MemHarness: Memory Is Reconstructed, Not Replayed
Paper • 2607.28272 • Published • 13
# Load model directly
from transformers import AutoModel
model = AutoModel.from_pretrained("KnowledgeXLab/MemHarness", device_map="auto")Quick Links
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.
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("text-generation", model="KnowledgeXLab/MemHarness")