Every Response Counts: Quantifying Uncertainty of LLM-based Multi-Agent Systems through Tensor Decomposition

• URL: http://arxiv.org/abs/2604.08708v1
• Date: Thu, 09 Apr 2026 19:01:50 GMT
• Title: Every Response Counts: Quantifying Uncertainty of LLM-based Multi-Agent Systems through Tensor Decomposition
• Abstract summary: Large Language Model-based Multi-Agent Systems (MAS) consistently outperform single-agent systems on complex tasks.<n>Existing Uncertainty Quantification methods fail to address the unique complexities of the MAS.<n>We introduce MATU, a novel framework that quantifies uncertainty through tensor decomposition.
• Score: 10.210978141489003
• License: http://creativecommons.org/licenses/by/4.0/
• Abstract: While Large Language Model-based Multi-Agent Systems (MAS) consistently outperform single-agent systems on complex tasks, their intricate interactions introduce critical reliability challenges arising from communication dynamics and role dependencies. Existing Uncertainty Quantification methods, typically designed for single-turn outputs, fail to address the unique complexities of the MAS. Specifically, these methods struggle with three distinct challenges: the cascading uncertainty in multi-step reasoning, the variability of inter-agent communication paths, and the diversity of communication topologies. To bridge this gap, we introduce MATU, a novel framework that quantifies uncertainty through tensor decomposition. MATU moves beyond analyzing final text outputs by representing entire reasoning trajectories as embedding matrices and organizing multiple execution runs into a higher-order tensor. By applying tensor decomposition, we disentangle and quantify distinct sources of uncertainty, offering a comprehensive reliability measure that is generalizable across different agent structures. We provide comprehensive experiments to show that MATU effectively estimates holistic and robust uncertainty across diverse tasks and communication topologies.

Related papers

• Semantic Uncertainty-Guided Orchestration in Hierarchical Multi-Agent Systems [11.562923882714095]
  This paper introduces a semantic-uncertainty-guided orchestration approach, HASSUM.<n>It estimates uncertainty using semantic entropy and semantic density, which measure trust at the level of answer semantics rather than output probabilities.<n>Because the approach operates independently of any particular agent architecture, it can be integrated into a broad range of hierarchical and collaborative multi-agent systems.
  arXiv  Detail & Related papers  (2026-08-11T03:55:53Z)
• Prototype-Anchored Generalized Manifold Regression for Unknown-Domain Object Detection [58.25418970608328]
  We study Single-Domain Generalized Object Detection (Single-DGOD), which aims to transfer a detector trained on a single source domain to multiple unseen domains.<n>Existing methods mainly rely on simulation-driven strategies, such as data augmentation or textual prompts, to enlarge the training distribution.<n>We propose Manifold Regression with Visual-Text Dual Chain-of-Thought (MR-DCoT), which formulates unknown-domain generalization as a manifold regression problem.
  arXiv  Detail & Related papers  (2026-07-08T09:27:15Z)
• MM-StanceDet: Retrieval-Augmented Multi-modal Multi-agent Stance Detection [8.10743562429719]
  We propose Retrieval-Augmented Multi-modal Multi-agent Stance Detection (MM-StanceDet)<n>MM-StanceDet is a novel multi-agent framework integrating Retrieval Augmentation for contextual grounding, specialized Multimodal Analysis agents for nuanced interpretation, a Reasoning-Enhanced Debate stage, and Self-Reflection for robust adjudication.<n>Extensive experiments on five datasets demonstrate MM-StanceDet significantly outperforms state-of-the-art baselines.
  arXiv  Detail & Related papers  (2026-04-30T14:34:18Z)
• Chain of Modality: From Static Fusion to Dynamic Orchestration in Omni-MLLMs [84.3271821505699]
  Chain of Modality (CoM) is an agentic framework that transitions multimodal fusion from passive concatenation to dynamic orchestration.<n> operating in either a training-free or a data-efficient SFT setting, CoM achieves robust and consistent generalization across diverse benchmarks.
  arXiv  Detail & Related papers  (2026-04-16T01:21:14Z)
• Beyond Surface Artifacts: Capturing Shared Latent Forgery Knowledge Across Modalities [52.189426539304065]
  This paper introduces a paradigm shift that redefines multimodal forensics from conventional "feature fusion" to "modality generalization"<n>We propose the first modality-agnostic forgery (MAF) detection framework. By explicitly decoupling modality-specific styles, MAF precisely extracts the essential, cross-modal latent forgery knowledge.
  arXiv  Detail & Related papers  (2026-04-09T03:35:21Z)
• ResMAS: Resilience Optimization in LLM-based Multi-agent Systems [37.355345383912756]
  Large Language Model-based Multi-Agent Systems (LLM-based MAS)<n>LLM-based MAS are typically distributed across different devices or environments, making them vulnerable to perturbations such as agent failures.<n>We study the resilience of MAS under perturbations and find that both the communication topology and prompt design significantly influence system resilience.
  arXiv  Detail & Related papers  (2026-01-08T08:03:37Z)
• Explainable and Fine-Grained Safeguarding of LLM Multi-Agent Systems via Bi-Level Graph Anomaly Detection [76.91230292971115]
  Large language model (LLM)-based multi-agent systems (MAS) have shown strong capabilities in solving complex tasks.<n>XG-Guard is an explainable and fine-grained safeguarding framework for detecting malicious agents in MAS.
  arXiv  Detail & Related papers  (2025-12-21T13:46:36Z)
• Diagnosing and Mitigating Modality Interference in Multimodal Large Language Models [26.005367102695317]
  Multimodal Large Language Models can exhibit difficulty in distinguishing task-relevant from irrelevant signals.<n>We show that spurious information from irrelevant modalities often leads to significant performance degradation.<n>We propose a novel framework to finetune MLLMs, including perturbation-based data augmentation with both perturbations and adversarial perturbations.
  arXiv  Detail & Related papers  (2025-05-26T07:31:32Z)
• Uncertainty Quantification of Large Language Models through Multi-Dimensional Responses [4.505944978127014]
  We introduce a multi-dimensional UQ framework that integrates semantic and knowledge-aware similarity analysis.<n>This approach disentangles overlapping information from both semantic and knowledge dimensions, capturing both semantic variations and factual consistency.<n>Our empirical evaluations demonstrate that our method outperforms existing techniques in identifying uncertain responses.
  arXiv  Detail & Related papers  (2025-02-24T04:05:08Z)
• Progressive Multimodal Reasoning via Active Retrieval [64.74746997923967]
  Multi-step multimodal reasoning tasks pose significant challenges for large language models (MLLMs)<n>We propose AR-MCTS, a universal framework designed to progressively improve the reasoning capabilities of MLLMs.<n>We show that AR-MCTS can optimize sampling diversity and accuracy, yielding reliable multimodal reasoning.
  arXiv  Detail & Related papers  (2024-12-19T13:25:39Z)
• On the Complexity of Multi-Agent Decision Making: From Learning in Games to Partial Monitoring [105.13668993076801]
  A central problem in the theory of multi-agent reinforcement learning (MARL) is to understand what structural conditions and algorithmic principles lead to sample-efficient learning guarantees. We study this question in a general framework for interactive decision making with multiple agents. We show that characterizing the statistical complexity for multi-agent decision making is equivalent to characterizing the statistical complexity of single-agent decision making.
  arXiv  Detail & Related papers  (2023-05-01T06:46:22Z)
• Relational Reasoning via Set Transformers: Provable Efficiency and Applications to MARL [154.13105285663656]
  A cooperative Multi-A gent R einforcement Learning (MARL) with permutation invariant agents framework has achieved tremendous empirical successes in real-world applications. Unfortunately, the theoretical understanding of this MARL problem is lacking due to the curse of many agents and the limited exploration of the relational reasoning in existing works. We prove that the suboptimality gaps of the model-free and model-based algorithms are independent of and logarithmic in the number of agents respectively, which mitigates the curse of many agents.
  arXiv  Detail & Related papers  (2022-09-20T16:42:59Z)
• A Study of Syntactic Multi-Modality in Non-Autoregressive Machine Translation [144.55713938260828]
  It is difficult for non-autoregressive translation models to capture the multi-modal distribution of target translations. We decompose it into short- and long-range syntactic multi-modalities and evaluate several recent NAT algorithms with advanced loss functions. We design a new loss function to better handle the complicated syntactic multi-modality in real-world datasets.
  arXiv  Detail & Related papers  (2022-07-09T06:48:10Z)

This list is automatically generated from the titles and abstracts of the papers in this site.

This site does not guarantee the quality of this site (including all information) and is not responsible for any consequences.