Modality-Native Routing in Agent-to-Agent Networks: A Multimodal A2A Protocol Extension

Computer Science > Artificial Intelligence [Submitted on 14 Apr 2026] Modality-Native Routing in Agent-to-Agent Networks: A Multimodal A2A Protocol Extension Vasundra Srinivasan Preserving multimodal signals across agent boundaries is necessary for accurate cross-modal reasoning, but it is not sufficient. We show that modality-native routing in Agent-to-Agent (A2A) networks improves task accuracy by 20 percentage points over text-bottleneck baselines, but only when the downstream reasoning agent can exploit the richer context that native routing preserves. An ablation replacing LLM-backed reasoning with keyword matching eliminates the accuracy gap entirely (36% vs. 36%), establishing a two-layer requirement: protocol-level routing must be paired with capable agent-level reasoning for the benefit to materialize. We present MMA2A, an architecture layer atop A2A that inspects Agent Card capability declarations to route voice, image, and text parts in their native modality. On CrossModal-CS, a controlled 50-task benchmark with the same LLM backend, same tasks, and only the routing path varying, MMA2A achieves 52% task completion accuracy versus 32% for the text-bottleneck baseline (95% bootstrap CI on \DeltaTCA: [8, 32] pp; McNemar's exact p = 0.006). Gains concentrate on vision-dependent tasks: product defect reports improve by +38.5 pp and visual troubleshooting by +16.7 pp. This accuracy gain comes at a 1.8\times latency cost from native multimodal processing. These results suggest that routing is a first-order design variable in multi-agent systems, as it determines the information available for downstream reasoning. Comments: 14 pages, 4 figures (TikZ). PDFLaTeX. Supplementary code and experiment artifacts: this https URL Subjects: Artificial Intelligence (cs.AI) Cite as: arXiv:2604.12213 [cs.AI]   (or arXiv:2604.12213v1 [cs.AI] for this version)   https://doi.org/10.48550/arXiv.2604.12213 Focus to learn more Submission history From: Vasundra Srinivasan [view email] [v1] Tue, 14 Apr 2026 02:44:50 UTC (18 KB) Access Paper: HTML (experimental) view license Current browse context: cs.AI < prev   |   next > new | recent | 2026-04 Change to browse by: cs References & Citations NASA ADS Google Scholar Semantic Scholar Export BibTeX Citation Bookmark Bibliographic Tools Bibliographic and Citation Tools Bibliographic Explorer Toggle Bibliographic Explorer (What is the Explorer?) Connected Papers Toggle Connected Papers (What is Connected Papers?) Litmaps Toggle Litmaps (What is Litmaps?) scite.ai Toggle scite Smart Citations (What are Smart Citations?) Code, Data, Media Demos Related Papers About arXivLabs Which authors of this paper are endorsers? | Disable MathJax (What is MathJax?)