Vis-CoT: A Human-in-the-Loop Framework for Interactive Visualization and Intervention in LLM Chain-of-Thought Reasoning
arXiv:2509.01412v3 Announce Type: replace Abstract: Large language models (LLMs) show strong reasoning via chain-of-thought (CoT) prompting, but the process is opaque, which makes verification, debugging, and control difficult in high-stakes settings. We present Vis-CoT, a human-in-the-loop framework that converts linear CoT text into an interactive reasoning graph. Users can visualize the logical flow, identify flawed steps, and intervene by pruning incorrect paths and grafting new, user-defined premises. This shifts interaction from passive observation to active collaboration, steering model
The increasing deployment of LLMs in high-stakes environments necessitates greater transparency and control over their reasoning processes.
This framework directly addresses the opaqueness of LLM chain-of-thought reasoning, which is a critical barrier to their wider adoption and trustworthy application.
Interaction with LLMs shifts from passive observation of outputs to active collaboration, allowing for real-time human intervention and correction in their reasoning.
- · AI developers
- · High-stakes industries (e.g., finance, healthcare)
- · LLM users
- · Black-box AI systems
- · LLM deployment without human oversight
Improved reliability and explainability of LLM applications, accelerating their integration into critical workflows.
Increased demand for tools and interfaces that facilitate human-AI collaboration and reasoning introspection.
Potential for new regulations requiring demonstrable human oversight or interpretability for deployed AI systems.
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Read at arXiv cs.CL