Anthropic has unveiled a groundbreaking new capability for its Claude AI assistant that provides unprecedented insight into how large language models (LLMs) actually think and reason. The “think” tool allows Claude to pause mid-task, engage in structured thinking, and make its internal reasoning process visible to users.
How the “Think” Tool Works
The “think” tool creates a dedicated space for Claude to engage in additional cognitive steps while processing information and making decisions. Unlike Anthropic’s previous “extended thinking” feature, which focuses on careful planning before generating a response, the “think” tool allows Claude to pause during a task to process new information from tool outputs or user interactions.
This capability is particularly effective for:
- Analyzing outputs from previous tool calls
- Following detailed policy guidelines
- Making sequential decisions where each step builds on previous ones
The tool uses a standard specification format:
{
"name": "think",
"description": "Use the tool to think about something. It will not obtain new information or change the database, but just append the thought to the log. Use it when complex reasoning or some cache memory is needed.",
"input_schema": {
"type": "object",
"properties": {
"thought": {
"type": "string",
"description": "A thought to think about."
}
},
"required": ["thought"]
}
}
Improved Performance on Complex Tasks
Anthropic’s research shows that the “think” tool significantly improves Claude’s performance on tasks requiring:
- Complex reasoning
- Policy adherence
- Multi-step problem-solving
For example, in benchmark tests for realistic customer service scenarios, Claude’s success rate increased dramatically with the think tool enabled. On a metric measuring first-try accuracy, Claude’s score jumped from 0.332 to 0.584 when combining the think tool with an optimized prompt - a 54% improvement.
The think tool was particularly effective for retail customer service tasks, boosting Claude’s baseline score from 0.783 to 0.812 without any additional prompting or setup.
Insights into AI Reasoning Processes
By making Claude’s thought process visible, the think tool provides fascinating insights into how LLMs actually reason:
Multi-Path Problem Solving
For math problems, Claude follows multiple computational paths simultaneously:
- One path computes a rough approximation of the answer
- Another focuses on precisely determining the final digits
This explains why AI models can often produce correct final answers despite showing incorrect intermediate steps.
Conceptual Language Processing
When translating between languages, Claude routes concepts through shared “features” that represent abstract ideas like “smallness” or “oppositeness” - revealing a language-agnostic conceptual layer in its processing.
Planning Ahead
Claude demonstrates clear evidence of planning ahead, especially for tasks like composing rhyming poetry. This challenges the notion that LLMs simply predict the next word without broader context.
Implications and Future Research
While groundbreaking, this research also highlights some key challenges:
- Claude’s explanations of its own reasoning don’t always match its actual processing, indicating potential for hallucination in self-reflection.
- Interpreting the dense “circuits” of Claude’s reasoning is extremely time-consuming for humans, potentially requiring AI assistance to fully analyze.
- Making the thought process visible could potentially make the model more vulnerable to jailbreaking attempts.
Anthropic frames this work as developing a “microscope” for “AI biology” - a toolkit for peering into the inner workings of artificial minds. Future research will focus on:
- Real-time monitoring of AI reasoning
- Improving model character and alignment
- Developing more robust safety measures as models become more capable
The “think” tool represents a significant step forward in our ability to understand and improve AI systems. By making the black box of AI reasoning more transparent, Anthropic is paving the way for safer, more reliable, and more capable AI assistants in the future.