Media Summary: Paper: RConE: Rough Cone Embedding for Multi-Hop Logical Query Answering on Multi-Modal Knowledge Graphs (2408.11526) ... Talk given by Prof. Dr. Oliver Stein from the Karlsruhe Institute of Technology (KIT), Germany, in the colloquium of the research ... You expanded the context window to 128K tokens. You added RAG. And your agent still makes the same mistakes it made ...

Solving The Granularity Gap In - Detailed Analysis & Overview

Paper: RConE: Rough Cone Embedding for Multi-Hop Logical Query Answering on Multi-Modal Knowledge Graphs (2408.11526) ... Talk given by Prof. Dr. Oliver Stein from the Karlsruhe Institute of Technology (KIT), Germany, in the colloquium of the research ... You expanded the context window to 128K tokens. You added RAG. And your agent still makes the same mistakes it made ... Ready to become a certified watsonx Generative AI Engineer? Register now and use code IBMTechYT20 for 20% off of your exam ... Analysis of Mixture of Experts (MoE) models' scaling properties introduces a new hyperparameter, Connect with me: OA Series playlist: ...

Faithful generation in large language models (LLMs) is challenged by knowledge conflicts between parametric memory and ... In this video, we delve into the problem of "Mismatched between Object-Oriented Representation and Relational Representation ... NIKSUN® NetTradeWatch™ was used to quickly detect and discover the root cause of costly sequence llm In this story, I have a super quick tutorial showing you how ... Struggling to move your RAG (Retrieval-Augmented Generation) demo into production? You're not alone. While building a basic ...

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Solving the Granularity Gap in Multi-Modal Knowledge Graphs (2408.11526)
The Granularity Gap Problem: A Hurdle for Applying Approximate Memory to Complex Data Layout
ACM ICMR 2026 Reflective Cross-Granularity Grounding with Preference Optimization
The granularity concept in mixed-integer optimization
AI Agents Don't Have a Memory Problem. They Have a Placement Problem.
RAG vs. CAG: Solving Knowledge Gaps in AI Models
Scaling Laws for Fine-Grained Mixture of Experts
Minimize the Heights II | GFG | Greedy Solution
#322 How to resolve knowledge conflicts during RAG in LLMs?
How to Solve Granularity Mismatch in Object Oriented Representation using JPA Hibernate ORM in MySQL
Detecting and Solving Sequence Gaps in a High Frequency Trading Infrastructure
LangGraph +Web Scraper + Long Term Memory + RAG  = Powerful Agentic Memory
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Solving the Granularity Gap in Multi-Modal Knowledge Graphs (2408.11526)

Solving the Granularity Gap in Multi-Modal Knowledge Graphs (2408.11526)

Paper: RConE: Rough Cone Embedding for Multi-Hop Logical Query Answering on Multi-Modal Knowledge Graphs (2408.11526) ...

The Granularity Gap Problem: A Hurdle for Applying Approximate Memory to Complex Data Layout

The Granularity Gap Problem: A Hurdle for Applying Approximate Memory to Complex Data Layout

Soramichi Akiyama and Ryota Shioya.

ACM ICMR 2026 Reflective Cross-Granularity Grounding with Preference Optimization

ACM ICMR 2026 Reflective Cross-Granularity Grounding with Preference Optimization

Reflective Cross-

The granularity concept in mixed-integer optimization

The granularity concept in mixed-integer optimization

Talk given by Prof. Dr. Oliver Stein from the Karlsruhe Institute of Technology (KIT), Germany, in the colloquium of the research ...

AI Agents Don't Have a Memory Problem. They Have a Placement Problem.

AI Agents Don't Have a Memory Problem. They Have a Placement Problem.

You expanded the context window to 128K tokens. You added RAG. And your agent still makes the same mistakes it made ...

RAG vs. CAG: Solving Knowledge Gaps in AI Models

RAG vs. CAG: Solving Knowledge Gaps in AI Models

Ready to become a certified watsonx Generative AI Engineer? Register now and use code IBMTechYT20 for 20% off of your exam ...

Scaling Laws for Fine-Grained Mixture of Experts

Scaling Laws for Fine-Grained Mixture of Experts

Analysis of Mixture of Experts (MoE) models' scaling properties introduces a new hyperparameter,

Minimize the Heights II | GFG | Greedy Solution

Minimize the Heights II | GFG | Greedy Solution

Connect with me: https://www.linkedin.com/in/sanyam-jain-229052250/ OA Series playlist: ...

#322 How to resolve knowledge conflicts during RAG in LLMs?

#322 How to resolve knowledge conflicts during RAG in LLMs?

Faithful generation in large language models (LLMs) is challenged by knowledge conflicts between parametric memory and ...

How to Solve Granularity Mismatch in Object Oriented Representation using JPA Hibernate ORM in MySQL

How to Solve Granularity Mismatch in Object Oriented Representation using JPA Hibernate ORM in MySQL

In this video, we delve into the problem of "Mismatched between Object-Oriented Representation and Relational Representation ...

Detecting and Solving Sequence Gaps in a High Frequency Trading Infrastructure

Detecting and Solving Sequence Gaps in a High Frequency Trading Infrastructure

NIKSUN® NetTradeWatch™ was used to quickly detect and discover the root cause of costly sequence

LangGraph +Web Scraper + Long Term Memory + RAG  = Powerful Agentic Memory

LangGraph +Web Scraper + Long Term Memory + RAG = Powerful Agentic Memory

llm #coding #aiagent #datascience #ai #rag #webscraping #langgraph In this story, I have a super quick tutorial showing you how ...

RAG Retrieval Deep Dive: BM25, Embeddings, and the Power of Agentic Search

RAG Retrieval Deep Dive: BM25, Embeddings, and the Power of Agentic Search

Struggling to move your RAG (Retrieval-Augmented Generation) demo into production? You're not alone. While building a basic ...