RoutingGen: Adaptive Routing Frameworks

• RoutingGen is a collection of dynamic, adaptive routing frameworks designed for diverse domains such as code generation, urban transportation, and quantum networking.
• It employs modular, learning-based strategies including dynamic routing policies, token cost formulations, and iterative optimization to balance efficiency and accuracy.
• Empirical studies show notable improvements, with reductions in token usage by 46% in code synthesis and travel time decreases of 8.6% in urban traffic management.

RoutingGen refers to multiple, independently developed frameworks and methodologies across a spectrum of domains including code generation with LLMs, intelligent transportation routing, quantum network entanglement routing, retrieval-augmented LLM model selection, intermodal transit planning, vehicle routing optimization, and delay tolerant network protocol synthesis. Each instance of RoutingGen implements dynamic, modular, or learning-based routing strategies tailored to the unique constraints and objectives of its application domain. The following sections review key instantiations of RoutingGen, their technical underpinnings, and empirical outcomes, drawing exclusively from primary, peer-reviewed arXiv sources.

1. RoutingGen for Difficulty-Aware Code Generation

The original RoutingGen framework for code generation, as proposed in "Intention Chain-of-Thought Prompting with Dynamic Routing for Code Generation" (Li et al., 16 Dec 2025), addresses two major limitations of uniform chain-of-thought (CoT) prompting for LLMs: unnecessary reasoning complexity on simple tasks and insufficient abstraction of program-intent on complex tasks. RoutingGen combines a lightweight LLM-based difficulty classifier with a dynamic routing policy that selects between few-shot direct code generation and a structured two-stage Intention Chain-of-Thought (ICoT) prompting strategy, depending on the classified task difficulty.

The workflow operates as follows:

1. Difficulty Classification: Given a natural-language code problem $q$, a classifier $\mathcal{M}_{\mathrm{cls}}$ predicts a label $d^*\in\{\text{Simple},\text{Complex}\}$ along with a rationale $r^*$. The selection is:

$$(d^*, r^*) = \arg\max_{d\in\{\mathrm{S},\mathrm{C}\}} P_{\mathcal{M}_{\rm cls}}(d, r \mid q, T_{\rm cls}).$$

1. Routing Policy: For simple problems, RouteGen uses few-shot direct generation ($\mathcal{G}_{\rm Direct}$); for complex ones, it selects ICoT prompting ($\mathcal{G}_{\rm ICoT}$).
2. ICoT Prompt Design: ICoT is a structured two-stage prompt schema:
   • Stage 1 elicits concise input/output specifications and the core algorithmic idea, including time complexity.
   • Stage 2 uses the output of Stage 1 as context for code generation via greedy decoding.
3. Token Cost Formulation: Token usage is calculated for prompt input and output per sample, enabling direct measurement of efficiency.

Empirically, RoutingGen achieves state-of-the-art Pass@1 accuracy on standard benchmarks (HumanEval, MBPP, OpenEval, etc.) across multiple LLMs, reducing total token usage by 46.37% on average compared to static ICoT prompting. The ablation studies confirm that both the Specification and Idea stages are essential for complex tasks, and the adaptive routing is insensitive to the specific classifier model, with Pass@1 varying by less than 1 percentage point (Li et al., 16 Dec 2025).

2. RoutingGen in Urban Transportation Optimization

In "Three Pillars Towards Next-Generation Routing System" (Li et al., 3 Sep 2024), RoutingGen refers to a real-time city-scale traffic routing system characterized by a closed control loop integrating routing decisions with future traffic prediction and simulation. The framework is constructed atop three core "pillars":

1. Macroscopic Traffic Condition Simulation: Replaces traditional microscopic simulators by tracking per-road vehicle flows and using learned per-edge latency functions:

$$c_e(t) = c_{e, \min}[1 + \sigma_e (x_e(t)/\varphi_e)^{\beta_e}]$$

where $x_e(t)$ is the edge occupancy and $c_e(t)$ travel time, with parameters trained from microscopic traces.

1. Incremental Future-Route Management: Efficiently updates network state upon route insertions/deletions using per-edge B-trees indexed by event time, enabling near-realtime resimulation of hundreds of thousands of route changes.
2. Iterative Global Optimization: Periodically selects the most congested queries for rerouting, solving time-dependent shortest-path subproblems, and converging via a stochastic gradient-descent-like loop to a local minimum in total travel time.

Experiments on Beijing road networks report an 8.6% reduction in total simulated travel time with $10\%$ rerouting over 10 iterations, executing in $\sim4$ minutes for 100,000 queries (Li et al., 3 Sep 2024).

3. RoutingGen for Quantum Network Entanglement

The RoutingGen approach for remote entanglement in quantum networks, from "Effective routing design for remote entanglement generation on quantum networks" (Li et al., 2020), tackles constrained quantum memory and fidelity in lattice-based quantum switches. The framework is modular and encapsulates:

1. Multi-Path Routing: Each entanglement request is decomposed into up to $k$ shortest disjoint or partially disjoint paths post-purification, utilizing integer flow allocations while maintaining per-edge capacity constraints after accounting for fidelity-enhancing purification.
2. Capacity Scheduling: Three options exist:
   • Proportional Share (PS): Capacity allocated proportional to request weights and path lengths.
   • Progressive Filling (PF): Max-min-fair additive increase across all feasible paths until saturation.
   • Propagatory Update (PU): Global iterative rebalancing based on per-edge excess demand, typically offering maximal throughput at slight fairness cost.
3. Physical Integration: The algorithm embeds quantum operations (swapping/purification) within each step, and performance is quantified via throughput, utilization, and fairness indices.

Comparative studies demonstrate PU achieves highest throughput and utilization, while PF is strictest for fairness and PS is most robust under failures (Li et al., 2020).

4. RoutingGen for Retrieval-Augmented LLM Routing

"Query Routing for Retrieval-Augmented LLMs" (Zhang et al., 29 May 2025) introduces a RoutingGen formalism for intelligent selection among multiple retrieval-augmented LLMs (RAG-LLMs), accounting for both the dynamic nature of external document retrieval and model-specific RAG capabilities:

1. Formal Routing Objective: The routing policy $R(q,d)$ maps each (query, document set) pair to a chosen model, aiming to maximize expected correctness.
2. Representation Learning: The router jointly encodes query, document, cross-interaction, model parametric knowledge, and RAG-capability via a mixture of frozen and learned embedding tables.
3. Contrastive Training: Cross-contrastive loss distinguishes positive pairs (correct model/document) from negatives by similarity to the query.
4. Latency-Aware Scoring: Allows speed-accuracy trade-offs by selecting the fastest model within a score threshold of the predicted optimum.

Empirically, RAGRouter achieves +3.61% accuracy over the best single RAG-LLM and +3.29–9.33% over non–RAG-aware routing baselines across five knowledge-intensive domains and remains robust to noisy retrieval (Zhang et al., 29 May 2025).

5. Intermodal and Vehicle Routing: Programmatic and Heuristic Frameworks

RoutingGen also denotes frameworks for intermodal transportation planning (Prandtstetter et al., 2020) and advanced vehicle routing problem (VRP) solution relaxations (Mandal et al., 2022):

• Intermodal Route Alternatives: The algorithm natively supports the computation of diverse, optimal multimodal (car, bike, walk, public transport) paths for a specified objective function, using mode-specific cost multipliers and transfer penalties, without explicit graph layering or k-shortest plateau enumeration. Applications include specialized motorhome routing with vehicle-specific edge restrictions and optional last-mile mode shifts (Prandtstetter et al., 2020).
• Local Area Route Relaxations for VRP: LA routes generalize ng-route and elementary-route relaxations by limiting allowed cycles to those broken at a recursively defined set of "special indices" tied to spatial proximity. This balances computational tractability (in pricing subproblems during column-generation) with solution quality, yielding up to 60× speedups compared to Decremental State Space Relaxation (DSSR) in hard instances (Mandal et al., 2022).

6. RoutingGen in Delay Tolerant Network Protocol Synthesis

"Genetic Improvement of Routing Protocols for Delay Tolerant Networks" (Lorandi et al., 2021) implements RoutingGen as a genetic programming framework for evolving store-and-forward routing protocols (e.g., Epidemic, PRoPHET). Candidate protocols are represented as strongly-typed program trees using atomic protocol operations as terminals and Boolean/control-flow functions as nonterminals. Evolution is governed by tournament selection, subtree mutation/crossover, and fitness-by-delivery-probability evaluated against simulated urban DTN scenarios.

GI variants achieved 30–80% gains in delivery probability in 4/6 scenarios compared to hand-tuned baselines, at the cost of increased average latency and buffer time, suggesting an evolved bias toward "aggressive send-early, low-replica" logic (Lorandi et al., 2021).

7. Synthesis and Cross-Domain Significance

The various incarnations of RoutingGen are characterized by:

• Adaptive routing policies, often data-driven or learning-based
• Explicit cost, efficiency, and trade-off analyses
• Modular design, enabling integration with domain-specific constraints (prompting, quantum operations, real-time graphs, intermodal transfer penalties, etc.)
• Empirical superiority or significant practical speed-ups in their respective domains

While each RoutingGen instance is domain-specific in its technical instantiation, all share the core objective of dynamically selecting among routing or decision-making strategies in a way that is sensitive to task-instance complexity, resource constraints, or external context. Where possible, these frameworks formalize routing itself as an optimization or learning problem and often outperform static, uniform, or hand-engineered approaches by significant empirical margins.