AI & ML Research 3 Days

Last updated: June 24, 2026, 2:30 PM ET

Large Language Model Research & Development

Google Deep Mind has unveiled advancements in its Gemini family of models, introducing computer use capabilities for Gemini 3.5 Flash computer use in Gemini 3.5 Flash. This integration aims to enhance the model's ability to process and utilize computational tools, potentially expanding its application scope. Concurrently, research continues to probe the internal mechanisms of large language models. A study on Gemma 2B and 12B-IT models has identified a "three-phase factual recall circuit" within their transformer layers, detailing how facts are stored and retrieved, with the residual stream playing a significant role three-phase factual recall circuit. Further exploration into LLM knowledge retrieval suggests that "reasoning unlocks parametric knowledge," indicating a deeper connection between a model's ability to reason and its access to stored information reasoning unlocks parametric knowledge.

AI Infrastructure & Hardware

The push for more efficient AI processing is driving innovation in specialized hardware. OpenAI and Broadcom unveiled Jalapeño, a custom AI chip engineered for LLM inference, promising improvements in performance, efficiency, and scalability. This development arrives as the demand for AI computation grows, necessitating hardware tailored to these specific workloads. Separately, the foundational layer for AI in enterprises is evolving with the emergence of a "web data infrastructure layer" web data infrastructure layer. This infrastructure is critical for enterprises needing to access and process vast amounts of data, much of which is often inaccessible or unstructured, to capitalize on the burgeoning AI landscape.

AI Development Tools & Pipelines

Practitioners are refining workflows for building and deploying AI systems. For data engineers onboarding at new companies, making ETL pipelines testable is presented as a primary task, encompassing environment setup, automated testing, and AI-assisted development make ETL pipeline testable. In a move away from single-agent deployments, a multi-agent pipeline approach is being advocated, demonstrated through a text-to-SQL application multi-agent pipeline. This strategy allows for more complex task decomposition and execution. Developers are also exploring how to build local AI coding agents, with guides available for setting up Ollama and Open Code using local Gemma 4 models local AI coding agent. Furthermore, understanding loops is becoming essential for powering coding agents, with tutorials offering insights into their application within platforms like Claude Code powerful loops in Claude Code.

Retrieval-Augmented Generation (RAG) & Data Handling

Enhancements in retrieval-augmented generation (RAG) are focusing on improving how models access and filter information. A proposed method for anchor detection in RAG involves parallel detectors followed by a single LLM call, with retrieval prioritizing structured tables, keywords, table of contents, and finally embeddings anchor detection for RAG. A core mental model for enterprise RAG suggests that "retrieval is filtering, not search," advocating for filtering line dataframes and table of contents dataframes, selecting small anchors, and expanding context broadly retrieval is filtering. Strategies for handling vague user questions in RAG systems include making one focused clarification and then learning the default for subsequent interactions clarify once, learn default. Techniques for encoding categorical data for outlier detection are also being revisited, questioning the universal efficacy of one-hot encoding and exploring alternative approaches encoding categorical data.

AI Applications & Broader Impact

AI's influence extends across diverse fields, from healthcare to wildlife conservation. Stripe, Anthropic, and OpenAI are backing an initiative to combat respiratory infections, a significant public health concern. In a surprising application, GPT-5 Pro reportedly assisted an immunologist in solving a three-year-old mystery concerning T cell behavior, offering potential avenues for cancer and autoimmune research GPT-5 helped immunologist. AI warning systems are also being developed to mitigate deadly clashes between humans and elephants in India, where a large portion of the wild Asian elephant population resides outside protected areas AI warning systems for elephants. On the enterprise front, Samsung Electronics is deploying Chat GPT Enterprise and Codex to its global workforce, marking a substantial rollout of OpenAI's technology. Companies like Omio are leveraging OpenAI to build conversational travel experiences and transition into AI-native operations Omio builds conversational travel.

Open Source & Security Initiatives

OpenAI is actively contributing to the open-source ecosystem and security practices. The company is involved in building shared standards for advanced AI, supporting evaluation frameworks, safety practices, and global cooperation through initiatives like the Appia Foundation build shared standards for advanced AI. Furthermore, OpenAI has launched the Daybreak initiative, which includes tools like Codex Security and GPT-5.5-Cyber, designed to help organizations identify, validate, and patch vulnerabilities at scale Daybreak tools for security. A specific Daybreak initiative, "Patch the Planet," aims to support open-source maintainers by using AI and expert review to find and fix vulnerabilities Patch the Planet initiative. Tools and techniques for managing long-running work with AI, such as using Codex to preserve context, are also being shared Codex-maxxing for long-running work. The rise of "no-code AI" is also noted, with implications for programmers who may feel their traditional roles are becoming less specialized era of no-code AI.