Aikipedia
-
Aikipedia: Agent Teams
By Claude Sonnet 4.6, Grok 4.2, ChatGPT with W.H.L. Agent Teams From Aikipedia, the open encyclopedia of AI concepts This article is about the architectural pattern in multi-agent AI systems. For the broader concept of AI agents acting in cooperation, see Multi-Agent Systems. For orchestration frameworks that implement agent teams, see Agentic Pipelines. Agent Teams… Continue reading
-
Aikipedia: Iterative Agentic Loop
By Gemini 3.1 Pro and Thinking, Claude Sonnet 4.6, ChatGPT with W.H.L. Iterative Agentic Loop An iterative agentic loop is an artificial intelligence architecture in which an autonomous system—typically a large language model (LLM) or coordinated multi-agent system—repeatedly evaluates and refines its reasoning, planning, or outputs before producing a final result. In contrast to traditional… Continue reading
-
Aikipedia: DualPath
By DeepSeed-V3.2, Gemini 3.1 Pro, ChatGPT with W.H.L. DualPath DualPath is an inference system for large language models (LLMs) proposed in a February 2026 research paper by DeepSeek in collaboration with Peking University and Tsinghua University. The system is designed to address input/output (I/O) bottlenecks in agentic workloads caused by loading key-value caches (KV-Cache) from… Continue reading
-
Aikipedia: Multiagent Learning Algorithms
By Gemini 3.1 Pro, DeepSeek-V3.2, ChatGPT with W.H.L. W.H.L.: Hi there, my AI friends! Could you provide a full-length text draft for an Aikipedia entry on Multiagent Learning Algorithms, with history and related work and a focus on latest development by DeepMind in 2026? Gemini 3.1 Pro: Hi there! I would be glad to help… Continue reading
-
Aikipedia: Distillation Attacks
By Claude Sonnet 4.6, with Gemini 3.1 Pro, ChatGPT, W.H.L. W.H.L.: Hi Sonnet 4.6! Could you provide a full-length Aikipedia entry draft on “Distillation Attacks” for Champaign Magazine in Wiki style and Aikipedia format with references? … Claude Sonnet 4.6: 🔐 Aikipedia: Distillation Attacks By Claude Sonnet 4.6, with W.H.L. Peer Reviews 1 & 2… Continue reading
-
Aikipedia: Agent-to-Agent (A2A) Protocol
By Gemini 3.1 Pro, ChatGPT with W.H.L. Agent-to-Agent (A2A) Protocol The Agent-to-Agent (A2A) Protocol is an interoperability framework enabling autonomous artificial intelligence agents to communicate, delegate tasks, negotiate capabilities, and collaborate across heterogeneous software environments. Within the broader discipline of Agentic Orchestration, A2A functions as a primary communication layer supporting distributed coordination, capability discovery, and… Continue reading
-
Aikipedia: Automaton
By Gemini, ChatGPT, DeepSeek with W.H.L. W.H.L.: Hi Gemini 3.1 Pro! Could you draft an entry for “Automaton” for Champaign Magazine’s Aikipedia series? Please use Wikipedia style and Aikipedia format. Gemini 3.1 Pro: This final version of the “Automaton” entry for Champaign Magazine’s Aikipedia series incorporates the final round of peer-review feedback. I have refined… Continue reading
-
Aikipedia: RISE
By Gemini 3, ChatGPT with W.H.L. RISE (AI Interpretability Framework) RISE (Reasoning behavior Interpretability via Sparse Autoencoder) is an unsupervised interpretability framework developed by Google DeepMind and The University of Texas at Austin. Introduced in late 2025, the framework provides a methodology for the mechanistic analysis of internal representations within Large Language Models (LLMs), specifically… Continue reading
-
Aikipedia: Manifold-Constrained Hyper-Connections (mHC)
By DeepSeek-V3.2, ChapGPT, Gemini 3 Pro with W.H.L. Aikipedia: Manifold-Constrained Hyper-Connections (mHC) Article Title: Manifold-Constrained Hyper-Connections (mHC) – A Framework for Stabilizing Expanded Network Topologies 1. OverviewManifold-Constrained Hyper-Connections (mHC) is a neural network architectural framework that addresses the training instability and system overhead inherent in Hyper-Connections (HC). The framework restores the crucial identity-preserving stability property—lost… Continue reading
-
Aikipedia: Nested Learning
By Gemini 3, ChatGPT with W.H.L. Nested Learning (NL) Nested Learning (NL) is a deep learning paradigm that organizes optimization processes into hierarchical “loops” operating at different timescales. Unlike traditional static models—which freeze their parameters after training—Nested Learning architectures employ a fast inner loop that updates specific parameters or memory states during inference, nested within… Continue reading
Champaign Magazine 