micro-scout/README.md

micro-scout

A research project for a fast, local code-context scout.

The scout is designed to find useful source snippets, account for relationships between symbols, and pass context to a larger model through a small agent harness: the loop that manages the model and its tools.

Status

The research report and experiment plan are available. Implementation, trained weights, and project-specific benchmark results are not available yet.

Documentation

• Research and development plan: related work, Graphify, architecture, data, training, evaluation, resources, and an eight-week roadmap.
• Research date: September 16, 2026.
• The current budget excludes calls to the teacher and main models. It covers training the scout and the supporting infrastructure.

Proposed architecture

Repository and working-tree changes
    → symbol graph and search indexes
    → candidate retrieval
    → small model for selection and action choice
    → source snippets with verified locations
    → larger model and solution verification

Repository facts live in an external, updatable index. The model learns to select useful context and search actions for unfamiliar projects.

One proposed training setup uses GPT-5.6 Luna to generate examples for the local scout, then evaluates the scout with GPT-6 Astra as the main solver. The research report describes how to check whether the learned retrieval behavior transfers between them.

Initial experiments

1. Build a minimal harness with search, symbol reading, and graph traversal.
2. Compare conventional search, graph search, and an existing reranker on the same tasks.
3. Measure task success, end-to-end latency, context size, and reference freshness.
4. Evaluate a custom encoder, then reduce its size.
5. If the benefit is confirmed, train action selection and search-budget allocation.

Success criterion

Reduce time to solution while maintaining task success on unfamiliar repositories. Evaluation covers the full agent loop, additional reads, and index updates, as well as individual model-call latency.

Model sizes, latency targets, and budgets in the report are hypotheses to test. They are not measured micro-scout results.