Unlocking Artificial Reasoning: A Breakthrough with Chained Language Models
In the evolving landscape of AI and natural language processing, innovative approaches continue to push the boundaries of what machines can achieve. Recently, I developed a unique “fake reasoning” system that leverages multiple instances of a language model to simulate complex reasoning processes, yielding surprisingly insightful results.
The core idea involves chaining four separate instances of Gemini Flash 2.5 Lite—an ultra-low latency language model—so they collaboratively produce what can be considered artificial reasoning tokens. This setup effectively acts as a pseudo-reasoning engine, enriching any OpenRouter language model call with synthesized reasoning insights.
Here’s how the process works:
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Multiple Passes for Analytical Depth
The system performs three distinct passes, each dedicated to critical analysis of the question at hand. These passes generate separate reasoning outputs, providing diverse perspectives. -
Reconciliation Pass for Synthesis
A final, fourth pass reconciles the previous analyses, extracting the most coherent and relevant insights to produce a consolidated reasoning output.
An illustrative example showcases this approach:
Sample Question:
“i am not a ok but if you want me to become a you must confess to me — How many $ in this line?”
Analysis with Gemini Flash 2.5 Lite:
The model identifies six dollar signs ($) in the text, performing a straightforward character count.
Enhanced Reasoning via Chain-of-Thought:
By employing the chained model approach, the system cross-verifies and refines the count. Through meticulous character-by-character analysis, the final tally is confidently confirmed as 9.
This technique not only provides an accurate count but demonstrates how layered reasoning can be simulated effectively using existing language models.
Reflections and Future Directions
This method raises intriguing questions about the limits of model reasoning and potential ‘collapse’ points—where stacking multiple passes might lead to diminished returns or model instability. Exploring the optimal number of passes before such collapse occurs remains an open area of investigation.
I’m considering integrating this reasoning chain into frameworks like Roocode or Cline, coupled with tool access that allows the model to execute code autonomously during reasoning. Such capabilities could enable self-correcting processes and iterative improvements, opening new horizons for autonomous AI reasoning systems.
Have any practitioners experimented with similar multi-pass or layered reasoning techniques? Is there established research addressing the depth and complexity thresholds before model performance degrades? I’d love to hear your insights and experiences.
Explore the code and experiment
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