ModaRoute: Multimodal Routing Paradigm

• ModaRoute is a modality-aware routing paradigm that processes inputs in their native modes to preserve modality-specific fidelity for improved downstream inference.
• It operates in both agent-to-agent systems and video retrieval, dynamically selecting channels based on capability metadata or query semantics.
• Empirical studies show that preserving native modality signals boosts task accuracy significantly, despite introducing higher computational latency.

Searching arXiv for the cited ModaRoute-related papers to ground the article in current literature. ModaRoute is a general routing paradigm for multimodal systems in which routing decisions are made at the modality level rather than by collapsing heterogeneous inputs into a single representation. In the literature supplied here, the term is most concretely instantiated by two distinct but conceptually related lines of work: modality-native routing in Agent-to-Agent networks, especially the MMA2A architecture introduced in “Modality-Native Routing in Agent-to-Agent Networks: A Multimodal A2A Protocol Extension” (Srinivasan, 14 Apr 2026), and dynamic modality selection for large-scale multimodal video retrieval in “Smart Routing for Multimodal Video Retrieval: When to Search What” (Rosa, 12 Jul 2025). Across these settings, ModaRoute denotes a routing layer that decides which modality-specific channel, agent, or index should receive a given information unit, with the central design premise that routing determines the information topology of the system and therefore constrains what downstream reasoning can achieve (Srinivasan, 14 Apr 2026).

1. Definition and conceptual scope

ModaRoute refers to modality-aware routing in systems that process heterogeneous inputs such as voice, image, and text. The defining principle is “forwarding parts in their original modality when the receiving agent supports it” (Srinivasan, 14 Apr 2026). In the A2A setting, this means preserving FilePart payloads such as audio/wav or image/png rather than coercing them into TextPart proxies via speech-to-text or image captioning. In the video-retrieval setting, it means selecting only those modality indices—ASR, OCR, and visual—that are likely to answer a given query, rather than exhaustively searching all indices or relying on a fused text surrogate (Rosa, 12 Jul 2025).

The concept therefore spans at least two architectural regimes. In one regime, routing is an inter-agent transport decision over typed protocol objects. In the other, routing is a retrieval-time decision over specialized search backends. In both cases, the route decision is not a mere systems optimization; it governs which evidence remains available to the downstream model or search process. This suggests a unifying interpretation of ModaRoute as a modality-selection layer that preserves or activates modality-specific evidence when doing so is beneficial, while permitting fallback to cheaper or more universal representations when necessary.

A common misconception is that multimodal benefit arises merely from adding more modalities to a system. The evidence in (Srinivasan, 14 Apr 2026) shows that preserving multimodal signals across boundaries is necessary but not sufficient: improvements appear only when downstream reasoning can exploit the additional fidelity. Likewise, (Rosa, 12 Jul 2025) shows that always using every modality is not intrinsically optimal, because selective routing can preserve competitive retrieval effectiveness while reducing computational overhead.

2. Protocol-level ModaRoute in A2A systems

In the A2A formulation, ModaRoute is realized as MMA2A, an architecture layer on top of an HTTP + JSON-RPC 2.0–based Agent-to-Agent protocol (Srinivasan, 14 Apr 2026). A2A agents publish an Agent Card at /.well-known/agent-card.json, exchange messages composed of typed Parts, and already support multimodal payloads via FilePart. MMA2A exploits this native support by inspecting capability declarations in Agent Cards and routing each part according to modality support.

The paper defines three specialized agents backed by the same LLM, Gemini 2.5 Flash, so that routing is isolated as the main experimental variable (Srinivasan, 14 Apr 2026). The deployment includes a Voice Agent $\mathcal{A}_v$, a Vision Agent $\mathcal{A}_i$, and a Text Agent $\mathcal{A}_t$. Capabilities are declared through fields such as skills[].inputModes and skills[].outputModes, which MMA2A interprets as the capability set $\text{cap}(\mathcal{A}_j)$. The Modality-Aware Router (MAR), implemented as a lightweight proxy of fewer than 500 lines of Python, intercepts tasks/send and tasks/sendSubscribe, fetches and caches Agent Cards with a 60-second TTL, and decides whether a part should be forwarded natively or transcoded to text (Srinivasan, 14 Apr 2026).

The core routing rule is given explicitly as:

$$\text{route}(p, \mathcal{A}_j) = \begin{cases} p & \text{if } m \in \text{cap}(\mathcal{A}_j) \ \text{transcode}(p, t) & \text{otherwise} \end{cases}$$

where $p$ is a message part, $m \in \{v,i,t\}$ is its modality, and $\text{transcode}(p,t)$ denotes speech-to-text for audio or image captioning for images (Srinivasan, 14 Apr 2026). This is capability-driven modality selection. The text-bottleneck baseline is implemented by overriding this rule so that all non-text parts are forcibly transcoded, regardless of target-agent capability. Methodologically, the paper keeps the same agents, same LLM backend, same knowledge base, and same task decomposition in both systems; only the routing strategy changes (Srinivasan, 14 Apr 2026).

This design makes ModaRoute in A2A systems a protocol-level layer rather than a change to model internals. It requires no protocol modification, only disciplined use of FilePart, MIME types, and Agent Card metadata. A plausible implication is that modality-aware routing can be introduced incrementally in existing A2A deployments, provided capability declarations are trustworthy and downstream agents are already multimodal.

3. Information preservation and the two-layer requirement

The principal argument for ModaRoute in (Srinivasan, 14 Apr 2026) is that text bottlenecks erase decision-relevant signal. In the voice channel, speech-to-text removes prosody, tone, emphasis, hesitations, and other cues that the Voice Agent can otherwise use to infer sentiment, urgency, or admission of fault. In the image channel, captioning removes fine-grained spatial or visual evidence such as hairline cracks, burn marks, orientation, or local spatial relations. MMA2A preserves these as raw audio/wav, image/png, or image/jpeg payloads whenever a downstream agent supports them (Srinivasan, 14 Apr 2026).

However, the paper establishes what it calls a “two-layer requirement” (Srinivasan, 14 Apr 2026). The first layer is protocol-level modality routing, which preserves high-fidelity information across agent boundaries. The second layer is agent-level reasoning capability, which must be able to exploit the preserved signal. This is demonstrated through an ablation in which the final LLM-backed reasoning step is replaced with a keyword-matching heuristic. Under that ablation, modality-native and text-bottleneck pipelines achieve identical accuracy, 36% versus 36% (Srinivasan, 14 Apr 2026). Only when the final decision is made by LLM reasoning does the native-routing advantage emerge.

The result is significant because it distinguishes two different failure modes. A system may fail because routing destroys signal before any reasoning agent sees it, or because reasoning is too weak to capitalize on preserved evidence. ModaRoute addresses only the first of these directly. This suggests that modality-native routing should be treated as a co-design problem with prompting, model choice, tool augmentation, and output schemas, not as a standalone transport optimization.

The paper summarizes the causal structure as RoutingStrategy → InputFidelity → TaskAccuracy, with ReasoningCapability moderating the fidelity-to-accuracy link (Srinivasan, 14 Apr 2026). In encyclopedia terms, this is one of the clearest formal contributions associated with the ModaRoute concept: routing is elevated from a transport concern to a first-order determinant of informational availability.

4. Empirical performance in controlled A2A benchmarking

The A2A instantiation of ModaRoute is evaluated on CrossModal-CS, a controlled 50-task benchmark designed specifically to isolate routing effects (Srinivasan, 14 Apr 2026). The tasks span four categories—product defect report, assembly guidance, visual troubleshooting, and warranty claim—and each requires joint reasoning over at least two modalities. Ground truth is an 8-class action label comprising approve_warranty, deny_warranty, initiate_replacement, initiate_return, order_part, escalate_to_specialist, provide_instructions, and troubleshoot_step (Srinivasan, 14 Apr 2026).

The benchmark’s main methodological strength is that the LLM backend, knowledge base, prompts, task decomposition, and orchestration are held constant across conditions (Srinivasan, 14 Apr 2026). Only the routing rule differs: native modality preservation versus forced text transcoding. Under this design, MMA2A achieves 52.0% Task Completion Accuracy (TCA), compared with 32.0% for the text-bottleneck baseline, for a gain of 20 percentage points (Srinivasan, 14 Apr 2026). The paired comparison yields 11 discordant pairs favoring MMA2A and 1 favoring the baseline, with McNemar’s exact test $p = 0.006$, and a 10,000-sample paired bootstrap confidence interval on $\Delta$TCA of $\mathcal{A}_i$0 percentage points (Srinivasan, 14 Apr 2026).

The gains are concentrated in vision-heavy tasks. Product defect report improves by +38.5 percentage points, and visual troubleshooting by +16.7 percentage points (Srinivasan, 14 Apr 2026). Assembly guidance, which depends on voice and text, improves by +16.6 points, while warranty claim improves by +7.7 points. This pattern is consistent with the hypothesis that modality-native routing matters most when perceptual detail is decision-critical.

The benefit comes with a latency cost. Overall end-to-end latency rises from 7.19 seconds to 13.04 seconds, approximately a $\mathcal{A}_i$1 factor, with paired $\mathcal{A}_i$2 and $\mathcal{A}_i$3 (Srinivasan, 14 Apr 2026). Yet bandwidth remains essentially unchanged at +0.5%, because larger binary payloads are mitigated by small input sizes and URI-based transfer of large files (Srinivasan, 14 Apr 2026). This supports a nuanced systems conclusion: native multimodal routing is not primarily a bandwidth problem in this setting, but a multimodal inference latency problem.

The routing profile further clarifies behavior. Under MMA2A, all voice inputs are routed natively, 70% of images are routed natively, and total native-routing share rises from 50% to 82% (Srinivasan, 14 Apr 2026). The remaining image transcoding occurs when content must be sent to text-only agents, which illustrates that even a modality-native system may contain modality bottlenecks at specific endpoints.

5. Query-time ModaRoute for multimodal retrieval

A distinct but related use of the term appears in “Smart Routing for Multimodal Video Retrieval: When to Search What” (Rosa, 12 Jul 2025). Here, ModaRoute is not a protocol proxy but an LLM-based routing system for large-scale video retrieval. The problem setting is that each clip may be indexed by speech (ASR), on-screen text (OCR), and visual embeddings, and exhaustive multimodal retrieval is accurate but computationally expensive. Dense fused captions can serve as a strong text-based upper bound, but require costly offline processing and still miss some critical information; the paper states that 34% of clips contain scene text not captured by ASR (Rosa, 12 Jul 2025).

The system uses GPT-4.1 as a query router that inspects a natural-language search query and selects a subset of the three modality indices—ASR, OCR, and visual—to search (Rosa, 12 Jul 2025). Each selected index is queried in parallel, and results are combined by linear rank fusion. Formally, the route decision can be interpreted as a mapping $\mathcal{A}_i$4, where $\mathcal{A}_i$5 is the user query and $\mathcal{A}_i$6 is the selected subset (Rosa, 12 Jul 2025).

The system is evaluated on 1.8 million video clips from 29,259 source videos using 2,195 queries with known ground-truth clips (Rosa, 12 Jul 2025). In the reported comparison, an All-Text upper bound based on fused captions achieves 75.9% Recall@5, while ModaRoute achieves 60.9% Recall@5 using an average of 1.78 modalities per query rather than exhaustive 3.0-modality search (Rosa, 12 Jul 2025). This reduces computational overhead by 41%. Notably, ModaRoute substantially outperforms naive late fusion over all three modalities, which achieves only 38.7% Recall@5, as well as single-modality baselines such as ASR-only at 46.2% (Rosa, 12 Jul 2025).

The routing behavior is especially strong on ASR-derived queries, where ModaRoute matches the All-Text upper bound at 86.2% Recall@5 (Rosa, 12 Jul 2025). It is weaker on OCR-derived queries, reflecting a recognized routing difficulty: OCR intent is often confounded with ASR and visual content, especially in cases involving subtitles or visually embedded text (Rosa, 12 Jul 2025). The single-modality routing confusion matrix reported in the paper shows OCR as the hardest modality to identify correctly.

This retrieval formulation suggests a broader meaning of ModaRoute: it can designate not only modality-preserving transport within multi-agent systems, but also dynamic selection of modality-specific computational pathways under efficiency constraints. In both cases, the core design question is identical: when should the system preserve or activate a modality rather than substitute a cheaper proxy?

6. Design principles, trade-offs, and limitations

Two design principles recur across the ModaRoute literature represented here. The first is capability- or intent-driven selection. In A2A systems, routing depends on inputModes and outputModes declared in Agent Cards (Srinivasan, 14 Apr 2026). In retrieval systems, routing depends on semantic cues in the query and the router’s inference about which evidence source is needed (Rosa, 12 Jul 2025). The second is selective fidelity: full multimodal fidelity is preserved or activated only where it is likely to matter.

A concise summary of the two principal instantiations is helpful.

Setting	Routing object	Main benefit
A2A / MMA2A	Message parts across agents	Higher downstream information fidelity
Video retrieval	Query dispatch across modality indices	Lower search cost with competitive effectiveness

The central trade-off is between fidelity or effectiveness and systems cost. In MMA2A, native multimodal processing yields a 20-point TCA improvement but incurs a $\mathcal{A}_i$7 latency factor (Srinivasan, 14 Apr 2026). In video retrieval, ModaRoute sacrifices accuracy relative to the dense All-Text upper bound but reduces computational overhead by 41% and still exceeds naive multimodal late fusion (Rosa, 12 Jul 2025). These are not contradictory results; they show that ModaRoute is not tied to a single optimization criterion. In one regime it prioritizes preserving information for better reasoning, in another it prioritizes reducing unnecessary computation while keeping quality competitive.

Limitations are also explicit. The A2A benchmark is small, domain-specific, English-only in voice, and uses a single underlying model family for all agents (Srinivasan, 14 Apr 2026). The retrieval system depends on GPT-4.1 as a router, which introduces latency and external-model dependence, and its OCR routing remains weaker than ASR or visual routing (Rosa, 12 Jul 2025). More generally, both lines of work indicate that routing quality depends heavily on metadata quality, model capability, and the cleanliness of modality boundaries. When those boundaries blur—as with subtitles that are both textual and spoken—routing becomes harder.

A plausible implication is that future ModaRoute systems will move toward adaptive or hybrid routing. The A2A paper already proposes a priority-aware extension:

$\mathcal{A}_i$8

which explicitly trades off fidelity and latency (Srinivasan, 14 Apr 2026). The retrieval paper similarly points toward multi-step or feedback-driven routing as a natural extension (Rosa, 12 Jul 2025).

7. Relation to broader routing research

ModaRoute should not be conflated with all uses of “routing” in machine learning or transportation. Several papers in the supplied corpus involve routing, but at different abstraction levels. “Attention Is All You Need For Mixture-of-Depths Routing” (Gadhikar et al., 2024) studies token routing inside transformers; “R2-T2: Re-Routing in Test-Time for Multimodal Mixture-of-Experts” (Li et al., 27 Feb 2025) studies test-time adjustment of expert weights in multimodal MoE encoders; and “SMAR: Soft Modality-Aware Routing Strategy for MoE-based Multimodal LLMs Preserving Language Capabilities” (Xia et al., 6 Jun 2025) studies expert specialization across modalities within an MoE LLM. These works are adjacent because they all treat routing as a learned allocation of computation or representation, but they do not define ModaRoute as an inter-agent or cross-index modality router.

The strongest conceptual relation is to the view of routing as determining an information topology. That phrase appears explicitly in (Srinivasan, 14 Apr 2026), which argues that routing specifies “which evidence is available to which agent, in what fidelity, at decision time.” A similar systems-level theme appears in (Rosa, 12 Jul 2025), where query-time routing determines which modality-specific evidence sources will even be searched. Taken together, these papers suggest that ModaRoute is best understood not as a single architecture, but as a family of modality-aware control layers that sit between input and reasoning or retrieval.

This suggests an emerging research agenda around three questions. First, how should routing decisions combine modality capability, task priority, and cost? Second, what degree of downstream reasoning competence is necessary to realize gains from modality preservation? Third, how should multimodal systems be evaluated so that routing, rather than model size or prompt variation, is isolated as the primary experimental variable? The A2A work’s paired benchmark design and the video retrieval work’s cost-effectiveness analysis provide two concrete evaluation templates (Srinivasan, 14 Apr 2026, Rosa, 12 Jul 2025).

In this sense, ModaRoute names a shift in multimodal systems design. Instead of assuming that transport, indexing, and downstream inference can be separated cleanly, it treats routing itself as a first-order design variable.