This document outlines the key next research areas to strengthen and extend SyntaxLab's pseudocode planning, versioning, and feedback refinement system. It includes a visual dependency graph and structured prompts to guide implementation.
SyntaxLab has a strong foundation in pseudocode versioning and logic planning, but to reach production-grade scale and learning efficiency, the following domains need deeper exploration:
- Develop entropy, novelty, and convergence scores
- Prevent overfitting to single plan structure
- Use Levenshtein + AST diff + semantic embeddings
- Implement plan lineage clustering (e.g., HDBSCAN, DeepWalk)
- Visual diff heatmaps to support merge suggestions
- Plan hash graphs and feature fingerprints
- Trace mutation score failures back to pseudocode steps
- Borrow from bug localization and SHAP-like explainability
- Automatically adjust prompts or logic steps based on success/failure
- Evolve logic planning strategies from data
- Convert diffs and lineage trees to human-readable summaries
- Compare plans in plain English
- Alert on quality drops after plan updates
- Track validation score drift over time
- Extract reusable subplans or motifs across similar tasks
- Leverage plan modules like code macros
- Train models to emit logic plans directly from prompts
- Use prior plan successes as training data
graph TD
%% Core Foundations
A0["π§ Plan Evolution Framework"]
A1["π Feedback Scoring & Metrics"]
A2["π Plan Diff & Lineage Graph"]
%% First Order Research
A0 --> B1["π Plan Diversity Metrics\n(e.g. entropy, semantic diff)"]
A0 --> B2["π Fork/Merge Logic\n+ Plan Clustering"]
A1 --> B3["π Validation Signal Attribution\n(e.g. blame, SHAP)"]
A1 --> B4["π Feedback Loop Refinement\n(auto-adjust prompt/steps)"]
A2 --> B5["π Human-readable Plan Diffs\n(natural language summaries)"]
A2 --> B6["π¦ Plan Regression Detection\n(reverts, drift)"]
%% Second Order Research
B1 --> C1["π Plan Space Exploration\n(graph embedding, diversity preservation)"]
B2 --> C2["𧬠Transferable Plan Modules\n(across tasks/domains)"]
B2 --> C3["π Plan Clustering Algorithms\n(HDBSCAN, GNNs)"]
B3 --> C4["π Step-level Causal Links\n(trace to mutation scores)"]
B4 --> C5["π€ Generative Plan Synthesizer\n(from learned patterns)"]
B5 --> C6["π Plan-to-Text Summarization\n(T5, GPT, etc.)"]
B6 --> C7["π¨ Alerting System for Plan Regressions"]
%% Future Enhancements
C1 --> D1["π§ Plan Prior Learning\n(offline RL, reward modeling)"]
C5 --> D2["π§ͺ Plan Pretraining\n(few-shot plan synthesis)"]
style A0 fill:#fafafa,stroke:#555,color:#000,fontWeight:bold
style A1 fill:#fafafa,stroke:#555,color:#000,fontWeight:bold
style A2 fill:#fafafa,stroke:#555,color:#000,fontWeight:bold
style D1 fill:#ccf,stroke:#55f
style D2 fill:#ccf,stroke:#55f
| Area | Research Prompt |
|---|---|
| Plan Scoring | How can plan diversity be measured across embeddings and semantic fields? |
| Fork/Merge | Whatβs the best heuristic for triggering automatic plan forks based on mutation score divergence? |
| Attribution | Can we map failed test cases back to pseudocode steps using causal attribution? |
| Regression | What rollback heuristics prevent score decay after plan merges? |
| Generation | How do we train a generative model to produce high-scoring plans directly from prompts? |
- Prototype a
scorePlanDiversity(planA, planB)tool - Run summarization experiments over lineage diffs
Let me know if you'd like these prioritized, expanded, or visualized in a Notion board or issue tracker.