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Research#LLM📝 BlogAnalyzed: Jan 4, 2026 05:51

PlanoA3B - fast, efficient and predictable multi-agent orchestration LLM for agentic apps

Published:Jan 4, 2026 01:19
1 min read
r/singularity

Analysis

This article announces the release of Plano-Orchestrator, a new family of open-source LLMs designed for fast multi-agent orchestration. It highlights the LLM's role as a supervisor agent, its multi-domain capabilities, and its efficiency for low-latency deployments. The focus is on improving real-world performance and latency in multi-agent systems. The article provides links to the open-source project and research.
Reference

““Plano-Orchestrator decides which agent(s) should handle the request and in what sequence. In other words, it acts as the supervisor agent in a multi-agent system.””

Permalinkr/singularity
Research Paper#Quantum Computing🔬 ResearchAnalyzed: Jan 3, 2026 06:22

Adaptive Resource Orchestration for Scalable Quantum Computing

Published:Dec 31, 2025 14:58
1 min read
ArXiv

Analysis

This paper addresses the critical challenge of scaling quantum computing by networking multiple quantum processing units (QPUs). The proposed ModEn-Hub architecture, with its photonic interconnect and real-time orchestrator, offers a promising solution for delivering high-fidelity entanglement and enabling non-local gate operations. The Monte Carlo study provides strong evidence that adaptive resource orchestration significantly improves teleportation success rates compared to a naive baseline, especially as the number of QPUs increases. This is a crucial step towards building practical quantum-HPC systems.
Reference

“ModEn-Hub-style orchestration sustains about 90% teleportation success while the baseline degrades toward about 30%.”

PermalinkArXiv
Research#llm🔬 ResearchAnalyzed: Jan 4, 2026 08:15

Orchestrator Multi-Agent Clinical Decision Support System for Secondary Headache Diagnosis in Primary Care

Published:Dec 3, 2025 19:26
1 min read
ArXiv

Analysis

This article describes a research paper on an AI system designed to assist in diagnosing secondary headaches in primary care settings. The system, called Orchestrator, utilizes a multi-agent approach. The focus is on applying AI to improve diagnostic accuracy and efficiency in a medical context.
Reference

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PermalinkArXiv
Research#llm📝 BlogAnalyzed: Dec 29, 2025 06:08

Speculative Decoding and Efficient LLM Inference with Chris Lott - #717

Published:Feb 4, 2025 07:23
1 min read
Practical AI

Analysis

This article from Practical AI discusses accelerating large language model (LLM) inference. It features Chris Lott from Qualcomm AI Research, focusing on the challenges of LLM encoding and decoding, and how hardware constraints impact inference metrics. The article highlights techniques like KV compression, quantization, pruning, and speculative decoding to improve performance. It also touches on future directions, including on-device agentic experiences and software tools like Qualcomm AI Orchestrator. The focus is on practical methods for optimizing LLM performance.
Reference

“We explore the challenges presented by the LLM encoding and decoding (aka generation) and how these interact with various hardware constraints such as FLOPS, memory footprint and memory bandwidth to limit key inference metrics such as time-to-first-token, tokens per second, and tokens per joule.”

PermalinkPractical AI
Research#Agent👥 CommunityAnalyzed: Jan 10, 2026 16:16

HuggingGPT: Orchestrating AI Models with ChatGPT

Published:Mar 31, 2023 17:22
1 min read
Hacker News

Analysis

The article highlights HuggingGPT, a system leveraging ChatGPT to manage and orchestrate various AI models from Hugging Face. This approach signifies a move towards more modular and accessible AI solutions.
Reference

“HuggingGPT solves AI tasks using ChatGPT and models from Hugging Face.”

PermalinkHacker News