Dynamic Routing for Efficient LLM Agents
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On this page 4 sections
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Key Takeaways
- ProgRouter achieved a 93.0% task pass rate on HumanEval Plus within a 4800 J energy budget, outperforming other routing methods.
- The system delivered the best performance on the MBPP dataset with a 79.4% pass rate, 3376 J energy, and 10.3 s execution time.
- On the ASQA benchmark, the agent reached a 92.1% citation precision while staying within a 19000 J energy limit.
- The approach uses a progress-aware mechanism to balance task-solving quality against operational costs during multi-step workflows.
Summary & Methodology Analysis
ProgRouter implements an online orchestration framework that governs how tasks are assigned across a pool of LLM agents. The architecture utilizes a multi-view task progress scorer to integrate both coarse workflow stages and fine-grained indicators, such as subtask completion and trend analysis. This scoring system acts as the core signal for a dual-path task progress predictor, which processes information through structured and semantic channels to estimate the potential progress gain of candidate agents. An adaptive meta-gating mechanism then uses these estimates to select agents in real-time, effectively managing the trade-off between task quality and computational cost. To maintain budget adherence, the framework employs a virtual cost queue that tracks cumulative constraint violations, forcing the routing logic to penalize agents that might cause the system to exceed its energy targets.
Interactive System Flowchart
Cross-Examination & FAQs
A deeper dive clarifying mechanics, constraints, and baseline evaluations.
Q1. What is the primary goal of ProgRouter?
It aims to route tasks to specific LLM agents dynamically to maximize completion rates while staying within set energy budgets.
Q2. Does this approach improve performance compared to existing methods?
Yes, it outperformed baselines like MasRouter and CASCADIA across multiple benchmarks including HumanEval Plus and ASQA.
Q3. Is this tool ready for all types of agentic tasks?
Generalization to tasks like open-ended web navigation or tool-augmented QA has not yet been empirically verified.
Q4. What is the specific performance of ProgRouter on the MBPP benchmark?
It achieved a 79.4% pass rate, consumed 3376 J of energy, and completed tasks in 10.3 s.
Q5. How does the progress scorer function?
It uses a multi-view design that monitors workflow regimes, subtask completion, progress trends, and overall workflow state quality.
Q6. Does the system automatically learn how to track task progress?
While the design provides reliable signals, automatically learning these representations end-to-end remains a future research direction.
Q7. What benchmarks were used to evaluate this system?
The authors evaluated the system on HumanEval Plus, MBPP, MATH-500, and ASQA.
Q8. How does the routing algorithm handle cost constraints?
It uses a virtual cost queue to monitor energy consumption and applies cost penalties during the decision-making process to ensure the budget is respected.
Q9. What is the current limitation regarding workflow configuration?
The multi-view task progress scorer currently requires manual specification of workflow milestones by the user.