MemHarness: Memory Is Reconstructed, Not Replayed

This repository contains the model described in MemHarness: Memory Is Reconstructed, Not Replayed.

Paper: arXiv | Hugging Face Paper

Code: https://github.com/KnowledgeXLab/MemHarness

Description

MemHarness is a framework that equips LLM agents to actively harness and reconstruct past experiences based on the present context — instead of replaying retrieved memories verbatim. This model is a Qwen2.5-7B-Instruct based model fine-tuned with GRPO for memory-augmented decision making in agentic tasks such as ALFWorld and WebShop. It demonstrates state-of-the-art performance in both in-distribution and out-of-distribution scenarios.

Please refer to the paper for full details.