PARCO: Parallel AutoRegressive Models for Multi-Agent Combinatorial Optimization
Parallel autoregressive models for multi-agent combinatorial optimization.
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LPMARL: Linear Programming-based Task Assignment for Hierarchical Multi-agent Reinforcement Learning
Linear-programming-based task assignment in hierarchical multi-agent reinforcement learning.
Read overviewRRNCO: Towards Real-World Routing with Neural Combinatorial Optimization
Neural combinatorial optimization for routing problems with real-world requirements.
Read overviewNeural Embedded Mixed-Integer Optimization for Location-Routing Problems
Neural models embedded in mixed-integer optimization for location-routing problems.
Read overviewRL4CO: an Extensive Reinforcement Learning for Combinatorial Optimization Benchmark
A reinforcement-learning benchmark and library for combinatorial optimization.
Read overviewNeural Genetic Search in Discrete Spaces
Neural genetic search over discrete solution spaces.
Read overviewCAMP: Collaborative Attention Model with Profiles for Vehicle Routing Problems
Collaborative attention and vehicle profiles for routing.
Read overviewAnt Colony Sampling with GFlowNets for Combinatorial Optimization
GFlowNets and ant colony sampling for combinatorial optimization.
Read overviewGenerative Flows on Synthetic Pathway for Drug Design
Generative flows over synthetic pathways for drug design.
Read overviewGenetic Algorithms with Neural Cost Predictor for Solving Hierarchical Vehicle Routing Problems
Neural cost prediction within genetic algorithms for hierarchical routing.
Read overviewRouteFinder: Towards Foundation Models for Vehicle Routing Problems
Foundation models for vehicle routing across problem settings.
Read overviewHiMAP: Learning Heuristics-Informed Policies for Large-Scale Multi-Agent Pathfinding
Heuristics-informed policies for large-scale multi-agent pathfinding.
Read overviewEquity-Transformer: Solving NP-hard Min-max Routing Problems as Sequential Generation with Equity Context
Sequential generation with equity context for min-max routing.
Read overviewA Neural Separation Algorithm for the Rounded Capacity Inequalities
Neural separation of rounded capacity inequalities for vehicle routing.
Read overviewOIP: the foundation for running operational intelligence
How shared identity, permissions, deployment, and observability connect optimization capabilities to business applications.
Read storyIf agents both build and use software…
An exploration of agent teams, evidence, human supervision, and problem-solving capability.
Read storyTen years of SILAB, three years of Omelet: from research to intelligence in the field
A reflection on prediction, control, improvement, and the work of defining real problems.
Read storyProblem solving is not about being good at computation
The engineering judgment behind the balance between a faithful model and a solvable problem.
Read storyThe gap between strategy and execution
Why the structure of a decision matters when moving an AI pilot into daily operations.
Read storyTOAST: routing optimization through conversation
How TOAST connects a supervisor, specialist agents, and the routing engine, with implementation notes by Juni Lee and co-authors.
Read storyOmelet’s first town hall and second birthday
At Omelet’s second birthday and first town hall, the team shared field experience, agent architecture, and product-development direction.
Read storyTwo years of solving real problems
A second-anniversary reflection on combining operations research and machine learning with field experience.
Read story[Between professor and founder] SILAB’s research direction amid technological change
Between professor and founder: choosing a research direction as AI changes the questions we ask.
Read storyA paradigm shift in optimization: data-driven learning and the role of AI
What learning, reasoning, and computing change about industrial optimization.
Read storyAn Omelet workshop, in retrospect
A photo journal from Omelet’s October 2024 team workshop.
Read story[Between professor and founder] The decision to start a company
A founder’s reflection on the complementary work of a researcher and an entrepreneur.
Read storyOpenAI’s Strawberry and OaaSIS, Omelet’s optimization AI service
How learned solution generation, improvement search, and feedback can form an optimization loop.
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