Adaptive Agents

🧭 Summary

Most modern AI systems treat intelligence as something stored inside a model.

A neural network is trained on massive datasets, its weights are adjusted, and those weights become the system’s knowledge. When the model produces an output, we interpret that output as the result of the intelligence encoded inside those parameters.

But this perspective has a limitation.

Once training is complete, the model is largely static. It does not improve through its own actions, and it does not adapt based on the outcome of its behavior unless we retrain it.

How to design a pipeline that turns vague goals into smart prompts

🧪 Summary

Why spend hours engineering prompts when AI can optimize its own instructions. This blog post introduces a novel approach toward creating a self-improving AI by treating prompts as programs. Traditional AI systems often rely on static instructions rigid and limited in adaptability. Here, we present a different perspective: viewing the Large Language Model (LLM) as a prompt compiler capable of dynamically transforming raw instructions into optimized prompts through iterative cycles of decomposition, evaluation, and intelligent reassembly.

📖 Summary

Imagine drowning in a sea of research papers, each holding a fragment of the knowledge you need for your next breakthrough. How does an AI system, striving for self-improvement, navigate this information overload to find precisely what it needs? This is the core challenge our Document Intelligence pipeline addresses, transforming chaotic documents into organized, searchable knowledge.

In this post we combine insights from Paper2Poster: Towards Multimodal Poster Automation from Scientific Papers and Domain2Vec: Vectorizing Datasets to Find the Optimal Data Mixture without Training to build an AI document profiler that transforms unstructured papers into structured, searchable knowledge graphs.