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

• URL: http://arxiv.org/abs/2511.10182v1
• Date: Fri, 14 Nov 2025 01:37:31 GMT
• Title: Beyond the Black Box: Demystifying Multi-Turn LLM Reasoning with VISTA
• Authors: Yiran Zhang, Mingyang Lin, Mark Dras, Usman Naseem,
• Abstract summary: VISTA is a web-based Visual Interactive System for Textual Analytics in multi-turn reasoning tasks.<n>It allows users to visualize the influence of context on model decisions and interactively modify conversation histories.<n>The platform is open-source and supports easy integration of custom benchmarks and local models.
• Score: 20.43574322731524
• License: http://creativecommons.org/licenses/by/4.0/
• 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.

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