LoRe: Adaptive Interaction-Evaluation Routing with Per-Step Interaction Budgets for Iterative Graph Solvers
📰 ArXiv cs.AI
Learn how LoRe optimizes iterative graph solvers by adaptively routing interactions with per-step budgets, reducing inference time and memory usage
Action Steps
- Implement LoRe as a drop-in wrapper around existing iterative graph solvers
- Configure per-step interaction-evaluation budgets to control the trade-off between accuracy and efficiency
- Evaluate the performance of LoRe on benchmark graph optimization problems
- Compare the results with traditional diffusion-based neural solvers
- Apply LoRe to real-world graph-based optimization problems to demonstrate its practical impact
Who Needs to Know This
Researchers and engineers working on graph-based optimization problems can benefit from LoRe's adaptive interaction-evaluation routing to improve solver efficiency
Key Insight
💡 LoRe's per-step interaction-evaluation budgeting enables efficient and scalable graph optimization
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🚀 LoRe: Adaptive interaction-evaluation routing for iterative graph solvers, reducing inference time and memory usage! 📈
Key Takeaways
Learn how LoRe optimizes iterative graph solvers by adaptively routing interactions with per-step budgets, reducing inference time and memory usage
Full Article
Title: LoRe: Adaptive Interaction-Evaluation Routing with Per-Step Interaction Budgets for Iterative Graph Solvers
Abstract:
arXiv:2605.29005v1 Announce Type: cross Abstract: Diffusion-based neural solvers for combinatorial optimization repeatedly re-evaluate dense edge/factor interactions, making inference expensive in wall-clock time and often memory-bound at scale. Inspired by the computational methodologies of many-body physics, we introduce LoRe, a training-free, inference-time drop-in wrapper that enforces per-step interaction-evaluation budgeting: at each iteration, it evaluates only a fixed fraction of interac
Abstract:
arXiv:2605.29005v1 Announce Type: cross Abstract: Diffusion-based neural solvers for combinatorial optimization repeatedly re-evaluate dense edge/factor interactions, making inference expensive in wall-clock time and often memory-bound at scale. Inspired by the computational methodologies of many-body physics, we introduce LoRe, a training-free, inference-time drop-in wrapper that enforces per-step interaction-evaluation budgeting: at each iteration, it evaluates only a fixed fraction of interac
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