Principle:Spcl Graph of thoughts Thought Improvement
| Knowledge Sources | |
|---|---|
| Domains | Graph_Reasoning, Thought_Operations |
| Last Updated | 2026-02-14 |
| Implemented By | Implementation:Spcl_Graph_of_thoughts_Improve_Operation |
Overview
Operation pattern that refines existing thought states by prompting a language model for improvements without validation.
Description
The Improve operation is a single-pass refinement mechanism in the Graph of Thoughts framework. Unlike the more complex ValidateAndImprove operation, Improve does not include any validation logic. It simply takes each predecessor thought, prompts the language model to improve its state, parses the response for state updates, and produces a new thought with the updates merged in.
Key characteristics:
- Single-pass refinement: No validation loop or retry mechanism; each thought is improved exactly once
- Prompts the LM using the Prompter's improve_prompt method, passing the current thought state as keyword arguments
- Parses the LM response using the Parser's parse_improve_answer method to extract a state update dictionary
- Creates a new thought by merging the original state with the state update:
{**thought.state, **state_update} - Requires at least one predecessor operation (enforced by assertion)
- Always requests exactly one response from the LM per thought (
num_responses=1)
Usage
Use the Improve operation when you want to refine thought states through the language model without needing to validate the results. Common scenarios include:
- Post-merge refinement: After an Aggregate operation merges results, use Improve to let the LM clean up or optimize the merged output
- Iterative improvement pipelines: Chain multiple Improve operations or use Improve after Score-KeepBestN to refine the best candidate
- Simple refinement: When you trust the LM to produce a better version and do not need programmatic validation
This operation appears in the sorting workflow as a refinement step after the initial merge, where the LM is prompted to improve the merged sorted list.
Theoretical Basis
The Improve operation implements a transformation refinement step in the GoT graph. In graph-theoretic terms, it is a vertex with a one-to-one mapping from input thoughts to output thoughts, where each output is a refined version of its corresponding input.
Formally, given a thought t with state s, the Improve operation produces a new thought t' with state:
s' = s union delta(s)
where delta(s) is the state update produced by the language model in response to the improvement prompt. The union operator merges the dictionaries, with the update values overriding existing keys.
This is a simpler variant of the iterative refinement in ValidateAndImprove, trading the validation-driven convergence loop for a single unconditional improvement step. The simplicity makes it suitable for cases where:
- The improvement prompt is reliable enough that a single pass suffices
- Validation would be too expensive or not meaningful
- The operation is embedded in a larger pipeline that provides its own quality control (e.g., a subsequent Score-KeepBestN step)
Code Reference
The Thought Improvement principle is implemented in the Improve class:
- Source file:
graph_of_thoughts/operations/operations.py, Lines 480-533 - Class:
Improve(Operation) - Operation type:
OperationType.improve
Related Pages
- Implementation:Spcl_Graph_of_thoughts_Improve_Operation - Concrete implementation of this principle
- Principle:Spcl_Graph_of_thoughts_Validation_And_Improvement - More complex variant with validation loop
- Principle:Spcl_Graph_of_thoughts_Thought_Aggregation - Aggregate operation often precedes Improve for post-merge refinement
- Workflow:Spcl_Graph_of_thoughts_GoT_Sorting_Pipeline - Sorting workflow using Improve as a refinement step