MemHarness: Memory Is Reconstructed, Not Replayed
Paper • 2607.28272 • Published • 14
How to use from
SGLangUse Docker images
docker run --gpus all \
--shm-size 32g \
-p 30000:30000 \
-v ~/.cache/huggingface:/root/.cache/huggingface \
--env "HF_TOKEN=<secret>" \
--ipc=host \
lmsysorg/sglang:latest \
python3 -m sglang.launch_server \
--model-path "KnowledgeXLab/MemHarness" \
--host 0.0.0.0 \
--port 30000# Call the server using curl (OpenAI-compatible API):
curl -X POST "http://localhost:30000/v1/completions" \
-H "Content-Type: application/json" \
--data '{
"model": "KnowledgeXLab/MemHarness",
"prompt": "Once upon a time,",
"max_tokens": 512,
"temperature": 0.5
}'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.
Install from pip and serve model
# Install SGLang from pip: pip install sglang# Start the SGLang server: python3 -m sglang.launch_server \ --model-path "KnowledgeXLab/MemHarness" \ --host 0.0.0.0 \ --port 30000# Call the server using curl (OpenAI-compatible API): curl -X POST "http://localhost:30000/v1/completions" \ -H "Content-Type: application/json" \ --data '{ "model": "KnowledgeXLab/MemHarness", "prompt": "Once upon a time,", "max_tokens": 512, "temperature": 0.5 }'