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[2602.14035] FloCA: Towards Faithful and Logically Consistent Flowchart Reasoning
Summary
The paper introduces FloCA, a flowchart-oriented conversational agent designed to enhance decision-making in dialogue systems by ensuring logical consistency and faithfulness in flowchart reasoning.
Why It Matters
FloCA addresses critical limitations in current large language models (LLMs) related to flowchart reasoning, which is essential for effective task-oriented dialogues. By improving the accuracy and consistency of interactions, it has implications for various applications in artificial intelligence, particularly in automated decision-making systems.
Key Takeaways
- FloCA formalizes flowchart reasoning in flowchart-oriented dialogue systems.
- It overcomes LLM limitations by integrating external tools for topology-constrained graph execution.
- The proposed evaluation framework includes new metrics for assessing reasoning accuracy and interaction efficiency.
- Extensive experiments demonstrate FloCA's superiority over existing LLM-based methods.
- The research contributes to advancements in AI applications requiring logical consistency.
Computer Science > Artificial Intelligence arXiv:2602.14035 (cs) [Submitted on 15 Feb 2026] Title:FloCA: Towards Faithful and Logically Consistent Flowchart Reasoning Authors:Jinzi Zou, Bolin Wang, Liang Li, Shuo Zhang, Nuo Xu, Junzhou Zhao View a PDF of the paper titled FloCA: Towards Faithful and Logically Consistent Flowchart Reasoning, by Jinzi Zou and 5 other authors View PDF HTML (experimental) Abstract:Flowchart-oriented dialogue (FOD) systems aim to guide users through multi-turn decision-making or operational procedures by following a domain-specific flowchart to achieve a task goal. In this work, we formalize flowchart reasoning in FOD as grounding user input to flowchart nodes at each dialogue turn while ensuring node transition is consistent with the correct flowchart path. Despite recent advances of LLMs in task-oriented dialogue systems, adapting them to FOD still faces two limitations: (1) LLMs lack an explicit mechanism to represent and reason over flowchart topology, and (2) they are prone to hallucinations, leading to unfaithful flowchart reasoning. To address these limitations, we propose FloCA, a zero-shot flowchart-oriented conversational agent. FloCA uses an LLM for intent understanding and response generation while delegating flowchart reasoning to an external tool that performs topology-constrained graph execution, ensuring faithful and logically consistent node transitions across dialogue turns. We further introduce an evaluation framework with an ...