fix: broken links in blog

website/blog/2024-12-18-Reasoning-Update/index.mdx

@@ -26,7 +26,7 @@ tags: [LLM, GPT, research, tutorial]
 
 ## Introduction
 
-In our [previous post](/blog/2024-12-02-ReasoningAgent2), we introduced the ReasoningAgent, which utilized Beam Search for systematic reasoning. Today, we include MCTS (Monte Carlo Tree Search) and Language Agent Tree Search (LATS) as alternative search strategies, which present advantages in different scenarios.
+In our [previous post](https://ag2ai.github.io/ag2/blog/2024/12/02/ReasoningAgent2/), we introduced the ReasoningAgent, which utilized Beam Search for systematic reasoning. Today, we include MCTS (Monte Carlo Tree Search) and Language Agent Tree Search (LATS) as alternative search strategies, which present advantages in different scenarios.
 
 Our previous ReasoningAgent draws inspiration from OpenAI's 2023 paper, [Let's Verify Step by Step](https://arxiv.org/pdf/2305.20050), as well as the 2024 [O1](https://openai.com/o1/) feature. The landscape of contemporary research is rich, with notable works such as [DeepSeek-R1](https://api-docs.deepseek.com/news/news1120), [Macro-O1](https://github.com/AIDC-AI/Marco-o1), and [OpenR](https://github.com/openreasoner/openr).
 
@@ -256,7 +256,7 @@ The new ReasoningAgent offers a flexible toolkit for systematic reasoning with L
 
 ## For Further Reading
 
-* [Original ReasoningAgent with Beam Search](/blog/2024-12-02-ReasoningAgent2)
+* [Original ReasoningAgent with Beam Search](https://ag2ai.github.io/ag2/blog/2024/12/02/ReasoningAgent2/)
 * [Documentation about ReasoningAgent](/docs/reference/agentchat/contrib/reasoning_agent)
 * [MCTS in Wikipedia](https://en.wikipedia.org/wiki/Monte_Carlo_tree_search)
 * [Example Notebook](https://ag2ai.github.io/ag2/docs/notebooks/agentchat_reasoning_agent/)