AI & ML Research 3 Days

Last updated: July 6, 2026, 5:31 AM ET

Large Language Model Architectures & Training

Researchers are exploring new architectures and methods for building and deploying large language models. One approach focuses on optimizing Retrieval Augmented Generation (RAG) systems by assembling prompts from a base prompt and specific question rules, managed by a dispatcher that translates parsed questions into typed LLM calls Assemble Each RAG Generation. This contrasts with systems that simply return text from RAG, proposing instead a "typed answer contract" where the schema dictates the questions and ensures checkable answers, thereby preventing hallucinations Stop Returning Text RAG. Another paper analyzes PANet, a feature pyramid network that shortens the path between low-level and high-level features, offering insights into architectural efficiency PANet Paper Walkthrough. Furthermore, the complexities of long versus short context models are being weighed against cost, speed, and data requirements Long Context vs. Short. For those looking to build their own LLMs, progress is being made, although significant challenges remain Setting Up Your Own.

AI Agent Functionality & Implementation

The operational mechanisms of AI agents are under scrutiny, with a particular focus on the ReAct loop. This framework explains how agents reason, act, and observe to arrive at a final answer incrementally AI Agents Explained. Beyond theoretical frameworks, practical implementations are also being re-evaluated. One developer has replaced complex "LLM wikis" that rely on agents, embeddings, and repeated model calls with a pure Python compiler. This deterministic alternative transforms markdown into a linked, linted system, suggesting a more streamlined approach to organizing local notes LLM Wikis Over-Engineered.

Retrieval Strategies & Model Performance

Discussions around retrieval methods for LLMs are moving beyond foundational assumptions. The effectiveness of cosine similarity as the primary basis for RAG retrieval is being questioned, with alternative perspectives on retrieval mechanics being proposed Untaught Lessons RAG Retrieval. These developments aim to refine how models access and process information, impacting overall performance and accuracy.

Research Collaborations & Broader AI Impact

Significant research collaborations are emerging, with Google Deep Mind announcing a partnership with A24, marking a first-of-its-kind initiative. While specific details of this collaboration are not provided, it signals a broadening of AI research into new domains. Elsewhere, discussions on AI's societal impact continue, with a mention of a smoking "endgame" in the context of technology news, though its direct relation to AI research is tangential The Download. Separately, advancements in bio-engineering, such as a device that can revive eyeballs from dead donors, could enable eye transplants, though this falls outside the direct scope of AI and ML research device revives eyeballs.