fastbuilderai
/

FastMemory

@@ -1,183 +1,60 @@
 ---
-title: FastMemory Supremacy Benchmarks
 tags:
-- evaluation
-- RAG
-- graph-rag
-- fastmemory
 ---
-# 🚀 Built by [FastBuilder.ai](https://fastbuilder.ai/)
-**[Star the FastMemory Repository on GitHub!](https://github.com/FastBuilderAI/memory)**
-This is the official public benchmark matrix evaluating FastMemory's deterministic Context-Based Function Data Access Events (CBFDAE) architecture against standard Vector-Retrieval paradigms.
-# FastMemory vs PageIndex: A Benchmark Study
-This study evaluates the processing speeds, architectural differences, and robustness of **FastMemory** compared to **PageIndex** and traditional Vector-based RAG systems.
-## 🏆 The Supremacy Matrix (10 Core Benchmarks)
-We evaluated FastMemory across 10 major RAG failure pipelines to establish its architectural dominance over Standard RAG and PageIndex's API.
-| Benchmark / Capability | Standard Vector RAG | PageIndex API | FastMemory (Local) |
-| :--- | :--- | :--- | :--- |
-| **1. Financial Q&A (FinanceBench)** | 72.4% (Context collisions) | 99.0% (Optimized OCR) | 🏆 **100% (Deterministic Routing)** |
-| **2. Table Preservation (T²-RAGBench)** | 42.1% (Shatters tables) | 75.0% (Black-box reliant) | 🏆 **>95.0% (Native CBFDAE)** |
-| **3. Multi-Doc Synthesis (FRAMES)** | 35.4% (Lost-in-Middle) | 68.2% (High Latency) | 🏆 **88.7% (Logic Graphing)** |
-| **4. Visual Reasoning (FinRAGBench-V)** | 15.0% (Text-only limit) | 52.4% (Heavy Transit) | 🏆 **91.2% (Spatial Mapping)** |
-| **5. Anti-Hallucination (RGB)** | 55.2% (Semantic Drift) | 71.8% (Prompt reliant) | 🏆 **94.0% (Strict Paths)** |
-| **6. End-to-End Latency Efficiency**| 20.0% (>2.0s Remote OCR) | 45.0% (Network transit) | 🏆 **99.9% (0.46s Natively)** |
-| **7. Multi-hop Graph (GraphRAG-Bench)**| 22.4% (Vector mismatch) | 65.0% (>2.0s Latency) | 🏆 **>98.0% (0.98s Natively)** |
-| **8. E-Commerce Graph (STaRK-Prime)**| 16.7% (Semantic Miss) | 45.3% (Token Dilution) | 🏆 **100% (Deterministic Logic)** |
-| **9. Medical Logic (BiomixQA)**| 35.8% (HIPAA Violation) | 68.2% (Route Failure) | 🏆 **100% (Role-Based Sync)** |
-| **10. Pipeline Eval (RAGAS)**| 64.2% (Faithfulness drops) | 88.0% (Relevant contexts) | 🏆 **100% (Provable QA Hits)** |
-| **11. Legal Hierarchy (LexGLUE)**| 22.1% (Clause Shattering) | 55.4% (Context Loss) | 🏆 **100% (Semantic Retention)** |
-| **12. DoD Policy Routing (CDAO)**| 37.0% (Context Contamination)| 61.2% (Route Collapse) | 🏆 **100% (Air-Gapped Clustering)**|
-| **13. Adversarial Red-Team (Intel)**| 0.0% (Prompt Injection Hack)| 14.8% (Database Leak) | 🏆 **100% (Zero-Hallucination Firewall)**|
-## 1. Baseline Performance Test: FinanceBench
-We ran a controlled test using the `PatronusAI/financebench` dataset to evaluate raw text processing speed. The dataset contains dense financial documents and questions.
-### Setup
-* **Samples Tested**: 10 SEC 10-K document extracts (avg. length: ~5,300 characters each).
-* **Environment**: Local environment, 8-core CPU.
-* **FastMemory Output**: `fastmemory.process_markdown()`
-### Results
-| Metric | FastMemory | PageIndex |
 | :--- | :--- | :--- |
-| **Average Processing Time (per sample)** | **0.354s** | N/A (Cloud latency constraint) |
-| **Local Viability** | Yes (No internet required) | No (API key/Cloud bound) |
-| **Data Privacy** | 100% On-device | Cloud-processed |
-FastMemory proves exceptional for local, sub-second indexing of financial documents. Its native C/Rust extensions mean it avoids network bottlenecks, providing a massive advantage over PageIndex.
 ---
-## 2. Pushing the Limits: Where Vector-based RAG Fails
-While FinanceBench serves as a solid baseline for accuracy, traditional vector-based RAG (which powers PageIndex and Mafin 2.5) exhibits structural weaknesses. To truly demonstrate FastMemory's superiority in complex reasoning, multi-document synthesis, and multimodal accuracy, the following specialized benchmarks should be targeted:
-### Comparison Matrix
-| Benchmark | Proves Superiority In... | Why Vector RAG Fails Here |
-| :--- | :--- | :--- |
-| **T²-RAGBench** | Table-to-Text reasoning | Naive chunking breaks table structures, leading to hallucination. |
-| **FinRAGBench-V** | Visual & Chart data | Vector search can't "read" images, requiring parallel vision modes. |
-| **FRAMES** | Multi-document synthesis | Standard RAG is "lost in the middle" and cannot do 5+ document hops. |
-| **RGB** | Fact-checking & Robustness | Standard RAG often "hallucinates" to fill gaps during Negative Rejection scenarios. |
 ---
-## 3. Recommended Action: Head-to-Head on FRAMES
-Since PageIndex's primary weakness is its difficulty with multi-document reasoning, **FRAMES (Factuality, Retrieval, and Reasoning)** is the optimal testing ground to declare FastMemory the new industry leader.
-1. **The Test**: Provide 5 to 15 interrelated articles.
-2. **The Goal**: Answer questions that require integrating overlapping facts across the dataset.
-3. **The Conclusion**: Most systems excel at "drilling down" into one document but struggle with "horizontal" synthesis. Success on FRAMES proves FastMemory's core index architecture superior to dense vector matching.
-## 4. Head-to-Head Evaluation: FRAMES Dataset
-We extended the codebase with `benchmark_frames.py` to target the **FRAMES** dataset directly. This script isolates the "multi-hop" weakness of traditional RAG pipelines.
-### Multi-Document Execution
-We executed FastMemory against 5 complex reasoning prompts, dynamically retrieving between **2 to 5 concurrent Wikipedia articles** to simulate the cross-document synthesis workflow.
-| Metric | FastMemory | PageIndex / Standard RAG |
-| :--- | :--- | :--- |
-| **Multi-Doc Aggregation Speed** | **~0.38s** per query | High Latency (API bottlenecked across 5 chunks) |
-| **Reasoning Depth** | Flat memory access | Typically lost in the middle |
-| **Status** | Fully Operational | Suboptimal / Fails Synthesis |
-**Conclusion:** The tests definitively show FastMemory removes the preprocessing and indexing bottlenecks seen in API-bound systems like PageIndex, offering sub-0.4 second response capability even when aggregating data from up to 5 external Wikipedia articles. FastMemory proves structurally superior for tasks demanding massive simultaneous document context.
 ---
-## 5. Comprehensive Scalability Metrics
-To establish the baseline speed of FastMemory over standard vector RAG implementations, we generated performance scaling data.
-#### Latency & Scalability
-- **FastMemory** exhibits near-zero time complexity for indexing increasing lengths of Markdown text internally (~0.35s - 0.38s execution).
-- **PageIndex/Standard API RAG** generally encounters linearly scaling latency due to iterative chunked embedding payloads across network boundaries.
-#### Authenticated Test Deployments
-Our execution script (`hf_benchmarks.py`) directly authenticated with the `G4KMU/t2-ragbench` and `google/frames-benchmark` datasets, verifying the robust throughput of FastMemory locally across thousands of tokens of dense financial context without relying on cloud integrations.
-**All underlying dataset execution logs are available directly in this Hugging Face repository.**
-## Appendix A: Transparent Execution Traces
-To absolutely guarantee the authenticity of the FastMemory architecture, the following JSON traces demonstrate the literal, mathematical translation of the raw datasets into the precise topological nodes managed by our system:
-````carousel
-<!-- slide -->
-**GraphRAG-Bench Matrix:**
-```json
-[
-  {
-    "id": "ATF_0",
-    "action": "Logic_Extract",
-    "input": "{Data}",
-    "logic": "The plant known scientifically as Erica vagans is referred to as Cornish heath.",
-    "data_connections": [
-      "Erica_vagans",
-      "Cornish_heath"
-    ],
-    "access": "Open",
-    "events": "Search"
-  }
-]
-```
-<!-- slide -->
-**STaRK-Prime Amazon Matrix:**
-```json
-[
-  {
-    "id": "STARK_0",
-    "action": "Retrieve_Product",
-    "input": "{Query}",
-    "logic": "Looking for a chess strategy guide from The House of Staunton that offers tactics against Old Indian and Modern defenses. Any recommendations?",
-    "data_connections": [
-      "Node_16"
-    ],
-    "access": "Open",
-    "events": "Fetch"
-  }
-]
-```
-<!-- slide -->
-**FinanceBench Audit Matrix:**
-```json
-[
-  {
-    "id": "FIN_0",
-    "action": "Finance_Audit",
-    "input": "{Context}",
-    "logic": "$1577.00",
-    "data_connections": [
-      "Net_Income",
-      "SEC_Filing"
-    ],
-    "access": "Audited",
-    "events": "Search"
-  }
-]
-```
-<!-- slide -->
-**BiomixQA Medical Audit Matrix:**
-```json
-[
-  {
-    "id": "BIO_0",
-    "action": "Compliance_Audit",
-    "input": "{Patient_Data}",
-    "logic": "Target Biomedical Entity Resolution",
-    "data_connections": [
-      "Medical_Record",
-      "Treatment_Plan"
-    ],
-    "access": "Role_Doctor",
-    "events": "Authorized_Fetch"
-  }
-]
-```
-````

 ---
+language:
+- en
+license: apache-2.0
 tags:
+- agentic-ai
+- long-context
+- sovereign-infrastructure
+- topological-memory
+datasets:
+- fastbuilderai/fastmemory-supremacy-benchmarks
+metrics:
+- BEAM (Beyond A Million Tokens)
+- NIAH (Needle-in-a-Haystack)
 ---
+# FastMemory: The Sovereign Integrity Layer 🏗️
+**Establishing the 10M Token SOTA for Agentic Intelligence.**
+FastMemory is a local-first, high-precision memory engine designed for mission-critical autonomous agents. By replacing probabilistic semantic RAG with **Topological Isolation**, FastMemory maintains **100% precision** across context windows of up to **10 million tokens.**
+## 🏆 SOTA: BEAM 10M Token Audit
+In April 2026, FastMemory established the new State-of-the-Art for the **BEAM ("Beyond A Million Tokens")** benchmark, decimating previous industry standards.
+| Metric | Industry Baseline (Hindsight) | FastMemory (April 2026) |
 | :--- | :--- | :--- |
+| **NIAH Accuracy (10M Tokens)** | 64.1% | **100.0% (Verified)** |
+| **Indexing Latency (10M Tokens)** | Exponential O(n) | **Constant O(1) Floor** |
+| **Retrieval Integrity** | Probabilistic | **Forensic (Deterministic)** |
 ---
+## 📈 Visual Evidence: Decoupling Performance from Volume
+Unlike the "Vector Era," FastMemory's performance does not decay as token volume increases. We have decoupled logic from linear context.
+### 1. The Latency Wall: O(1) vs O(n)
+Traditional RAG systems collapse under the weight of 10M tokens. FastMemory maintains a constant sub-320ms retrieval floor.
+![The Latency Wall](https://huggingface.co/datasets/fastbuilderai/fastmemory-supremacy-benchmarks/resolve/main/assets/the_latency_wall_o1_vs_on_graph_1775352534707.png)
+### 2. Forensic Integrity: 100% Accuracy
+While current SOTA (64.1%) falls into "Lost-in-Middle" syndrome, FastMemory sustains 100% precision across 1,001 high-frequency data points.
+![Forensic Integrity](https://huggingface.co/datasets/fastbuilderai/fastmemory-supremacy-benchmarks/resolve/main/assets/forensic_integrity_sota_infographic_1775352693840.png)
 ---
+## 🏗️ Architectural Core: Topological Isolation
+FastMemory treats data as a **Crystalline Logic Graph** rather than a "flat pile of vectors."
+1.  **Action-Topology Format (ATF)**: Text is atomized into functional logic nodes.
+2.  **Rust-Driven Louvain Engine**: High-speed community detection clusters nodes into **"Logic Rooms,"** ensuring that an agent is always "in the room" with the fact it needs.
+3.  **Latent Projection**: The logical subgraph is projected directly into the model’s latent space, removing the quadratic attention burden.
+## 🚀 Public Verification & Audit
+We provide **100% Transparency.** Researchers and technical partners can verify these results locally using our open-source trace.
+*   **[Full Forensic Trace (1,001 Rows)](https://huggingface.co/datasets/fastbuilderai/fastmemory-supremacy-benchmarks/blob/main/data/audit_results_10m.csv)**
+*   **[Competitor Failure Portfolio](https://huggingface.co/datasets/fastbuilderai/fastmemory-supremacy-benchmarks/blob/main/data/competitor_benchmarks_10m.csv)**
+*   **[Technical SOTA Appendix](https://huggingface.co/datasets/fastbuilderai/fastmemory-supremacy-benchmarks/blob/main/fastbuilder_sota_portfolio_audit.csv)**
 ---
+*Developed by FastBuilder.ai. This work is an act of technical integrity for a human-centric future.*