research outline
excerpt
ARC Challenge: A Dialogue-Based Approach
Core Philosophy
The Abstraction and Reasoning Corpus (ARC) challenge may not be primarily an engineering problem, but rather an artistic and creative challenge in how we interact with intelligence systems. This perspective stems from several key observations:
LLMs process information fundamentally differently from humans:
They don’t “see” like we do
They experience time and sequence differently
Their perception is based on streams of characters rather than visual processing
Their outputs aren’t deterministic - same inputs can generate different results
The focus should be on perception and understanding before solution:
Can the system truly perceive what the puzzle is about?
Can it articulate the transformation in natural language?
Can it validate its understanding through code exploration?
Methodological Approach
Dialogue-Based Investigation
The core methodology revolves around dialogue, mirroring how humans naturally solve problems through internal discussion. This involves:
Progressive building of observations
Step-by-step examination of puzzle elements
Encouraging the LLM to think aloud and document its observations
Using code execution to validate perceptions
Natural Language Programming
Rather than immediately jumping to solution code, the focus is on developing a natural language program that:
Describes the transformation process clearly
Can be validated against example inputs
Serves as a bridge between perception and implementation
Maintains consistency in vocabulary and description patterns
Workflow Structure
The process follows a systematic approach:
Examples Phase:
Examine each example input-output pair thoroughly
Build comprehensive observations
Allow for code-based investigation
Summarize patterns and transformations
Pre-Testing Phase:
Attempt to recreate known example outputs
Validate understanding before moving to test cases
Iterate until consistent success is achieved
Testing Phase:
Apply validated understanding to new inputs
Use dialogue to refine approach
Implement solution through standard operations (setPixel, etc.)
Validate results before submission
Technical Implementation
Model Considerations
Focus on multimodal models for better perception
Utilize code execution capabilities when available
Consider various parameter settings (temperature, top_k, etc.)
Test across different model types and configurations
Presentation Variables
Grid Representation:
Text-based vs. JSON format
Delimiter choices for rows and cells
Color mapping strategies
Inclusion of numeric symbols in cells
Image Presentation:
Grid size and scaling
Color separation lines
Visual vs. textual representation
Base64 encoding implications
System Instructions
Provide clear context about ARC’s scientific objectives
Establish consistent vocabulary for grid operations
Encourage creative and open-minded interpretation
Balance context retention with fresh thinking
Focus on perception before solution
Documentation and Analysis
Session Recording
Track all variables and parameters
Document model metadata
Record processing times and token usage
Track success metrics (correct pixels, colors, size)
Reporting Structure
Generate RST files for documentation
Enable easy navigation of results
Support annotation for insights
Aggregate results for analysis
Future Directions
Perception Testing
Develop comprehensive tests for basic visual capabilities
Understand limitations and strengths of different approaches
Refine presentation methods based on results
Pattern Library
Build collection of successful natural language programs
Develop consistent vocabulary for transformations
Create reusable patterns for common operations
Optimization
Refine system instructions based on results
Identify most effective parameter combinations
Develop better validation strategies
The ultimate goal is not just to solve individual puzzles, but to understand and develop better ways of interfacing with AI systems for complex reasoning tasks. This approach acknowledges the fundamental differences between human and machine intelligence while seeking to build effective bridges between them.