Active Asymmetric Multi-Agent Multimodal Learning under Uncertainty

• URL: http://arxiv.org/abs/2602.04763v1
• Date: Wed, 04 Feb 2026 17:01:31 GMT
• Title: Active Asymmetric Multi-Agent Multimodal Learning under Uncertainty
• Authors: Rui Liu, Pratap Tokekar, Ming Lin,
• Abstract summary: We propose Active Asymmetric Multi-Agent Multimodal Learning under Uncertainty (A2MAML)<n>A2MAML is a principled approach for uncertainty-aware, modality-level collaboration.<n>Experiments on connected autonomous driving scenarios for collaborative accident detection demonstrate that A2MAML consistently outperforms both single-agent and collaborative baselines.
• Score: 15.933557806106071
• License: http://creativecommons.org/licenses/by-nc-nd/4.0/
• Abstract: Multi-agent systems are increasingly equipped with heterogeneous multimodal sensors, enabling richer perception but introducing modality-specific and agent-dependent uncertainty. Existing multi-agent collaboration frameworks typically reason at the agent level, assume homogeneous sensing, and handle uncertainty implicitly, limiting robustness under sensor corruption. We propose Active Asymmetric Multi-Agent Multimodal Learning under Uncertainty (A2MAML), a principled approach for uncertainty-aware, modality-level collaboration. A2MAML models each modality-specific feature as a stochastic estimate with uncertainty prediction, actively selects reliable agent-modality pairs, and aggregates information via Bayesian inverse-variance weighting. This formulation enables fine-grained, modality-level fusion, supports asymmetric modality availability, and provides a principled mechanism to suppress corrupted or noisy modalities. Extensive experiments on connected autonomous driving scenarios for collaborative accident detection demonstrate that A2MAML consistently outperforms both single-agent and collaborative baselines, achieving up to 18.7% higher accident detection rate.

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