6 papers
Beyond the Harness: End-to-End Optimization of Context Artifacts for Enterprise Text-to-SQL
Kate Gwimm, Carson Eisenach
Deploying LLMs for enterprise Text-to-SQL is bottlenecked less by the model than by what context reaches it: business logic spans thousands of tables, and no model can ingest a ful…
Ready from Day 1: Population-Aware Coordination for Large-Scale Constrained Multi-Agent Systems
Angel Wang, Dominique Perrault-Joncas, Alvaro Maggiar +2
In large-scale multi-agent systems with shared resource constraints, an upstream planner must iteratively evaluate candidate resource plans -- assessing feasibility, aggregate resp…
BRIDGE: Building Representations In Domain Guided Program Synthesis
Robert Joseph George, Carson Eisenach, Udaya Ghai +3
Large language models can generate plausible code, but remain brittle for formal verification in proof assistants such as Lean. A central scalability challenge is that verified syn…
Deep RL Dual Sourcing Inventory Management with Supply and Capacity Risk Awareness
Defeng Liu, Ying Liu, Carson Eisenach
In this work, we study how to efficiently apply reinforcement learning (RL) for solving large-scale stochastic optimization problems by leveraging intervention models. The key of t…
Structure-Informed Deep Reinforcement Learning for Inventory Management
Alvaro Maggiar, Sohrab Andaz, Akhil Bagaria +4
This paper investigates the application of Deep Reinforcement Learning (DRL) to classical inventory management problems, with a focus on practical implementation considerations. We…
Outbound Modeling for Inventory Management
Riccardo Savorgnan, Udaya Ghai, Carson Eisenach +1
We study the problem of forecasting the number of units fulfilled (or ``drained'') from each inventory warehouse to meet customer demand, along with the associated outbound shippin…