Implement techniques from LongCat-Flash-Thinking-2601 Technical Report. LongCat-Flash-Thinking-2601 achieves state-of-the-art performance among open-source models on a wide range of agentic benchmarks, including agentic search, agentic tool use, and tool-integrated reasoning
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---
name: longcat-flash-thinking-2601-technical-report
title: "LongCat-Flash-Thinking-2601 Technical Report"
version: 0.0.2
engine: skillxiv-v0.0.2-claude-opus-4.6
license: MIT
url: "https://arxiv.org/abs/2601.16725"
keywords: ["agent", "model", "reasoning"]
description: "Implement techniques from LongCat-Flash-Thinking-2601 Technical Report. LongCat-Flash-Thinking-2601 achieves state-of-the-art performance among open-source models on a wide range of agentic benchmarks, including agentic search, agentic tool use, and tool-integrated reasoning"
---
## Overview
This skill implements concepts from the research paper [[2601.16725](https://arxiv.org/abs/2601.16725)].
## When to Use
- When you need to implement techniques described in this paper
- When working on problems that this research addresses
- When you want to understand the core concepts and methodology
## When NOT to Use
- This skill provides research-level insights; production implementations may require additional engineering
- Some concepts may require significant tuning for specific use cases
- Always evaluate applicability to your specific problem domain
## Key Concepts
The paper addresses: We introduce LongCat-Flash-Thinking-2601, a 560-billion-parameter open-source Mixture-of-Experts (MoE) reasoning model with superior agentic reasoning capability. LongCat-Flash-Thinking-2601 achieves state-of-the-art performance among open-source models on a wide range of agentic benchmarks, includi...
For detailed methodology and implementation details, refer to the [full paper](https://arxiv.org/html/2601.16725).