Tiermem
Description
From Lossy to Verified: A Provenance-Aware Tiered Memory for Agents
Installation
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README
From Lossy to Verified: A Provenance-Aware Tiered Memory for Agents
[](https://www.python.org/downloads/) [](https://opensource.org/licenses/MIT) [](https://huggingface.co/FreedomIntelligence/TierMem) [](https://arxiv.org/abs/2602.17913)
**A memory-augmented LLM system for long-context question answering with intelligent routing between summary-based and raw-retrieval pipelines.**
[Installation](#installation) • [Quick Start](#quick-start) • [Model](#pretrained-model) • [Benchmarks](#supported-benchmarks) • [Training](#router-training)
Table of Contents
- Overview
- Key Features
- Architecture
- Installation
- Pretrained Model
- Quick Start
- Project Structure
- Router Training
- Supported Benchmarks
- Results
- Configuration
- Citation
- Contributing
- License
- Acknowledgments
Overview
TierMem implements a two-tier memory architecture that balances efficiency and accuracy for long-context question answering:
- Summary Index (S-path): Fast semantic search over extracted facts using Mem0
- Page Store (R-path): Raw conversation chunks with BM25 retrieval for detailed context
A trained router model dynamically selects between these paths based on query complexity, ensuring optimal performance across different types of questions.
Key Features
✨ **Intelligent Routing** - Trained router automatically selects the best retrieval strategy 🚀 **High Performance** - Optimized fo
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