prabhatkr/sanskrit-ppo-benchmarks
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prabhatkr
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posted an update about 2 hours ago
Why did FastMemory achieve 100% accuracy at 10 million tokens while the SOTA is stalling at 64%?
It’s not because we have a better "retriever." It’s because we changed the Topology of Truth.
Standard RAG treats your data like a pile of leaves. You search for a leaf, and you hope you find the right one. As the pile grows to 10 million leaves, you inevitably fail.
FastMemory treats your data like a Building.
Topological Isolation: We use Rust-driven Louvain clustering to atomize text into "Logic Rooms."
Crystalline Grounding: Facts don't "decay" in our context; they are locked into a deterministic graph.
Sub-Second O(1) Routing: We don't "search" the haystack; we navigate the floor plan.
If your AI is mission-critical, "mostly right" is a liability. It’s time to move beyond the RAG pile and into the topological foundation.
Verify Yourself: https://huggingface.co/datasets/fastbuilderai/fastmemory-supremacy-benchmarks/blob/main/README.md
#MachineLearning #GraphAI #LouvainClustering #FastBuilder #EngineeringExcellence
updated a dataset about 7 hours ago
fastbuilderai/fastmemory-supremacy-benchmarks posted an update 4 days ago
AI fails to scale because it wasn't trained on the data it is expected to work.
Enterprise data is messier than the internet and web data that has been used to train the general models. That's why the bootup response of AI seems delightful. But that glory fades when rubber meets the road.
The other approaches like RAG, KG and Ontologies proved unworthy too.
In a recent hot out-of-the-press research, Topology beats RAG/KG/Ontology by a huge margin.
Download PDF: https://arxiv.org/html/2603.12458v1
Blog: https://fastbuilder.ai/blog/why-ai-fails-to-scale-why-topology-is-the-top-choice-for-the-enterprise
FastMemory is a topology builder for direct AI integration. Just 3 lines of code to build topology and wrap it in your LLM queries.
❌ You don't have to build embedding pipelines. Also no $$$ spent of embedding token usage.
❌ You also don't have to invest in heavy vector storage which is bigger in size than the underlying data. Vectors are 20%-50% bigger in size than text data storage.
✅ You only need the python app running the 'fastmemory' library and store the topology as graph in Neo4J or graphDB or any such storage.
✅ The stored topology is 10X-30X smaller than the data.
Get the magic of AI right away similar to native AI performance without heavy infra.
#topology #AI #RAG #Ontology #knowledgegraph #fastmemoryOrganizations
