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Beyond the Black Box: Demystifying Multi-Turn LLM Reasoning with VISTA

Yiran Zhang, Mingyang Lin, Mark Dras, Usman Naseem

TL;DR

The paper tackles the challenge of diagnosing and understanding multi-turn reasoning in large language models, where long context and evolving states obscure causal factors. It presents VISTA, a web-based visual analytics platform with a client-server architecture that supports unified model/benchmark management, interactive counterfactual analysis, and automated reasoning explanations. A key feature is the Reasoning Dependency Tree, which provides a transparent, step-by-step representation of the model's inferences, and the ability to modify dialogue history and compare parallel sessions. Evaluations via a TurnBench case study demonstrate how VISTA exposes errors, enables targeted debugging, and offers an extensible framework for integrating local models and new benchmarks.

Abstract

Recent research has increasingly focused on the reasoning capabilities of Large Language Models (LLMs) in multi-turn interactions, as these scenarios more closely mirror real-world problem-solving. However, analyzing the intricate reasoning processes within these interactions presents a significant challenge due to complex contextual dependencies and a lack of specialized visualization tools, leading to a high cognitive load for researchers. To address this gap, we present VISTA, an web-based Visual Interactive System for Textual Analytics in multi-turn reasoning tasks. VISTA allows users to visualize the influence of context on model decisions and interactively modify conversation histories to conduct "what-if" analyses across different models. Furthermore, the platform can automatically parse a session and generate a reasoning dependency tree, offering a transparent view of the model's step-by-step logical path. By providing a unified and interactive framework, VISTA significantly reduces the complexity of analyzing reasoning chains, thereby facilitating a deeper understanding of the capabilities and limitations of current LLMs. The platform is open-source and supports easy integration of custom benchmarks and local models.

Beyond the Black Box: Demystifying Multi-Turn LLM Reasoning with VISTA

TL;DR

The paper tackles the challenge of diagnosing and understanding multi-turn reasoning in large language models, where long context and evolving states obscure causal factors. It presents VISTA, a web-based visual analytics platform with a client-server architecture that supports unified model/benchmark management, interactive counterfactual analysis, and automated reasoning explanations. A key feature is the Reasoning Dependency Tree, which provides a transparent, step-by-step representation of the model's inferences, and the ability to modify dialogue history and compare parallel sessions. Evaluations via a TurnBench case study demonstrate how VISTA exposes errors, enables targeted debugging, and offers an extensible framework for integrating local models and new benchmarks.

Abstract

Recent research has increasingly focused on the reasoning capabilities of Large Language Models (LLMs) in multi-turn interactions, as these scenarios more closely mirror real-world problem-solving. However, analyzing the intricate reasoning processes within these interactions presents a significant challenge due to complex contextual dependencies and a lack of specialized visualization tools, leading to a high cognitive load for researchers. To address this gap, we present VISTA, an web-based Visual Interactive System for Textual Analytics in multi-turn reasoning tasks. VISTA allows users to visualize the influence of context on model decisions and interactively modify conversation histories to conduct "what-if" analyses across different models. Furthermore, the platform can automatically parse a session and generate a reasoning dependency tree, offering a transparent view of the model's step-by-step logical path. By providing a unified and interactive framework, VISTA significantly reduces the complexity of analyzing reasoning chains, thereby facilitating a deeper understanding of the capabilities and limitations of current LLMs. The platform is open-source and supports easy integration of custom benchmarks and local models.

Paper Structure

This paper contains 7 sections, 1 figure.

Table of Contents

  1. Introduction
  2. VISTA Platform Framework
  3. Unified Model and Benchmark Management
  4. Interactive Counterfactual Analysis
  5. Automated Reasoning Explanation
  6. Case Study
  7. Conclusion

Figures (1)

  • Figure 1: Overview of the five main system pages: (a) Benchmark management; (b) Dataset management; (c) Model management; (d) Provider management; (e) Benchmarking page, showing dataset settings at the top, support information (e.g., verifiers for multi-step reasoning) on the left, and an analysis interface on the right for visualizing reasoning steps, model decisions, and editing prompts or outputs for new sessions.