Adaptive Query Routing

Updated 28 November 2025

Adaptive Query Routing is a dynamic strategy for mapping user queries to optimal experts or computational paths by balancing accuracy and cost.
It employs diverse methodologies, including cosine similarity, rule-based policies, and reinforcement learning to tailor routing decisions in multi-agent and federated systems.
Empirical results demonstrate improved accuracy, reduced latency, and cost savings, highlighting its scalability and modularity across varied domains.

Adaptive query routing is the dynamic selection of the optimal resource, expert, or computational path for a user query based on query content, system state, domain specialization, performance/cost constraints, or context. It encompasses a wide span of methodologies and technical frameworks across AI agent orchestration, LLM routing, federated query processing, retrieval-augmented systems, and network routing. The core objective is to achieve domain-targeted accuracy, efficient resource utilization, and robust responsiveness through end-to-end adaptive decision logic.

1. Formalization and Semantics of Adaptive Query Routing

Adaptive query routing generalizes to the problem of mapping a query $q \in Q$ to one of several resources, experts, agents, or models $\mathcal{A} = \{a_1, ..., a_N\}$ , maximizing a scenario-defined utility. The utility is typically parameterized over desired accuracy and operational costs, e.g.:

$a^*(q) = \arg\max_{a \in \mathcal{A}} U(q, a), \quad U(q,a) = \alpha\,\mathrm{Accuracy}(q,a) - \lambda\,\mathrm{Cost}(q,a)$

Where utility combines empirical accuracy (task performance, answer correctness) and cost (tokens, latency, financial expense). In multi-agent and multi-model settings, agents can correspond to LoRA adapters (Shekar et al., 17 Oct 2025), LLM checkpoints (Ding et al., 28 Jun 2025, Jin et al., 4 Jun 2025), domain experts, network nodes (Panayotov et al., 10 Mar 2025, Tsai et al., 2021), or even hybrid symbolic/neural paths (Hakim et al., 15 Jun 2025). Routing may be centralized via a router function $R(q)$ , per-resource via a rule-based policy $\pi(q)$ , or fully distributed as in self-routing agent frameworks (Zheng et al., 22 Oct 2025).

Cosine similarity over learned or static embeddings is a common selection mechanism:

$R(q) = \arg \max_{a \in \mathcal{A}} \frac{\phi(q) \cdot \psi(a)}{\|\phi(q)\|\|\psi(a)\|}$

with $\phi(q)$ as the query embedding, $\psi(a)$ as resource-specific metadata embeddings.

2. Architectures and Workflows

2.1 Domain-Specialized LLM Routing via LoRA-as-Tools

Adaptive Minds (Shekar et al., 17 Oct 2025) exemplifies domain-adaptive routing by equipping a base LLM (e.g., LLaMA-3.1-8B) with multiple LoRA adapters, each specializing in a target domain (General, Chemistry, Finance, Medical, AI/Tech). Queries are embedded by the base LLM; the router executes a vector similarity search against precomputed LoRA metadata embeddings. The winning adapter is dynamically loaded onto the base model for inference. Workflow orchestration is managed by LangGraph, enabling modular agent nodes, conversational memory, and error handling.

2.2 Multi-Model Cost-aware Routing

BEST-Route (Ding et al., 28 Jun 2025), PILOT (Panda et al., 28 Aug 2025), RadialRouter (Jin et al., 4 Jun 2025), DiSRouter (Zheng et al., 22 Oct 2025), MoMA (Guo et al., 9 Sep 2025), and RTR (2505.19435) demonstrate multi-model LLM routing architectures, with routing agents learning compressed representations of both queries and LLM/model features. MoMA expands this paradigm by orchestrating both AI agents and LLMs through FSM-controlled intent recognition and a Pareto-efficient TOPSIS scoring scheme, using task-profiled performance and cost metrics.

2.3 Context- and Priority-Aware Agent Routing

The APBDA framework (Panayotov et al., 10 Mar 2025) introduces adaptive routing for AI multi-agent systems based on an extended Dijkstra graph, integrating agent capabilities, load, reliability, and network parameters into dynamic edge costs. RL-tuned weighting factors optimize for dynamic network load and task priority thresholds.

2.4 Retrieval-Augmented Routing and Hybrid-Source Selection

RAGRouter (Zhang et al., 29 May 2025) and rule-driven agent frameworks (Bai et al., 30 Sep 2025) address query routing in Retrieval-Augmented Generation systems, modeling both static parametric and external document knowledge representations and leveraging contrastive learning for dynamic capability alignment. The rule-driven agent system further enables flexible source selection (unstructured documents, databases, hybrid, or bare LLM), under continual rule refinement and semantic similarity meta-caching.

2.5 Federated Query and Network Routing

ADQUEX (Beiranvand et al., 2015) routes federated SPARQL queries adaptively, utilizing per-tuple cost and latency metrics to dynamically reorder join plans and network paths. In Content-Centric Networking, real-time query-based routing table updates opportunistically probe cache states and update forwarding tables to minimize congestion (Tsai et al., 2021).

2.6 Wireless Sensor Networks

Adaptive query routing in energy-constrained multi-service wireless sensor networks operates over per-node forwarding tables and path cost functions that combine energy, reliability, latency, and disjoint path metrics. Routing policy is altered in real-time according to query type and scenario-specified QoS requirements (Sen, 2010).

3. Methods for Adaptive Decision Making

Routing Methodology	Key Mechanism	Deployment Context
Neural embedding matching	Cosine/dot similarity over query and resource embeddings	LLM tool orchestration (Shekar et al., 17 Oct 2025)
Rule-based scoring	Explicit predicate-based path scoring, meta-caching	Hybrid RAG source routing (Bai et al., 30 Sep 2025)
RL-based cost tuning	Adaptive edge weights via Q-learning/policy gradients	Multi-agent system (Panayotov et al., 10 Mar 2025)
Multi-arm bandits	Contextual LinUCB with embedding prior, knapsack cost policy	Budgeted LLM selection (Panda et al., 28 Aug 2025)
Mixed experts / FSM	MoE gating, FSM-driven agent selection, dynamic masking	Generalized orchestration (Guo et al., 9 Sep 2025)
Best-of-n sampling	Multi-sample selection with proxy scorer for improved quality	LLM routing (cost minimization) (Ding et al., 28 Jun 2025)
Utility-based selection	MLP-predicted accuracy/cost trade-off for model-strategy pairing	Model+reasoning strategy (2505.19435)

Many systems proceed from simple similarity, hard rules, or cost prediction to hybrid, jointly trained objective functions balancing latency and resource cost (e.g., RTR, RadialRouter, MoMA).

4. Parameter-Efficient Routing and Model Adaptation

Parameter-efficient adaptation (LoRA (Shekar et al., 17 Oct 2025)) enables scaling to large numbers of specialized experts while minimally increasing memory and computational overhead. Adaptive Minds utilizes rank-16 LoRA matrices, activating ≈0.1–0.3% extra model parameters per adapter (20–30M) for domain Q&A instruction-tuning tasks.

Routing systems gracefully scale as pools of models/experts grow; memory overhead and router cost remain marginal compared to full model inference. In DiSRouter, self-awareness training ensures modularity—agents join/leave dynamically without retraining the system (Zheng et al., 22 Oct 2025).

5. Performance, Empirical Results, and Trade-Off Analysis

Routing Accuracy & Efficiency

Adaptive Minds achieves 100% routing accuracy on a 25-query test set versus 48.3% for keyword-based baselines, with throughput of 20 queries/minute and mean latency of 3.486s (up to 4.1× faster than non-adaptive baselines) (Shekar et al., 17 Oct 2025). BEST-Route reports up to 60% cost reduction at <1% performance drop (Ding et al., 28 Jun 2025). PILOT attains 93% of GPT-4 accuracy at 25% of its cost under online bandit-budget constraints (Panda et al., 28 Aug 2025). RadialRouter consistently outperforms prior methods by 9.2pp (Balance) and 5.8pp (Cost-First) in performance-cost scenarios, adapting robustly to dynamic model pools (Jin et al., 4 Jun 2025).

Resource Utilization

SymRAG's adaptive neuro-symbolic routing maintains CPU utilization as low as 3.6–6.2% and absolute latency of 0.985–3.165s, with disabling logic causing 169–1151% increases in processing time (Hakim et al., 15 Jun 2025).

Scalability and Modularity

Distributed self-routing (DiSRouter) demonstrates monotonic utility gains as agents are added, with no retraining overhead; utility is robust across in- and out-of-domain scenarios (Zheng et al., 22 Oct 2025). MoMA achieves superior cost-efficiency (score=70.1%, cost=10.04) compared to single best LLM usage; auto-routing further reduces cost by 31.5% while maintaining competitive accuracy (Guo et al., 9 Sep 2025).

Network and Federated Query Settings

APBDA reduces 99p latency by 30–50% for high-priority tasks and improves load balancing by ~15% (Panayotov et al., 10 Mar 2025). ADQUEX achieves optimal join reordering, with intermediate results within <10% of best static plan and clear latency mitigation under dynamic endpoint delays (Beiranvand et al., 2015). Query-based CCN routing reduces response time and retransmissions by substantial margins (SmartFlooding: 8–15% fewer Interests, BestRoute: 60–80% fewer retransmissions at η=0.4–0.7 normalized cache size) (Tsai et al., 2021).

6. Limitations, Failure Modes, and Prospective Extensions

Common limitations include:

Potential latency overhead from multi-stage routing and expert activation (Adaptive Minds, MoMA, RTR).
Assignment ambiguity in cross-domain queries, diminishing routing granularity (Adaptive Minds).
Scalability risks due to adapter or embedding collision/interference in large pools (Shekar et al., 17 Oct 2025).
Need for high-quality, large-scale bench datasets for robust response quality assessment and domain-specific performance calibration (MoMA, RadialRouter).
Centralized routers lack modularity and fail to generalize across changes in model pool composition (Zheng et al., 22 Oct 2025).

Future research directions explicitly called out:

Dynamic adapter fusion and in-generation path selection (Shekar et al., 17 Oct 2025).
Routing log introspection and explainability (Shekar et al., 17 Oct 2025).
Online rule refinement and meta-caching (Bai et al., 30 Sep 2025).
Multi-modal and multi-agent pipeline extension (Hakim et al., 15 Jun 2025, Guo et al., 9 Sep 2025).
Cost-policy integration for real-time budget management, bandit frameworks (Panda et al., 28 Aug 2025).
End-to-end joint router/expert training for cost-performance calibration and real-time adaptability (Guo et al., 9 Sep 2025).

7. Cross-Domain Applicability and Generalization

Adaptive query routing is broadly applicable across domains where task heterogeneity, resource cost, or expert specialization matters:

Multi-domain LLM and expert orchestration in conversational AI and assistance (Shekar et al., 17 Oct 2025, Guo et al., 9 Sep 2025).
Federated querying over linked open data (Beiranvand et al., 2015).
Retrieval-augmented document and structured data pipelines in QA and information retrieval (Zhang et al., 29 May 2025, Bai et al., 30 Sep 2025).
Real-time agent and resource selection in sensor networks and distributed AI systems (Sen, 2010, Panayotov et al., 10 Mar 2025).
Cost-optimized, scenario-aware inference under budget constraints, both centralized and distributed (Panda et al., 28 Aug 2025, Zheng et al., 22 Oct 2025).

Practical deployment guidelines emphasize modular agent and adapter design, periodic rule and embedding updates, judicious cost-performance trade-off calibration via scenario-aware controllers, and maintaining interpretability through rule-driven or embedding-based analysis.

Primary references for this article include Adaptive Minds (Shekar et al., 17 Oct 2025), BEST-Route (Ding et al., 28 Jun 2025), APBDA (Panayotov et al., 10 Mar 2025), RAGRouter (Zhang et al., 29 May 2025), PILOT (Panda et al., 28 Aug 2025), RadialRouter (Jin et al., 4 Jun 2025), MoMA (Guo et al., 9 Sep 2025), DiSRouter (Zheng et al., 22 Oct 2025), ADQUEX (Beiranvand et al., 2015), rule-driven routing (Bai et al., 30 Sep 2025), SymRAG (Hakim et al., 15 Jun 2025), and adaptive routing for WSNs (Sen, 2010).