Announcing ReasoningLens — Visualizing and Diagnosing LLM Reasoning at a Glance
TL;DR: Long-form reasoning (CoT) is a double-edged sword. While models like OpenAI o1 and DeepSeek-R1 are smarter than ever, debugging a 10,000-token reasoning trace is a nightmare. ReasoningLens turns that "Wall of Text" into an interactive, hierarchical map.
Released Artifacts
| 2026/02/03 | ||
|---|---|---|
| icip-cas/ReasoningLens | Code | https://github.com/icip-cas/ReasoningLens |
The Problem: When Transparency Becomes a Burden
The era of Large Reasoning Models (LRMs) has arrived. We love their ability to self-correct and plan, but there’s a catch: Understanding how they reached a conclusion is getting harder. When a model produces a massive reasoning trace, the "critical" logic often gets buried under repetitive procedural steps. Finding a single hallucination or a logical pivot feels like finding a needle in a haystack.
Introducing ReasoningLens 🔍
Built on top of Open WebUI, ReasoningLens is a developer-centric toolkit designed to help the open-source community visualize, understand, and debug model reasoning chains without losing their minds.
1. Hierarchical Visualization: From Chaos to Clarity
Most CoT tokens are just "execution" (doing the math), while only a few are "strategic" (deciding to change course). ReasoningLens separates the signal from the noise:
- Planning Unit Segmentation: We automatically detect logical words like "Wait, let me re-check..." or "Alternatively...".
- The Macro View (Exploration): See the high-level strategy—where the model backtracked, where it validated, and where it struggled.
- The Micro View (Exploitation): Dive deep into specific arithmetic or substitutions only when you need to.
2. Automated Error Detection: The "Agentic" Auditor
Longer reasoning doesn't always mean better reasoning. "Length-scaling" can introduce hallucinations that are hard to spot. Our SectionAnalysisAgent acts as a specialized auditor for your traces:
- ⚡ Batch-wise Analysis: Efficiently parses massive traces without losing context, making large-scale debugging feasible.
- 🧠 Rolling Summary Memory: Remembers context from prior sections, catching non-local inconsistencies and logical drift that would exhaust a human reviewer.
- 🧮 Tool-Augmented Verification: Tired of models failing at basic math? ReasoningLens integrates a calculator to verify arithmetic steps automatically.
3. Model Profiling: Beyond the Single Trace
One-off debugging is great, but systemic patterns matter more. ReasoningLens aggregates data across multiple conversations to build a Reasoning Profile of your model:
- Aggregate: Collect traces across diverse domains (Coding, Math, Logic).
- Compress: Distill recurring patterns into a compact memory state.
- Report: Generate a structured Markdown report highlighting the model's "Blind Spots" and "Consistent Strengths."
"ReasoningLens doesn't just show you what the model said; it shows you how the model thinks."
Get Started
ReasoningLens is open-source and ready to plug into your Open WebUI workflow. Stop scrolling through endless text and start seeing the logic.
Citation
@software{Zhang_ReasoningLens_2026,
author = {Zhang, Jun and Zheng, Jiasheng and Lu, Yaojie and Cao, Boxi},
license = {MIT},
month = feb,
title = {{ReasoningLens}},
url = {https://github.com/icip-cas/ReasoningLens},
version = {0.1.0},
year = {2026}
}
Future Directions
We’re just getting started. Beyond daily debugging, we are building ReasoningLens to be the definitive analysis layer for the next generation of LLMs.
- Robust Interpretability: A robust, model-agnostic pipeline that standardizes reasoning traces across different model series.
- Agentic Insights: Support for tool-use agents, specifically designed to inspect the Plan-Act-Observe cycle and trace-back tool dependencies.
- Open & Modular: An ecosystem of plugins for custom error detection and specialized scoring, making ReasoningLens a flexible foundation for the entire community.
Team & Contributions
- Jun Zhang (Main Contributor）
- Jiasheng Zheng (Contributor)
- Yaojie Lu (Contributor)
- Boxi Cao (Project Lead)
Acknowledgements
We thank the Open WebUI community and all early users and contributors for their feedback and support. We look forward to continued contributions from the open-source community. ReasoningLens is better because of your time and curiosity.
Join Us
Have questions or want to discuss ideas? Open an issue on GitHub or join the discussion in our community! Together, let's create an even more powerful tool for the community. 🌟