mlcommons / mlcommons/algorithmic-efficiency
Anima: live PH monitoring in conversational agent — overfitting detection every 50 interactions
Nobody has claimed this yet.
- Dominant language
- Python
- Stars
- 425
- Forks
- 78
- PR merge metrics
- No merged PRs in 30d
Description
Anima — Conversational consciousness agent
Anima integrates Persistent Homology into a live inference loop. Every 50 interactions it:
- Computes H0 persistence on class-mean direction vectors
- Checks H0 gap (train vs test proxy) for overfitting
- Logs top-3 confusion pairs from merge order
This enables self-monitoring — the agent detects when its own representations are degrading, without external validation.
Key metric: H0 gap predicts overfitting with r=0.998 correlation.
Related projects
- logout — Main research project (350+ hypotheses)
- ph-training — Standalone training pipeline
Contributor guide
First steps
- Read the whole issue, then the project's contributing guide.
- Comment on the issue to say you are picking it up — it saves two people doing the same work.
- Fork the repository and make your change on a branch.
- Open a pull request that references the issue number.
Research direction
The issue names no files, tests, or entry point in mlcommons/algorithmic-efficiency; first determine whether the proposed Anima work belongs in this repository or only in the linked projects. Use the described 50-interaction loop, H0 gap, and confusion-pair logging to define the integration and validation scope; done should mean that scope is agreed and the monitoring behavior can be verified.
Written by the indexing model from the issue text.
Assessment
- Tech stack
- python
- Domain
- machine-learning
- Issue type
- Feature
- Difficulty
- 5/5
- Estimated time
- Over a week
- Activity status
- Quiet
- Clarity
- Needs clarification
- Newbie friendliness
- 25/100