Retrieval-Augmented Multi-LLM Ensemble for Industrial Part Specification Extraction

⋅

arXiv:2601.05266v1 Announce Type: new Abstract: Industrial part specification extraction from unstructured text remains a persistent challenge in manufacturing, procurement, and maintenance, where manual processing is both time-consuming and error-prone. This paper introduces a retrieval-augmented multi-LLM ensemble framework that orchestrates nine state-of-the-art Large Language Models (LLMs) within a structured three-phase pipeline. RAGsemble addresses key limitations of single-model systems by combining the complementary strengths of model families including Gemini (2.0, 2.5, 1.5), OpenAI (GPT-4o, o4-mini), Mistral Large, and Gemma (1B, […]

Ver mais

Liked Liked

Nearly Half of Enterprises Waste Millions on Underutilized GPU Capacity

⋅

ClearML’s 2025–2026 State of AI Infrastructure report reveals a costly disconnect in enterprise AI operations: nearly half of organizations waste millions on idle GPU capacity while AI teams wait in queues. Manual provisioning, weak automation, vendor lock-in, and immature governance block ROI just as AI agents and large-scale deployments accelerate.

Ver mais

Liked Liked

[D] AI Research laptop, what’s your setup?

⋅

Dear all, first time writing here. I’m a deep learning PhD student trying to decide between a MacBook Air 15 (M4, 32 GB, 1 TB) and a ThinkPad P14s with Ubuntu and an NVIDIA RTX Pro 1000. For context, I originally used a MacBook for years, then switched to a ThinkPad and have been on Ubuntu for a while now. My current machine is an X1 Carbon 7 gen with no GPU, since all heavy training runs on […]

Ver mais

Liked Liked

Semi-Supervised Mixture Models under the Concept of Missing at Radom with Margin Confidence and Aranda Ordaz Function

⋅

arXiv:2601.14631v1 Announce Type: new Abstract: This paper presents a semi-supervised learning framework for Gaussian mixture modelling under a Missing at Random (MAR) mechanism. The method explicitly parameterizes the missingness mechanism by modelling the probability of missingness as a function of classification uncertainty. To quantify classification uncertainty, we introduce margin confidence and incorporate the Aranda Ordaz (AO) link function to flexibly capture the asymmetric relationships between uncertainty and missing probability. Based on this formulation, we develop an efficient Expectation […]

Ver mais

Liked Liked
I Tested a 7B Model That Beat Models 7× Its Size. Here’s What I Found.

I Tested a 7B Model That Beat Models 7× Its Size. Here’s What I Found.

⋅

Author(s): Adham Khaled Originally published on Towards AI. The Falcon-H1R doesn’t make sense on paper. Until you understand what UAE’s TII actually built. Last Saturday, I downloaded a model that shouldn’t exist. 7 billion parameters. Open-source. From Abu Dhabi. On paper, it’s nothing special. The AI world runs on models 10×, 20×, even 100× this size.​ Then I ran the benchmarks. AIME-24 mathematics: 88.1%. That’s better than ServiceNow’s Apriel 1.5 — a 15-billion parameter model that scored 86.2%.​ […]

Ver mais

Liked Liked

A Lightweight Medical Image Classification Framework via Self-Supervised Contrastive Learning and Quantum-Enhanced Feature Modeling

⋅

Intelligent medical image analysis is essential for clinical decision support but is often limited by scarce annotations, constrained computational resources, and suboptimal model generalization. To address these challenges, we propose a lightweight medical image classification framework that integrates self-supervised contrastive learning with quantum-enhanced feature modeling. MobileNetV2 is employed as a compact backbone and pretrained using a SimCLR-style self-supervised paradigm on unlabeled images. A lightweight parameterized quantum circuit (PQC) is embedded as a quantum feature enhancement module, forming a […]

Ver mais

Liked Liked
Botober 2025: Terrible recipes from a tiny neural net

Botober 2025: Terrible recipes from a tiny neural net

⋅

After seeing generated text evolve from the days of tiny neural networks to today’s ChatGPT-style large language models, I have to conclude: there’s something special about the tiny guys. Maybe it’s the way the tiny neural networks string together text letter by letter just based on what you’ve given it, rather than drawing from prior internet training. It’s not secretly drawing on some dark corner of the internet, it’s just mashing together statistically likely letter combinations. So, when […]

Ver mais

Liked Liked
Starving yourself is unproductive, but what happens when you starve your LLMs…of context?

Starving yourself is unproductive, but what happens when you starve your LLMs…of context?

⋅

Author(s): Surya Maddula Originally published on Towards AI. Starving your LLMs might be the key to contextual prompt reduction. LLMs have remarkable capabilities for nlp tasks, but when deploying them, there’s always been a few challenges because of two main reasons: computational costs and memory constraints. And this is especially true when processing lengthy prompts. Image explaining prompt length challenges.The article discusses various strategies and techniques for reducing prompt sizes when working with large language models (LLMs) without […]

Ver mais

Liked Liked

[R] Good modern alternatives to Perceiver/PercieverIO for datasets with many modalities?

⋅

I’ve been working on developing foundation models for massively multimodal datasets (around 30-40 different modalities on 1 dataset, you can kind of think of it like robot with a lot of different sensors). I think most scientific papers I see from the last couple years use Perceiver, which I feel is a really intuitive and elegant solution (like you literally just slap on name of modality + the data and let it handle the rest). However, it is […]

Ver mais

Liked Liked

Load More

↑

Digitado © 2025