ModalityDance / EngramEdit-LongCat-Flash-Lite-zsRE-2K

EngramEdit: ZsRE · 2K

Overview

Conditional memory expands an LLM's capacity through learned n-gram embeddings that participate in its computation. EngramEdit enables decoupled knowledge updates by updating these embeddings while keeping the Transformer backbone fixed. Revised facts remain usable across different expressions and in multi-hop reasoning, while unrelated knowledge and general capabilities are largely preserved.

This repository provides the EngramEdit checkpoint for 2,000 ZsRE edits on LongCat-Flash-Lite . The checkpoint contains the learned conditional memory updates. Load it together with the base model using the EngramEdit code below.

Links

📄 Paper · 🤗 Hugging Face Paper · 🌐 Project Page · 💻 GitHub · 🧪 Usage Example · 📊 Evaluation

Checkpoints: 🤗 CounterFact · 2K · 🤗 ZsRE · 2K · 🤗 MQuAKE · 3K

Usage

Clone the code repository , follow its installation instructions , and run the example from that checkout. Download the base model once:

hf download meituan-longcat/LongCat-Flash-Lite --local-dir data/LongCat-Flash-Lite

The example compares the model's response before and after loading the checkpoint. No editing, expression generation, or frequency-cache preparation is needed.

This example uses a ZsRE question and its target answer. The code prints the actual responses before and after loading the checkpoint.

from pathlib import Path

import torch
from huggingface_hub import hf_hub_download

from experiments.utils import load_model
from EngramEdit import prepare_engramedit_model

# Load the base model.
model, tokenizer = load_model("data/LongCat-Flash-Lite", "bfloat16")
prompt = "What university did Watts Humphrey attend?"
target = "Illinois Institute of Technology"


@torch.inference_mode()
def answer(prompt):
    inputs = tokenizer(prompt, return_tensors="pt").to(
        model.get_input_embeddings().weight.device
    )
    output = model.generate(
        **inputs, do_sample=False, max_new_tokens=32,
        pad_token_id=tokenizer.pad_token_id,
    )
    return tokenizer.decode(
        output[0, inputs.input_ids.shape[1]:], skip_special_tokens=True
    )


# Inspect the query and the target fact.
print("Prompt:", prompt)
# Prompt: What university did Watts Humphrey attend?
print("Target answer:", target)
# Target answer: Illinois Institute of Technology

print("Before loading:", answer(prompt))

# Download and load the learned conditional memory updates.
state_path = hf_hub_download(
    repo_id="ModalityDance/EngramEdit-LongCat-Flash-Lite-ZsRE-2K",
    filename="engramedit_state.pt",
    local_dir="checkpoints/zsre",
)
model = prepare_engramedit_model(model, state_dir=Path(state_path).parent)

# Ask the same question with the checkpoint loaded.
print("After loading:", answer(prompt))

Citation

@misc{cai2026engramedit,
  title  = {EngramEdit: Decoupled Knowledge Updates in LLMs through Conditional Memory},
  author = {Hongru Cai and Ran Wei and Wenjie Wang and Chengfa Wu and Ning Song and Yongqi Li and Wenjie Li},
  year   = {2026},
  eprint = {2610.10533},
  archivePrefix = {arXiv},
  primaryClass = {cs.CL},
  url    = {https://arxiv.org/abs/2610.10533}
}

License

This checkpoint is released under the MIT License . The base model is distributed separately under its own license .