Unifying Text Semantics and Graph Structures for Temporal Text-attributed Graphs with Large Language Models
LLMs are taking Graph Neural Networks to the next level:
While we've been discussing LLMs for natural language, they're quietly changing how we represent…
Unifying Text Semantics and Graph Structures for Temporal Text-attributed Graphs with Large
MiniRAG Introduces Near-LLM Accurate RAG for Small Language Models with Just 25% of the Storage
🏆🚣MiniRAG Introduces Near-LLM Accurate RAG for Small Language Models with Just 25% of the Storage.
Achieving that by Semantic-Aware Heterogeneous Graph…
MiniRAG Introduces Near-LLM Accurate RAG for Small Language Models with Just 25% of the Storage
Agentic Deep Graph Reasoning Yields Self-Organizing Knowledge Networks
I love Markus J. Buehler's work, and his latest paper "Agentic Deep Graph Reasoning Yields Self-Organizing Knowledge Networks" does not disappoint, revealing… | 19 comments on LinkedIn
Agentic Deep Graph Reasoning Yields Self-Organizing Knowledge Networks
MiniRAG Introduces Near-LLM Accurate RAG for Small Language Models with Just 25% of the Storage
🏆🚣MiniRAG Introduces Near-LLM Accurate RAG for Small Language Models with Just 25% of the Storage.
Achieving that by Semantic-Aware Heterogeneous Graph…
MiniRAG Introduces Near-LLM Accurate RAG for Small Language Models with Just 25% of the Storage
KnowPath: Knowledge-enhanced Reasoning via LLM-generated Inference Paths over Knowledge Graphs
Breaking LLM Hallucinations in a Smarter Way!
(It’s not about feeding more data)
Large Language Models (LLMs) still struggle with factual inaccuracies, but…
KET-RAG: Turbocharging AI Agents with 10x Cheaper, Smarter Knowledge Retrieval
KET-RAG: Turbocharging AI Agents with 10x Cheaper, Smarter Knowledge Retrieval
This Multi-Granular Graph Framework uses PageRank and Keyword-Chunk Graph to have the Best Cost-Quality Tradeoff
﹌﹌﹌﹌﹌﹌﹌﹌﹌
》The Problem: Knowledge Graphs Are Expensive (and Clunky)
AI agents need context to answer complex questions—like connecting “COVID vaccines” to “myocarditis risks” across research papers. But today’s solutions face two nightmares:
✸ Cost: Building detailed knowledge graphs with LLMs can cost $33,000 for a 5GB legal case.
✸ Quality: Cheap methods (like KNN graphs) miss key relationships, leading to 32% worse answers.
☆ Imagine training an AI doctor that either bankrupts you or misdiagnoses patients. Ouch.
﹌﹌﹌﹌﹌﹌﹌﹌﹌
》The Fix: KET-RAG’s Two-Layer Brain
KET-RAG merges precision (knowledge graphs) and efficiency (keyword-text maps) into one system:
✸ Layer 1: Knowledge Graph Skeleton
☆ Uses PageRank to find core text chunks (like “vaccine side effects” in medical docs).
☆ Builds a sparse graph only on these chunks with LLMs—saving 80% of indexing costs.
✸ Layer 2: Keyword-Chunk Bipartite Graph
☆ Links keywords (e.g., “myocarditis”) to all related text snippets—no LLM needed.
☆ Acts as a “fast lane” for retrieving context without expensive entity extraction.
﹌﹌﹌﹌﹌﹌﹌﹌﹌
》Results: Beating Microsoft’s Graph-RAG with Pennies
On HotpotQA and MuSiQue benchmarks, KET-RAG:
✸ Retrieves 81.6% of critical info vs. Microsoft’s 74.6%—with 10x lower cost.
✸ Boosts answer accuracy (F1 score) by 32.4% while cutting indexing bills by 20%.
✸ Scales to terabytes of data without melting budgets.
☆ Think of it as a Tesla Model 3 outperforming a Lamborghini at 1/10th the price.
﹌﹌﹌﹌﹌﹌﹌﹌﹌
》Why AI Agents Need This
AI agents aren’t just chatbots—they’re problem solvers for medicine, law, and customer service. KET-RAG gives them:
✸ Real-time, multi-hop reasoning: Connecting “drug A → gene B → side effect C” in milliseconds.
✸ Cost-effective scalability: Deploying agents across millions of documents without going broke.
✸ Adaptability: Mixing precise knowledge graphs (for critical data) with keyword maps (for speed).
Paper in comments
≣≣≣≣≣≣≣≣≣≣≣≣≣≣≣≣≣≣≣≣≣≣≣≣≣≣
》Build Your Own Supercharged AI Agent?
🔮 Join My 𝐇𝐚𝐧𝐝𝐬-𝐎𝐧 𝐀𝐈 𝐀𝐠𝐞𝐧𝐭𝐬 𝐓𝐫𝐚𝐢𝐧𝐢𝐧𝐠 TODAY!
and Learn Building AI Agent with Langgraph/Langchain, CrewAI and OpenAI Swarm + RAG Pipelines
𝐄𝐧𝐫𝐨𝐥𝐥 𝐍𝐎𝐖 [34% discount]:
👉 https://lnkd.in/eGuWr4CH | 10 comments on LinkedIn
KET-RAG: Turbocharging AI Agents with 10x Cheaper, Smarter Knowledge Retrieval
Adaptive Graph of Thoughts (AGoT), a test-time framework that replaces rigid prompting strategies (like Chain/Tree of Thought) with dynamic directed acyclic graphs
Dynamic Reasoning Graphs + LLMs = 🤝
Large Language Models (LLMs) often stumble on complex tasks when confined to linear reasoning.
What if they could dynamically restructure their thought process like humans?
A new paper introduces Adaptive Graph of Thoughts (AGoT), a test-time framework that replaces rigid prompting strategies (like Chain/Tree of Thought) with dynamic directed acyclic graphs (DAGs).
Instead of forcing fixed reasoning steps, AGoT recursively decomposes problems into sub-tasks, selectively expanding only the most critical pathways.
This is crucial for industries like scientific research or legal analysis, where problems demand non-linear, nested reasoning.
The key innovation lies in complexity checks: AGoT assesses each reasoning node, spawning sub-graphs for intricate subtasks while resolving simpler ones directly.
This mirrors how experts allocate mental effort—drilling into uncertainties while streamlining obvious steps.
The framework achieved 46.2% improvement on GPQA (a notoriously hard science QA benchmark), rivaling gains from compute-heavy fine-tuning.
By unifying chain, tree, and graph paradigms, AGoT retains CoT’s clarity, ToT’s exploration, and GoT’s flexibility without manual tuning.
The result? LLMs that self-adapt their reasoning depth based on problem complexity—no architectural changes needed.
For AI practitioners, AGoT’s DAG structure offers a principled interface to scale reasoning modularly.
↓
𝐖𝐚𝐧𝐧𝐚 𝐤𝐧𝐨𝐰 𝐰𝐡𝐚𝐭 𝐲𝐨𝐮 𝐦𝐢𝐬𝐬𝐞𝐝? Join my newsletter with 50k+ readers that breaks down all you need to know about the latest LLM research: llmwatch.com 💡
Adaptive Graph of Thoughts (AGoT), a test-time framework that replaces rigid prompting strategies (like Chain/Tree of Thought) with dynamic directed acyclic graphs
GFM-RAG: The First Graph Foundation Model for Retrieval-Augmented Generation
🚀 Introducing GFM-RAG: The First Graph Foundation Model for Retrieval-Augmented Generation!
We’re excited to share our latest research: GFM-RAG: Graph… | 20 comments on LinkedIn
GFM-RAG: The First Graph Foundation Model for Retrieval-Augmented Generation
Dynamic Reasoning Graphs + LLMs = 🤝
Large Language Models (LLMs) often stumble on complex tasks when confined to linear reasoning.
What if they could… | 10 comments on LinkedIn
KAG: Boosting LLMs in Professional Domains via Knowledge Augmented...
The recently developed retrieval-augmented generation (RAG) technology has enabled the efficient construction of domain-specific applications. However, it also has limitations, including the gap...
Terminology Augmented Generation (TAG)? Recently some fellow terminologists have proposed the new term "Terminology-Augmented Generation (TAG)" to refer to… | 29 comments on LinkedIn
What is really Graph RAG? Inspired by "From Local to Global: A Graph RAG Approach to Query-Focused Summarization" paper from Microsoft! How do you combine… | 12 comments on LinkedIn
Knowledge Graphs as a source of trust for LLM-powered enterprise question answering
Knowledge Graphs as a source of trust for LLM-powered enterprise question answering That has been our position from the beginning when we started our research… | 29 comments on LinkedIn
Knowledge Graphs as a source of trust for LLM-powered enterprise question answering
OG-RAG: Ontology-Grounded Retrieval-Augmented Generation For Large...
This paper presents OG-RAG, an Ontology-Grounded Retrieval Augmented Generation method designed to enhance LLM-generated responses by anchoring retrieval processes in domain-specific ontologies....
Large Language Models, Knowledge Graphs and Search Engines: A...
Much has been discussed about how Large Language Models, Knowledge Graphs and Search Engines can be combined in a synergistic manner. A dimension largely absent from current academic discourse is...
Can Graph Learning Improve Planning in LLM-based Agents?
Task planning in language agents is emerging as an important research topic alongside the development of large language models (LLMs). It aims to break down complex user requests in natural...
SimGRAG is a novel method for knowledge graph driven RAG, transforms queries into graph patterns and aligns them with candidate subgraphs using a graph semantic distance metric
SimGRAG is a novel method for knowledge graph driven RAG, transforms queries into graph patterns and aligns them with candidate subgraphs using a graph…
SimGRAG is a novel method for knowledge graph driven RAG, transforms queries into graph patterns and aligns them with candidate subgraphs using a graph semantic distance metric
Graphs + Transformers = the best of both worlds 🤝 The same models powering breakthroughs in natural language processing are now being adapted for graphs…
Unlocking universal reasoning across knowledge graphs
Unlocking universal reasoning across knowledge graphs. Knowledge graphs (KGs) are powerful tools for organizing and reasoning over vast amounts of… | 11 comments on LinkedIn
Unlocking universal reasoning across knowledge graphs.
takeaways from the International Semantic Web Conference #iswc2024
My takeaways from the International Semantic Web Conference #iswc2024 Ioana keynote: Great example of data integration for journalism, highlighting the use of…
takeaways from the International Semantic Web Conference hashtag#iswc2024
Beyond Vector Space : Knowledge Graphs and the New Frontier of Agentic System Accuracy
Beyond Vector Space : Knowledge Graphs and the New Frontier of Agentic System Accuracy ⛳ In the realm of agentic systems, a fundamental challenge emerges…
Beyond Vector Space : Knowledge Graphs and the New Frontier of Agentic System Accuracy
Understanding SPARQL Queries: Are We Already There
👉 Our paper "Understanding SPARQL Queries: Are We Already There?", explores the potential of Large Language Models (#LLMs) to generate natural-language…
Understanding SPARQL Queries: Are We Already There
Knowledge Graph Enhanced Language Agents for Recommendation
Language agents have recently been used to simulate human behavior and user-item interactions for recommendation systems. However, current language agent simulations do not understand the...
More Graph, More Agents: Scaling Graph Reasoning with LLMs
More Graph, More Agents: Scaling Graph Reasoning with LLMs Graph reasoning tasks have proven to be a tough nut to crack for Large Language Models (LLMs).…
More Graph, More Agents: Scaling Graph Reasoning with LLMs
Unlocking universal reasoning across knowledge graphs. Knowledge graphs (KGs) are powerful tools for organizing and reasoning over vast amounts of… | 13 comments on LinkedIn
Fact Finder -- Enhancing Domain Expertise of Large Language Models...
Recent advancements in Large Language Models (LLMs) have showcased their proficiency in answering natural language queries. However, their effectiveness is hindered by limited domain-specific...
Recently, Retrieval-Augmented Generation (RAG) has achieved remarkable success in addressing the challenges of Large Language Models (LLMs) without necessitating retraining. By referencing an...
