Enterprise AI Research Analysis
CONCUR: A Framework for Continual Constrained and Unconstrained Routing
by Peter Baile Chen, Weiyue Li, Dan Roth, Michael Cafarella, Samuel Madden, Jacob Andreas
Abstract: AI tasks differ in complexity and are best addressed with different computation strategies (e.g., combinations of models and decoding methods). Hence, an effective routing system that maps tasks to the appropriate strategies is crucial. Most prior methods build the routing framework by training a single model across all strategies, which demands full retraining whenever new strategies appear and leads to high overhead. Attempts at such continual routing, however, often face difficulties with generalization. Prior models also typically use a single input representation, limiting their ability to capture the full complexity of the routing problem and leading to sub-optimal routing decisions. To address these gaps, we propose CONCUR, a continual routing framework that supports both constrained and unconstrained routing (i.e., routing with or without a budget). Our modular design trains a separate predictor model for each strategy, enabling seamless incorporation of new strategies with low additional training cost. Our predictors also leverage multiple representations of both tasks and computation strategies to better capture overall problem complexity. Experiments on both in-distribution and out-of-distribution, knowledge- and reasoning-intensive tasks show that our method outperforms the best single strategy and strong existing routing techniques with higher end-to-end accuracy and lower inference cost in both continual and non-continual settings, while also reducing training cost in the continual setting.
Key Executive Impact
CONCUR revolutionizes how enterprises manage AI workflows, delivering tangible improvements in performance and operational efficiency.
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Average Accuracy Increase vs. Best Single Strategy
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Average Inference Cost Reduction
0x
Training Speedup in Continual Settings (vs. RTRFS)
Deep Analysis & Enterprise Applications
Select a topic to dive deeper, then explore the specific findings from the research, rebuilt as interactive, enterprise-focused modules.
The Challenge of Dynamic AI Routing
AI tasks vary significantly in complexity, demanding different computation strategies for optimal performance and cost-efficiency. Traditional AI routing systems struggle with this dynamic environment, especially when new models or decoding methods emerge.
Limitations of Prior Approaches:
- Existing routers typically rely on a single, monolithic model trained across all strategies, necessitating expensive full retraining whenever a new strategy is introduced.
- These methods often face generalization difficulties to unseen strategies and tasks.
- Many prior models use limited input representations, failing to capture the full complexity of routing decisions.
- Some modular designs exist but are often tailored to specific strategies, making extension to new ones non-trivial.
CONCUR: A Modular, Multi-Representation Routing Framework
CONCUR addresses the limitations of prior work through a novel, modular design and rich input representations.
Core Innovation:
- Modular Predictors: A separate predictor model is trained for each computation strategy. This allows for seamless, low-cost integration of new strategies by simply training an additional predictor, without disturbing existing models.
- Multiple Input Representations: Predictors leverage both general-purpose representations (text embeddings of task and strategy descriptions) and task-specific representations (learnable embeddings/projections for tasks, models, and decoding methods). This captures richer routing information.
- Accuracy & Cost Prediction: For each task and strategy, CONCUR predicts both the expected accuracy and computational cost (FLOPS).
Routing Logic:
- Unconstrained Routing: Tasks are routed to the strategy that maximizes a weighted trade-off between predicted accuracy and minimized cost.
- Constrained Routing: For tasks with a budget, CONCUR uses a dynamic programming approach to maximize overall accuracy across a batch of tasks without exceeding the total cost budget.
This design ensures high-quality routing decisions and efficient adaptation in continual learning scenarios.
Empirical Superiority Across Diverse Tasks
CONCUR's effectiveness was rigorously tested across various benchmarks and settings.
Experimental Scope:
- Diverse Task Categories: Multi-hop QA, general reasoning multiple-choice, and math problems.
- Data Distribution: Both in-distribution and challenging out-of-distribution tasks.
- Computation Strategies: Combinations of various LLMs (Qwen2.5, Llama-3.x) and decoding methods (Vanilla, Chain-of-Thought).
- Baselines: Compared against a best single-strategy baseline and strong existing routing methods (RouteLLM, EmbedLLM, RTR).
Key Findings:
- CONCUR consistently outperforms all baselines in both non-continual and continual settings, achieving higher end-to-end accuracy and lower inference costs.
- In continual settings, its modular design drastically reduces training overhead, enabling efficient adaptation to new strategies.
- The global optimization approach for constrained routing demonstrated substantial accuracy gains over local optimization baselines.
- An ablation study confirmed that both general-purpose and task-specific representations are crucial for CONCUR's superior performance.
+0.9%
Average Accuracy Improvement vs. Best Single Strategy in Non-Continual Settings
Enterprise Process Flow
Input Task
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Generate Multi-Representation Features
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Predict Acc/Cost for Each Strategy (Modular Predictors)
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Solve Optimization Problem (Constrained/Unconstrained)
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Optimal Strategy Decision
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Execute & Route Task
| Feature | CONCUR (Ours) | RouteLLM | EmbedLLM | RTR |
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| Modular Design for New Strategies |
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| Multiple Input Representations |
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| Continual Learning Support |
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| Constrained & Unconstrained Routing |
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Case Study: Optimizing Enterprise AI Workflows with Adaptive Routing
A large enterprise currently uses a single, high-cost LLM for all AI tasks, leading to unnecessary expenditures on simpler queries. By implementing CONCUR, they can dynamically route tasks to the most cost-effective yet accurate strategy. For example, less critical inquiries can be handled by smaller, faster models, while complex reasoning tasks leverage larger, CoT-enabled models.
This adaptive routing strategy allows the enterprise to achieve an average accuracy increase of 0.9% while simultaneously realizing a 12.3% reduction in inference costs. Furthermore, as new, more efficient models emerge, CONCUR's modular design enables seamless integration without costly full system retraining, ensuring long-term adaptability and sustained ROI.
Calculate Your Potential ROI
Estimate the financial and efficiency gains your enterprise could achieve with intelligent AI routing.
Annual Savings Potential
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Hours Reclaimed Annually
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Your Implementation Roadmap
A strategic approach to integrating dynamic AI routing for sustained advantage.
Phase 1: Initial Assessment & Strategy Definition
Identify existing AI tasks, currently utilized models, and desired performance/cost targets. Map out the landscape of AI activities within your enterprise to understand the potential for routing optimization.
Phase 2: Data Collection & Predictor Training
Gather relevant data for accuracy and cost metrics across your initial set of computation strategies. Train CONCUR's modular predictors for each strategy, establishing a baseline for routing decisions.
Phase 3: Deployment & Unconstrained Optimization
Deploy CONCUR for dynamic routing of AI tasks. Begin with unconstrained optimization, allowing the system to learn and apply optimal trade-offs between accuracy and cost for your workflows.
Phase 4: Constrained Routing & Budget Management
Introduce budget constraints for specific AI task categories. Leverage CONCUR's dynamic programming capabilities to achieve maximum accuracy within predefined computational cost limits, optimizing resource allocation.
Phase 5: Continual Adaptation & New Strategy Integration
As new LLMs or decoding methods emerge, seamlessly integrate them into CONCUR by training only the corresponding new predictors. This ensures your AI infrastructure remains cutting-edge, adaptive, and continuously optimized.
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