# FloCA: Towards Faithful and Logically Consistent Flowchart Reasoning

arXiv: https://arxiv.org/abs/2602.14035 · HTML: https://mlsanity.com/paper/2602.14035/

## Key facts

- No citing papers in the map yet (data through 14 Oct 2026).
- Placed in Reasoning benchmarks and evaluation, one of 17 directions in LLM reasoning.
- Builds on 36 of its 59 references that are in the map.
- First posted on arXiv 15 Feb 2026.

## Placed in

- LLM reasoning / [Reasoning benchmarks and evaluation](https://mlsanity.com/lr/reasoning-benchmarks-and-evaluation/)

## Builds on (most cited first, 30 of 36)

- [ReAct: Synergizing Reasoning and Acting in Language Models](https://mlsanity.com/paper/2210.03629/) (2022)
- [Reflexion: Language Agents with Verbal Reinforcement Learning](https://mlsanity.com/paper/2303.11366/) (2023)
- [Toolformer: Language Models Can Teach Themselves to Use Tools](https://mlsanity.com/paper/2302.04761/) (2023)
- [Tree of Thoughts: Deliberate Problem Solving with Large Language Models](https://mlsanity.com/paper/2305.10601/) (2023)
- [HuggingGPT: Solving AI Tasks with ChatGPT and its Friends in Hugging Face](https://mlsanity.com/paper/2303.17580/) (2023)
- [Graph of Thoughts: Solving Elaborate Problems with Large Language Models](https://mlsanity.com/paper/2308.09687/) (2023)
- [Logic-LM: Empowering Large Language Models with Symbolic Solvers for Faithful Logical Reasoning](https://mlsanity.com/paper/2305.12295/) (2023)
- [AgentTuning: Enabling Generalized Agent Abilities for LLMs](https://mlsanity.com/paper/2310.12823/) (2023)
- [Language is All a Graph Needs](https://mlsanity.com/paper/2308.07134/) (2023)
- [MDAgents: An Adaptive Collaboration of LLMs for Medical Decision-Making](https://mlsanity.com/paper/2404.15155/) (2024)
- [Recommender AI Agent: Integrating Large Language Models for Interactive Recommendations](https://mlsanity.com/paper/2308.16505/) (2023)
- [Empowering LLMs with Logical Reasoning: A Comprehensive Survey](https://mlsanity.com/paper/2502.15652/) (2025)
- [Large Language Models as Zero-shot Dialogue State Tracker through Function Calling](https://mlsanity.com/paper/2402.10466/) (2024)
- [Enhancing Reasoning Capabilities of LLMs via Principled Synthetic Logic Corpus](https://mlsanity.com/paper/2411.12498/) (2024)
- [FlowVQA: Mapping Multimodal Logic in Visual Question Answering with Flowcharts](https://mlsanity.com/paper/2406.19237/) (2024)
- [FlowLearn: Evaluating Large Vision-Language Models on Flowchart Understanding](https://mlsanity.com/paper/2407.05183/) (2024)
- [Divide and Translate: Compositional First-Order Logic Translation and Verification for Complex Logical Reasoning](https://mlsanity.com/paper/2410.08047/) (2024)
- [LogicAsker: Evaluating and Improving the Logical Reasoning Ability of Large Language Models](https://mlsanity.com/paper/2401.00757/) (2024)
- [Logic-of-Thought: Injecting Logic into Contexts for Full Reasoning in Large Language Models](https://mlsanity.com/paper/2409.17539/) (2024)
- [End-to-End Learning of Flowchart Grounded Task-Oriented Dialogs](https://mlsanity.com/paper/2109.07263/) (2021)
- [In-Context Learning User Simulators for Task-Oriented Dialog Systems](https://mlsanity.com/paper/2306.00774/) (2023)
- [MedDM:LLM-executable clinical guidance tree for clinical decision-making](https://mlsanity.com/paper/2312.02441/) (2023)
- [VERUS-LM: a Versatile Framework for Combining LLMs with Symbolic Reasoning](https://mlsanity.com/paper/2501.14540/) (2025)
- [Abstract Meaning Representation-Based Logic-Driven Data Augmentation for Logical Reasoning](https://mlsanity.com/paper/2305.12599/) (2023)
- [Beyond End-to-End VLMs: Leveraging Intermediate Text Representations for Superior Flowchart Understanding](https://mlsanity.com/paper/2412.16420/) (2024)
- [Rethinking Comprehensive Benchmark for Chart Understanding: A Perspective from Scientific Literature](https://mlsanity.com/paper/2412.12150/) (2024)
- [A Desideratum for Conversational Agents: Capabilities, Challenges, and Future Directions](https://mlsanity.com/paper/2504.16939/) (2025)
- [Flow2Code: Evaluating Large Language Models for Flowchart-based Code Generation Capability](https://mlsanity.com/paper/2506.02073/) (2025)
- [The Behavior Gap: Evaluating Zero-shot LLM Agents in Complex Task-Oriented Dialogs](https://mlsanity.com/paper/2506.12266/) (2025)
- [Towards Making Flowchart Images Machine Interpretable](https://mlsanity.com/paper/2501.17441/) (2025)

## Abstract (arXiv)

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 LLM-based user simulator and five new metrics covering reasoning accuracy and interaction efficiency. Extensive experiments on FLODIAL and PFDial datasets highlight the bottlenecks of existing LLM/VLM-based methods and demonstrate the superiority of FloCA. The code and dataset are publicly available at https://github.com/Jinzi-Zou/FloCA-flowchart-reasoning.