mlsanityData to 7 Oct 2026

TRACE-CS: A Hybrid Logic-LLM System for Explainable Course Scheduling

arXiv 2409.03671 ↗First posted 5 Sep 2024cs.AIcited by 0

On the map: LLM reasoning → Reasoning benchmarks and evaluation

Abstract (arXiv)

We present TRACE-CS, a novel hybrid system that combines symbolic reasoning with large language models (LLMs)to address contrastive queries in course scheduling problems. TRACE-CS leverages logic-based techniques to encode scheduling constraints and generate provably correct explanations, while utilizing an LLM to process natural language queries and refine logical explanations into user friendly responses. This system showcases how combining symbolic KR methods with LLMs creates explainable AI agents that balance logical correctness with natural language accessibility, addressing a fundamental challenge in deployed scheduling systems.

Key facts. No citing papers in the map yet (data through 7 Oct 2026). Placed in Reasoning benchmarks and evaluation, one of 17 directions in LLM reasoning. Builds on 6 of its 27 references that are in the map. First posted on arXiv 5 Sep 2024.

Builds on

6 of 27 references.

References · 6

  1. 2023On the Planning Abilities of Large Language Models : A Critical Investigationcited by 308
  2. 2023Embers of Autoregression: Understanding Large Language Models Through the Problem They are Trained to Solvecited by 175
  3. 2023A Survey of Large Language Models in Medicine: Progress, Application, and Challengecited by 123
  4. 2020On Exploiting Hitting Sets for Model Reconciliationcited by 11
  5. 2022Explaining Preference-driven Schedules: the EXPRES Frameworkcited by 5
  6. 2023Contrastive Explanations of Centralized Multi-agent Optimization Solutionscited by 4

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  1. 2023Logic-LM: Empowering Large Language Models with Symbolic Solvers for Faithful Logical Reasoning
  2. 2024ChatLogic: Integrating Logic Programming with Large Language Models for Multi-Step Reasoning
  3. 2024Distilling Algorithmic Reasoning from LLMs via Explaining Solution Programs
  4. 2024Leveraging LLMs for Hypothetical Deduction in Logical Inference: A Neuro-Symbolic Approach
  5. 2024HYBRIDMIND: Meta Selection of Natural Language and Symbolic Language for Enhanced LLM Reasoning
  6. 2023Improved Logical Reasoning of Language Models via Differentiable Symbolic Programming
  7. 2024A Closer Look at Logical Reasoning with LLMs: The Choice of Tool Matters
  8. 2025From Language to Logic: A Bi-Level Framework for Structured Reasoning