LAI #74: Smarter Prompts, Context-Aware Agents, and the Math Behind KANs

Author(s): Towards AI Editorial Team

Originally published on Towards AI.

Good morning, AI enthusiasts!

Want to start this week’s issue with a quick announcement: the enrollment for the next “From Beginner to Advanced LLM Developer” cohort is open right now — and will kick off June 1st with a call with our CEO, Louie Peters!

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Now, back to this week’s issue, we start with a breakdown of dynamic and customized prompting techniques, moving beyond static instructions to more adaptive, conversational flows. Then we dive into orthogonal polynomials in Kolmogorov-Arnold Networks — yes, it’s technical, but worth it if you care about what’s under the hood of interpretability and efficiency.

There’s more: we cover the Model Context Protocol and CrewAI for scaling enterprise agents, a hybrid attention method for forecasting binary sequences, and the evolution of Bayesian Networks for real-time, probabilistic reasoning.

Enjoy the read!

— Louis-François Bouchard, Towards AI Co-founder & Head of Community

Learn AI Together Community section!

AI poll of the week!

If most research doesn’t stick, what kind of breakthrough actually changes how you build or think? Every now and then, something shifts our perspective. Was it a technique, a paper, or a tool that rewired your approach to AI? Tell us in the thread!

Collaboration Opportunities

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TAI Curated Section

Article of the week

Designing Customized and Dynamic Prompts for Large Language Models By Shenggang Li

The author reviewed techniques for creating customized and dynamic prompts for Large Language Models. Customized prompts provide fixed, task-specific instructions, whereas dynamic prompts adjust based on conversational context for more adaptive responses. The discussion covered practical implementation through manual construction, DSPy for structured workflows, the dynamic-prompting library for real-time adjustments, Jinja2 for template-based composition, and LangChain for building comprehensive LLM applications.

Our must-read articles

1. Orthogonal Polynomials in Kolmogorov-Arnold Networks: Use Case and Scenarios By Fabio Yáñez Romero

This blog reviewed Kolmogorov-Arnold Networks (KANs), noting challenges with B-Spline implementations, particularly regarding parallelization and memory. It then explored Orthogonal Polynomials (OPs) as an alternative, highlighting their advantages in computational efficiency, reduced memory requirements, and effective capture of global patterns due to properties like linear independence and recurrence relations. While OPs offer these benefits, they necessitate input normalization and may be less adept with local variations. It also discussed implementations such as Chebyshev-KAN and Legendre-KAN.

2. Model Context Protocol and CrewAI: Scaling Enterprise AI with Standardized Context By Samvardhan Singh

This article explored Model Context Protocol (MCP) and CrewAI, technologies designed to enhance enterprise AI. MCP standardizes secure access to business data for AI agents, acting as a universal translator. CrewAI orchestrates these AI agents into collaborative teams to tackle complex tasks. Together, they enable scalable, context-aware AI systems by providing a structured approach to data access and agent coordination, detailing the technical workflow from query to completion.

3. Hybrid Attention for Binary Sequence Forecasting By Shenggang Li

This article introduces a method for binary sequence forecasting, BinaryTrendFormer. It integrates n-gram embeddings, count-aware self-attention, and recency-weighted statistics. This model predicts the next binary outcome and the K-step count distribution by transforming raw data into signals fused via attention. It employs multi-task optimization and time-series cross-validation. A code experiment demonstrates its application and performance, including uncertainty interval comparisons, while noting improvement areas. It also discussed real-world uses in finance, retail, genomics, and industrial IoT.

4. From Static to Dynamic: Evolving Bayesian Network Thinking for Real-World Applications By Shenggang Li

This article explored Bayesian Networks, distinguishing between static and dynamic models. Static BNs utilize current data and conditional probabilities for immediate risk assessment, as shown in a medical pneumonia diagnosis. Dynamic BNs extend this by adding a time dimension to forecast evolving trends, demonstrated with stock market predictions. Both approaches leverage historical data and domain knowledge. The provided Python code illustrated a practical implementation for each network type.

5. Understanding LLM Agents: Concepts, Patterns & Frameworks By Allohvk

This article explored LLM agents, defining them as entities that use LLMs to solve complex tasks by interacting with environments and tools, distinguishing them from static workflows. It covered their evolution from ReAct (Reason+Act) principles, memory integration, tool use, and agentic RAG. The discussion extended to single versus multi-agent systems, key collaborative patterns, and frameworks like LangGraph. Significant developments like the Model Context Protocol (MCP) for data connectivity and Agent-to-Agent (A2A) protocol for inter-agent communication were highlighted, along with the complexities of agent evaluation.

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Published via Towards AI