CoS: Towards Optimal Event Scheduling via Chain-of-Scheduling

Quick Overview

The CoS (Chain-of-Scheduling) framework successfully dismantles the three main bottlenecks in large-scale event scheduling—exploration, verification, and integration—by leveraging the strengths of both structured and unstructured reasoning, yielding significantly higher utility scores (e.g., 2.24 vs. 1.95 for the New York dataset) compared to traditional methods like Dynamic Programming, while maintaining low latency.

Key Points: The CoS framework achieved a utility score of 2.24 on the New York dataset, outperforming traditional methods (like the greedy approach) which scored 1.95. The paper identifies three main bottlenecks in large-scale event scheduling: Exploration, Verification, and Integration. CoS uses a three-stage process to solve complex combinatorial problems: Exploration (creative search), Verification (checking constraints/utility), and Integration (final compilation). The greedy approach, which prioritizes speed, resulted in a utility score of 1.95 and often missed the overall optimal plan. When tested on the London dataset, the larger DeepSeek-R1 model took 724 seconds without structure but achieved a higher utility score (3.73 vs. 3.3) when using CoS structure. The structured approach forces the LLM to internalize the logical steps required for optimal planning, avoiding the tendency of unconstrained models to hallucinate or loop. The final, successful approach uses a three-step reasoning process (Exploration, Verification, Integration) to distill complex scheduling problems into manageable steps.

Context: This video discusses the paper "CoS: Towards Optimal Event Scheduling via Chain-of-Scheduling," which introduces a novel framework designed to overcome the persistent challenges LLMs face when solving complex, real-world scheduling problems, such as planning events across multiple locations and constraints. The core issue addressed is the trade-off between speed and accuracy that plagues existing methods when trying to optimize schedules in dynamic environments like event management.