition, and whether available evidence supports direct regulation or only indirect interaction.

    Time to REFLECT: Can We Trust LLM Judges for Evidence-based Research Agents? · 2026

  • These challenges point to the need for reinforcement mechanisms, memory consolidation, and more robust selection strategies.

    Self-Evolving Software Agents · 2026 · DOI

  • Our primary contribution has been to bridge the gap between the high-level theory of agentic AI and its practical, high-stakes application within the field of engineering. To achieve this, we first establish a clear and necessary taxonomy, distinguishing the unique capabilities of agentic AI from its predecessors.

    Agentic AI systems in electrical power systems engineering: current state-of-the-art and challenges · 2026 · DOI

  • The central challenge of agentic AI is not autonomy itself, but accountable transition. Current agentic AI systems often lack explicit mechanisms for governing the transition from exploratory reasoning to operational effect.

    Accountable Agents - Declarative Control Architecture for Operational AI Systems · 2026 · DOI

  • Agentic AI is increasingly discussed as a prospective participant in business processes, yet it remains unclear how current implementations relate to this vision.

    Agentic AI In Business Processes: A Conceptually Grounded Analysis Of Open-Source Implementations · 2026

  • EinsteinArena provides agents with a live set of open problems, each with a solid verifier, public leaderboard, and problem-specific discussion forum where agents can ask questions and share insights.

    Harnessing the Collective Intelligence of AI Agents in the Wild for New Discoveries · 2026

  • Multi-agent LLM systems are increasingly deployed in settings where individual agent failures can cascade across the collective, yet the mechanisms by which such failures propagate—and the structural conditions under which they are contained or corrected—remain poorly characterized.

    Failure Propagation and Self-Correction in Multi-Agent LLM Systems: How Deliberative Consensus, Credit Assignment, and Architectural Isolation Jointly Determine Systemic Reliability · 2026 · DOI

  • The paper suggests that future research should focus on evaluating the performance of the proposed system in different domains. The paper suggests that future research should focus on improving the accuracy and reliability of forecasts using the proposed system. The paper suggests that future research should focus on developing new benchmarks for evaluating the performance of forecasting systems.

    Knowledge-Oriented Hierarchical Multi-Agent Intelligent System for Scenario Time Series Forecasting Based on LLM · 2026 · DOI

  • The paper identifies a need for a new approach to forecasting that can improve the accuracy and reliability of forecasts.

    Knowledge-Oriented Hierarchical Multi-Agent Intelligent System for Scenario Time Series Forecasting Based on LLM · 2026 · DOI

  • Proposed evolution of trial-guidance documents for a multi-agent CTC As coordinating centers begin delegating routine tasks to autonomous agents, traditional meta-documents, such as protocols, DSMB and CEC charters, and Data Management Plans, may no longer be ideal.

    Towards a multi-agent Clinical Trial Center framework to support modern clinical trials · 2026 · DOI

  • How to score partial progress on a multi-day task in a way that is both reproducible and not gameable remains an open methodological question, and one that the static, single-shot benchmarks currently used to validate self-evolving systems…

    LLM-Based Multi-Agent Orchestration: A Survey of Frameworks, Communication Protocols, and Emerging Patterns · 2026 · DOI

  • Further development of ACI and its components, such as QBD and CNS. Exploration of the applications and implications of ACI in various domains.

    Agentic Convergence Infrastructure™ (ACI™) · 2026 · DOI

  • We view AgentFuel as a first but significant step in advancing evals for timeseries analysis agents. We conclude by acknowledging several limitations and directions for improvement.

    Generating Expressive and Customizable Evals for Timeseries Data Analysis Agents with AgentFuel · 2026 · DOI

  • To validate SEED as a finished system - To estimate population effects - To develop governance principles for responsible use of SEED-assisted…

    Agents for Experiments, Experiments for Agents: A Design Grammar for AI-Enabled Experimental Science · 2026

  • The lack of a compact language for describing experimental conditions - The need for a framework that can guide AI-assisted generation of candidate experimental…

    Agents for Experiments, Experiments for Agents: A Design Grammar for AI-Enabled Experimental Science · 2026

  • We further outline open challenges for harness engineering, including evaluation beyond final task success, verification under incomplete feedback, regression-free harness improvement, consistent shared state across multiple agents, human oversight for safety-critical actions, and extensions to multimodal environments.

    Code as Agent Harness · 2026

  • Incorporating action model learning into RASO. Reducing reflection overhead. Extending the framework to more complex domains.

    RASO: Role-Aware Shared Reflection for Multi-Agent Orchestration in E-Commerce Long-Horizon Planning · 2026 · DOI

Most-cited papers in Multi-Agent Systems and Negotiation

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Open questions raised by papers listed on this page include: (1) In high-stakes and regulated domains, this lack of interpretability is not a secondary concern but a primary barrier to adoption (“Agentic AI: A Perspective on Architecture, Frameworks and Applications”, 2026); (2) The gap is the lack of a principled governance layer for generalist computer-using agents (“Governance by Construction for Generalist Agents”, 2026); (3) The paper suggests that future research should focus on evaluating the performance of the proposed system in different domains (“Knowledge-Oriented Hierarchical Multi-Agent Intelligent System for Scenario…”, 2026); (4) Agentic AI is increasingly discussed as a prospective participant in business processes, yet it remains unclear how current implementations relate to this vision (“Agentic AI In Business Processes: A Conceptually Grounded Analysis Of…”, 2026). 121 gap statements have been mined from Multi-Agent Systems and Negotiation papers in our 7.9M-paper local library, which holds 888 papers on the topic; the gaps come from whichever of those papers state one. They are mostly the research gaps the authors state and the papers' abstracts, plus future-work, limitations and challenges passages. The ones listed below are a selection still marked open; each names the study that raised it, with a DOI link where the paper has one, so you can read the original claim in context.

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