ConvMemory-CCGE-LA

ConvMemory CCGE-LA LoCoMo MPNet Seed-23 Alpha

This repository contains an alpha CCGE-LA conflict editor checkpoint for the public ConvMemory API.

CCGE-LA stands for Low-Amplitude Counterfactual Conflict Graph Editor. It is a lightweight post-ConvMemory editor for stale/current memory conflicts:

vector search -> ConvMemory -> CCGE-LA conflict-aware score edit -> memory context

Files

• ccge_la.pt: CCGE-LA editor checkpoint.
• manifest.json: training configuration and seed-23 test metrics.
• LICENSE: MIT license.

Usage

Install ConvMemory from GitHub, or from PyPI after the next package release:

pip install git+https://github.com/pth2002/ConvMemory.git

Load both checkpoints directly from Hugging Face Hub:

from convmemory import ConvMemory

model = ConvMemory.from_pretrained("Purdy0228/ConvMemory-LoCoMo-MPNet")
model.load_ccge_editor("Purdy0228/ConvMemory-CCGE-LA")

results = model.retrieve(
    query=query,
    memories=memories,
    editor="ccge_la",
    top_k=10,
)

For systems with precomputed embeddings, skip encoder loading on the base model:

model = ConvMemory.from_pretrained("Purdy0228/ConvMemory-LoCoMo-MPNet", embedding_model=False)
model.load_ccge_editor("Purdy0228/ConvMemory-CCGE-LA")
ranked = model.rerank_embeddings(
    query_embedding=query_embedding,
    memory_embeddings=memory_embeddings,
    memory_ids=memory_ids,
    memory_texts=memory_texts,
    query=query,
    editor="ccge_la",
)

Metrics

These are seed-23 test metrics from the release manifest. This is an alpha checkpoint, not a final benchmark release.

Training Notes

• Base checkpoint: Purdy0228/ConvMemory-LoCoMo-MPNet.
• Training split seed: 23.
• Candidate top-n: 192.
• Objective: retrieval cross-entropy plus a low-amplitude gate budget penalty.
• No current/stale labels, no gold-defined feature, and no distillation objective are used by the editor.

Limitations

• This is a public alpha checkpoint trained on a single LoCoMo-style seed-23 split.
• It is intended for API trials and early integration, not as a final benchmark claim.
• It should be used with the matching MPNet-family ConvMemory checkpoint.
• No inference widget is provided; use the convmemory Python library.

Citation

A formal citation will be added when a technical report is available.

Links

• GitHub: https://github.com/pth2002/ConvMemory
• Base ConvMemory checkpoint: https://huggingface.co/Purdy0228/ConvMemory-LoCoMo-MPNet
• CCGE-LA docs: https://github.com/pth2002/ConvMemory/blob/main/docs/CCGE_LA.md