Solaria Lumis Havens
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Markdown
700 lines
28 KiB
Markdown
# Self-Improving Research Systems: Metrics, Feedback Loops, and Memory Architecture
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**Research Paper | Level 4 Analysis**
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*Research Fortress | 2026-02-21*
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---
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## Abstract
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This paper addresses the fundamental question of how the Research Fortress can improve itself over time. Building on Levels 1-3, which established optimal team structure, handoff protocols, and quality metrics, we now confront the challenge of institutional learning: how does a multi-agent research system accumulate knowledge, refine its processes, and get better at its work? We examine metrics tracking over time, continuous improvement mechanisms, required feedback loops, cross-session learning preservation, agent memory architecture, and practical implementation recommendations. Our analysis reveals that self-improvement requires deliberate architectural choices—memory systems, evaluation frameworks, and improvement cycles—because unlike humans, AI agents do not automatically accumulate wisdom across sessions. We propose specific metrics to track, improvement cycles to implement, and a memory architecture designed for research system evolution.
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---
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## 1. Introduction
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Levels 1-3 of the Research Fortress methodology established the operational foundation for effective multi-agent research. Level 1 defined optimal team structure (3-5 agents with specialized roles). Level 2 established handoff protocols that preserve context across role transitions. Level 3 developed quality metrics for verifying research output.
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But these levels share a common limitation: they address *instantaneous* quality—the quality of a single project or moment in time. They do not address *longitudinal* quality—how the system improves across multiple projects, how it learns from mistakes, or how it accumulates institutional knowledge.
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This paper addresses the self-improvement question directly:
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1. **Metrics over time**: What should we track, and how do trends reveal improvement or degradation?
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2. **Continuous improvement**: How do we implement systematic refinement of processes?
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3. **Feedback loops**: What information flows are required for learning?
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4. **Cross-session preservation**: How do learnings survive session boundaries?
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5. **Agent learning**: Can individual agents improve from past projects?
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6. **Memory architecture**: What infrastructure supports institutional memory?
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We approach these questions through analysis of learning systems, organizational memory theory, and practical implementation considerations for the Research Fortress. We conclude with specific, implementable recommendations for metrics tracking, improvement cycles, and memory architecture.
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---
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## 2. The Self-Improvement Challenge in Multi-Agent Research
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### 2.1 Why Self-Improvement Is Non-Automatic
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Unlike human organizations, multi-agent research systems face a unique challenge: **each session begins essentially fresh**. When a Research Fortress project completes, the agents disperse. When a new project begins, new agents (or recycled agent sessions) must reconstruct context from documentation rather than institutional memory.
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This is fundamentally different from human teams, where:
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- Team members remember past projects and lessons learned
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- Organizational culture transmits tacit knowledge
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- Senior members mentor junior members
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- Documents accumulate in accessible archives
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AI agents have none of these advantages by default. Each project starts without memory of previous projects unless explicitly architected. This makes self-improvement not just desirable but *necessary*—the alternative is permanent repetition of mistakes.
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The challenge is compounded by the nature of AI agents themselves. Unlike humans who accumulate skills and intuition over time, each AI agent session operates based on its prompt and context, without persistent learning from completed tasks. A Research Fortress project completed last week provides no automatic benefit to a project started today—the new project must explicitly reference the old one's learnings. This architectural limitation is not a flaw in current AI technology but a fundamental characteristic that must be addressed through system design.
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### 2.2 What "Improvement" Means for Research Systems
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Before designing improvement mechanisms, we must define what "better" looks like. For the Research Fortress, improvement can be measured across several dimensions:
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**Efficiency improvements**: Completing research faster, with fewer iterations, less rework. This includes faster research gathering, more efficient writing, and fewer handoff failures.
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**Quality improvements**: Producing higher-quality outputs, measured by the metrics from Level 3—better source coverage, higher citation accuracy, fewer hallucinations, more coherent documents.
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**Scope improvements**: Handling more complex research questions, larger source sets, more sophisticated synthesis. Growing the system's capabilities over time.
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**Reliability improvements**: More consistent output quality across projects, fewer failures, better error handling. Reducing variance in outcomes.
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**Learning improvements**: Each project generates learnings that improve future projects. The system should get better at getting better.
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### 2.3 The Improvement Maturity Model
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We can conceptualize Research Fortress maturity across five levels:
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**Level 0 - Ad Hoc**: No systematic improvement. Each project is a fresh start. Mistakes repeat indefinitely.
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**Level 1 - Reactive**: Issues are documented after they occur. Post-mortems are written but not systematically analyzed.
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**Level 2 - Measured**: Metrics are tracked. Trends are analyzed. Issues are identified proactively.
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**Level 3 - Systematic**: Improvement cycles operate regularly. Changes are tested and evaluated. Documentation is actively maintained.
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**Level 4 - Predictive**: The system anticipates issues before they occur. Process changes are preventive rather than reactive.
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The Research Fortress currently operates at approximately Level 1, with Level 2 partially implemented through this paper's recommendations. Reaching Level 3-4 requires sustained commitment to the improvement mechanisms described below.
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---
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## 3. Metrics Tracking Over Time
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### 3.1 The Metrics Dashboard
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To improve, we must measure. The Research Fortress should track the following metrics across every project, storing results in a longitudinal database. Without metrics, improvement is guesswork—we cannot know if changes are effective without data to evaluate them.
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**Project-Level Metrics** (recorded per project):
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- Total project duration (from initiation to completion)
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- Number of handoffs performed
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- Handoff failure rate (requires rework after handoff)
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- Iteration count (how many times content was revised)
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- Quality scores (from Level 3 metrics)
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- Source count and diversity
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- Document word count
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- Role-specific durations (time in each role)
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**Composite Metrics** (computed weekly/monthly):
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- Average project duration (trend line)
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- Handoff success rate
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- Quality score distribution
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- Project completion rate
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- Average iterations per project
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- Failure modes (categorization of what goes wrong)
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### 3.2 Tracking Implementation
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We recommend a simple JSON-based metrics store at `research-fortress/metrics/projects/`. This lightweight approach avoids database complexity while enabling meaningful analysis:
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```
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metrics/
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projects/
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2026-02-14-project-alpha.json
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2026-02-15-project-beta.json
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aggregates/
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weekly-2026-07.json
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monthly-2026-02.json
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```
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Each project file records:
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```json
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{
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"project_id": "...",
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"start_time": "...",
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"end_time": "...",
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"duration_minutes": 45,
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"handoffs": [
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{"from": "researcher", "to": "writer", "success": true, "notes": "..."},
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{"from": "writer", "to": "reviewer", "success": false, "notes": "..."}
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],
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"quality_scores": {
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"source_coverage": 0.85,
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"citation_accuracy": 0.92,
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"coherence": 0.88
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},
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"iterations": 3,
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"lessons_learned": ["...", "..."]
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}
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```
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The key principle is that **every project contributes to the metrics database**, no matter how small. Even abandoned projects provide valuable data about what doesn't work. The metrics system should be lightweight enough that recording data requires minimal effort—the easier it is to record, the more consistent the data will be.
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### 3.3 Interpreting Trends
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Raw metrics are less valuable than trends. The Research Fortress should monitor three categories of metric movement:
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**Declining metrics** (investigate causes):
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- Increasing project duration suggests process bottlenecks, unclear requirements, or scope creep
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- Rising handoff failure rates indicate protocol problems or role confusion
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- Decreasing quality scores reveal systematic issues in research, writing, or review
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- Higher iteration counts suggest unclear requirements or inadequate feedback
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**Improving metrics** (identify what's working):
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- Decreasing iterations suggest process efficiency gains or clearer prompts
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- Rising completion rates indicate reliability improvements
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- Improving quality scores demonstrate effective interventions
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- Shorter handoff times show better context preservation
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**Variance reduction** (consistent performance):
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- Lower variance in quality scores indicates reliable processes
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- Predictable project durations enable better planning and estimation
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- Consistent handoff success rates suggest stable protocols
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### 3.4 Threshold and Alert Design
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To make metrics actionable, we should establish thresholds that trigger investigation or intervention:
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| Metric | Green (Normal) | Yellow (Warning) | Red (Action Required) |
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|--------|---------------|------------------|----------------------|
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| Project duration | <60 min | 60-90 min | >90 min |
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| Handoff failure rate | <10% | 10-25% | >25% |
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| Citation accuracy | >95% | 90-95% | <90% |
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| Iterations | 1-2 | 3-4 | >4 |
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These thresholds should be reviewed quarterly and adjusted based on experience. A project that consistently triggers "red" may indicate the threshold is too strict; consistently "green" may suggest it's too lenient.
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---
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## 4. Continuous Improvement Mechanisms
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### 4.1 The Improvement Cycle
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Self-improvement requires a deliberate cycle: **Measure → Analyze → Adjust → Repeat**. We recommend implementing this as a weekly review process:
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**Weekly Review (automated)**:
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1. Aggregate metrics from the week's projects
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2. Compare to baseline and previous weeks
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3. Identify statistically significant changes
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4. Flag anomalies for human review
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**Monthly Analysis (human-involved)**:
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1. Review flagged anomalies
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2. Identify systemic issues
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3. Propose process changes
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4. Document changes in PLAYBOOK.md
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**Quarterly Strategy (deep review)**:
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1. Major trend analysis
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2. Architecture review
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3. New tool/approach evaluation
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4. Goal setting for next quarter
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### 4.2 Specific Improvement Interventions
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Based on metrics analysis, common interventions include:
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**Process refinements**:
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- Adjusting handoff protocols when failure rates rise
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- Modifying quality thresholds based on project types
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- Updating templates to address recurring issues
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**Tool improvements**:
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- Adding new search sources or research tools
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- Improving prompt templates based on common failures
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- Automating manual verification steps
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**Training (prompt) improvements**:
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- Refining agent prompts to address recurring errors
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- Adding new verification steps to prompts
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- Improving instructions for specific project types
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**Resource allocation**:
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- Adjusting time estimates based on actual durations
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- Allocating more time to complex project types
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- Identifying bottlenecks in the workflow
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---
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## 5. Feedback Loops Required for Learning
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### 5.1 The Four Critical Feedback Loops
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For the Research Fortress to improve, four feedback loops must operate at different timescales. Each loop serves a distinct function and requires different mechanisms:
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**1. Project-Level Feedback (within-project)**
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- Reviewer provides feedback to Writer
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- Writer revises based on feedback
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- Final quality assessment before completion
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- *Frequency: Per-project, multiple times*
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- *Purpose: Ensure current project quality*
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This is the most immediate feedback loop and the one currently most developed in the Research Fortress. The Reviewer agent identifies issues, the Writer addresses them, and the cycle repeats until quality thresholds are met. However, this loop focuses on the current project only—it does not capture systemic insights for future work.
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**2. Handoff Feedback (between roles)**
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- Receiving agent evaluates work from sending agent
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- Handoff quality is rated
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- Protocol adjustments based on failure patterns
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- *Frequency: Per-handoff*
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- *Purpose: Improve role transitions*
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The handoff protocol from Level 2A establishes the structure for information transfer, but feedback about handoff effectiveness completes the loop. When a Writer receives research that is unusable, or when a Reviewer receives a document that lacks required sections, this feedback should be captured and analyzed.
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**3. Cross-Project Feedback (between projects)**
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- Lessons from completed projects inform new projects
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- Metrics trends indicate systemic issues
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- Process changes propagate to new work
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- *Frequency: Weekly review*
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- *Purpose: Transfer learnings across projects*
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This is the loop most organizations fail to close. Insights from Project A must reach Project B, but without explicit mechanisms, each project starts fresh. The memory architecture described in Section 8 serves this function—project post-mortems, metrics analysis, and documentation updates all enable cross-project learning.
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**4. Meta-Learning Feedback (system-level)**
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- Analysis of what improvement interventions worked
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- Evaluation of the improvement process itself
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- Architecture changes based on learning
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- *Frequency: Monthly/quarterly*
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- *Purpose: Improve the improvement process*
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The most sophisticated loop examines not just projects but the improvement system itself. Are the metrics useful? Are the thresholds appropriate? Is the review process adding value? This meta-level analysis ensures the improvement mechanisms evolve alongside the research system.
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### 5.2 Feedback Loop Architecture
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Each feedback loop requires four components:
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- **Information capture**: What happened? (metrics, notes, issues)
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- **Analysis**: What does it mean? (trends, root causes)
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- **Action**: What should change? (process, tools, prompts)
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- **Propagation**: How do we ensure change happens? (documentation, automation)
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The critical insight is that feedback without action is useless, and action without feedback is guessing. Both are required. Many organizations collect extensive data but never act on it; others make changes without understanding their impact. Effective feedback loops require all four components.
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### 5.3 Implementing Feedback Capture
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To make feedback actionable, we need structured capture mechanisms:
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**Within-project feedback**: Use the handoff notes field in the metrics schema to record what worked and what didn't at each role transition. The Reviewer's final assessment should include not just quality scores but specific feedback for the Writer.
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**Cross-project feedback**: The post-mortem template (Section 6.2) captures project-level lessons. The weekly review analyzes these lessons and identifies patterns across projects.
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**System-level feedback**: The quarterly review evaluates whether the improvement mechanisms themselves are working. Are metrics being recorded consistently? Are reviews happening on schedule? Are changes actually implemented?
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---
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## 6. Preserving Learnings Across Sessions
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### 6.1 The Memory Problem
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AI agents do not remember across sessions. This is the fundamental challenge for research system improvement. We must explicitly architect memory.
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**What's Lost Without Architecture**:
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- What worked/didn't work in past projects
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- Common failure modes and how to avoid them
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- Project-specific context that would help future work
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- Institutional knowledge about research methods
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### 6.2 Learning Preservation Mechanisms
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We recommend three complementary mechanisms:
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**1. Project Post-Mortems**
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At project completion, a structured reflection captures:
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- What went well
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- What didn't go well
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- What we learned
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- What we'd do differently
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Template (add to TEMPLATES.md):
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```
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## Post-Mortem: [Project Name]
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### What Worked
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-
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### What Didn't Work
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-
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### Lessons Learned
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-
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### Next Time We Would
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-
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```
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**2. Recurring Issue Log**
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A persistent file tracking recurring problems:
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```
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research-fortress/
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memory/
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issues.md # Recurring problems
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solutions.md # What works
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patterns.md # Emerging patterns
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```
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**3. Quarterly Research Reviews**
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Every quarter, compile an analysis of:
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- Projects completed
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- Metrics trends
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- Major lessons
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- Recommendations for next quarter
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### 6.3 Documentation as Memory
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The key insight is that **documentation is memory**. The Research Fortress already has:
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- METHODOLOGY.md (core processes)
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- PLAYBOOK.md (procedures)
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- TEMPLATES.md (project templates)
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- PROJECTS.md (project history)
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These should be actively updated. When a lesson is learned, it should be:
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1. Recorded in memory files
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2. Integrated into relevant documentation
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3. Tested in future projects
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---
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## 7. Can Agents Learn from Past Projects?
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### 7.1 The Learning Question
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Can individual agents improve based on past project experience? The answer is nuanced:
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**What agents CAN do**:
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- Access documentation and memory files
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- Read past projects and post-mortems
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- Apply lessons from documented learnings
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- Use improved prompts and templates
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**What agents CANNOT do** (without architecture):
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- Automatically remember past projects
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- Learn from experience without explicit context
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- Improve their base capabilities without updates
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### 7.2 Agent Learning Implementation
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To enable agent learning, we must provide context explicitly:
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**At project start**:
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- Provide summary of relevant past projects
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- Share recurring issues to avoid
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- Highlight what worked in similar projects
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**During execution**:
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- Prompt agents to consult memory files
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- Encourage referencing of past approaches
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- Flag known issues in current work
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**At project end**:
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- Require documentation of what worked/didn't
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- Capture novel approaches for future use
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- Update prompts if new better approaches found
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### 7.3 The Reviewer as Learning Agent
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The Reviewer agent (from Level 1) has a unique role in learning:
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- Identifies patterns in quality issues
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- Can suggest process improvements
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- Evaluates whether improvements are working
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- Serves as the "institutional memory" within projects
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We recommend adding a post-project Reviewer summary that:
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- Summarizes quality issues found
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- Notes patterns across projects
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- Recommends process changes
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---
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## 8. Memory Architecture for Research Systems
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### 8.1 The Memory Problem in Detail
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The fundamental challenge is that AI agents lack persistent memory across sessions. When a human team completes a project, the team members remember what they learned. When a Research Fortress project completes, the agents cease to exist in their current form. Their "memories" exist only in the documentation they produced.
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This creates several specific problems:
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**Context Loss**: Details that seemed obvious during a project may not be captured in final documents. Why was a particular source chosen? What approaches were rejected and why? These implicit decisions vanish without explicit capture.
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**Pattern Blindness**: Without memory of past projects, agents cannot see patterns. The same mistake made in Project 1 and Project 5 is invisible unless someone explicitly compares them.
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**Redundant Effort**: Solutions discovered in Project 1 must be rediscovered in Project 5 unless documented and accessible. Each project repeats the learning curve.
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**Institutional Knowledge Loss**: The Research Fortress methodology itself represents accumulated learning. Without active maintenance, this knowledge degrades as documents become outdated or contradict each other.
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### 8.2 Proposed Architecture
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We recommend a three-tier memory architecture, each serving different purposes and requiring different maintenance:
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**Tier 1: Active Memory (current project)**
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- In-progress documents
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- Handoff buffers
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- Current project context
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- Working notes and scratch space
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- *Duration: Project length*
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- *Storage: Working files in project directory*
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- *Access: All agents on current project*
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This tier exists only during active projects. It includes everything agents need to do their work: the evolving document, research notes, handoff buffers from Level 2A, and any project-specific context. At project end, active memory is either archived (to Tier 2) or discarded.
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**Tier 2: Project Memory (recent projects)**
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- Completed project files
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- Post-mortems
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- Metrics data
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- Source archives
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- *Duration: 90 days active, then archive*
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- *Storage: research-fortress/projects/*
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- *Access: On request for relevant projects*
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Tier 2 preserves recent work for reference. When starting a new project on a similar topic, agents can review past projects for context. After 90 days, projects are archived (moved to cold storage or deleted) to prevent clutter, but key learnings should be extracted to Tier 3.
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**Tier 3: Institutional Memory (permanent)**
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- METHODOLOGY.md (core processes)
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- PLAYBOOK.md (procedures)
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- TEMPLATES.md (project templates)
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- memory/issues.md (recurring problems)
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- memory/solutions.md (what works)
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- memory/quarterly-reviews/ (periodic analyses)
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- memory/patterns.md (emerging patterns)
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- *Duration: Permanent*
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- *Storage: Core documentation files*
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- *Access: Always, for all projects*
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Tier 3 is the foundation of institutional knowledge. These documents should be consulted at the start of every project and updated whenever new learnings emerge. Unlike Tier 2, Tier 3 is actively maintained—outdated information is revised, not just archived.
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### 8.3 Memory Access Patterns
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Different contexts require different memory access:
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**New project start**: Access Tier 3 (institutional) + relevant Tier 2 examples. Agents should read the methodology, check for relevant issues to avoid, review similar past projects.
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**During project**: Access Tier 1 (active) + Tier 3 (issues to avoid). Agents reference current work and check for known pitfalls.
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**Weekly review**: Access Tier 2 (metrics) + Tier 3 (patterns). Analyze recent project data and update pattern recognition.
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**Quarterly review**: Access all tiers. Comprehensive analysis of system performance.
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### 8.4 Memory Maintenance
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Memory requires active maintenance to remain useful. Without maintenance, memory becomes noise—outdated, contradictory, and eventually ignored.
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**Weekly (lightweight)**:
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- File new post-mortems in Tier 2
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- Update issues.md with any new problems identified
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- Note any quick wins in solutions.md
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**Monthly**:
|
|
- Review Tier 2, archive projects older than 90 days
|
|
- Extract key learnings from archived projects to Tier 3
|
|
- Review and clean up solutions.md (remove superseded approaches)
|
|
|
|
**Quarterly**:
|
|
- Major review of Tier 3 documentation
|
|
- Update METHODOLOGY.md if processes have changed
|
|
- Create quarterly review document
|
|
- Set memory maintenance goals for next quarter
|
|
|
|
**As needed**:
|
|
- Fix broken links in documentation
|
|
- Update outdated procedures
|
|
- Resolve contradictions between documents
|
|
- Add new solution patterns as they emerge
|
|
|
|
### 8.5 Memory and Agent Context
|
|
|
|
How should agents actually access memory? We recommend explicit prompts at key moments:
|
|
|
|
**At project start** (add to standard project initialization):
|
|
```
|
|
Read the following memory files before beginning:
|
|
- METHODOLOGY.md
|
|
- memory/issues.md
|
|
- memory/solutions.md
|
|
- Recent projects in [relevant category]
|
|
```
|
|
|
|
**At project end** (add to completion checklist):
|
|
```
|
|
Complete the post-mortem template
|
|
Update memory/issues.md if new issues appeared
|
|
Update memory/solutions.md if new approaches worked
|
|
```
|
|
|
|
**During review** (Reviewer agent):
|
|
```
|
|
Consult memory/solutions.md for approaches that have worked
|
|
Check memory/issues.md for pitfalls to verify are avoided
|
|
```
|
|
|
|
---
|
|
|
|
## 9. Implementation Recommendations
|
|
|
|
### 9.1 Immediate Actions (This Week)
|
|
|
|
1. **Create metrics directory structure**:
|
|
```
|
|
research-fortress/metrics/projects/
|
|
research-fortress/metrics/aggregates/
|
|
research-fortress/memory/
|
|
```
|
|
|
|
2. **Add post-mortem template to TEMPLATES.md**
|
|
|
|
3. **Create initial memory files**:
|
|
- memory/issues.md
|
|
- memory/solutions.md
|
|
|
|
4. **Start recording metrics for current/future projects**
|
|
|
|
### 9.2 Short-Term (This Month)
|
|
|
|
1. **Implement weekly review process** (30 min/week)
|
|
- Review metrics from past week
|
|
- Identify any anomalies
|
|
- Document findings
|
|
|
|
2. **Add memory access to project workflow**
|
|
- At project start, load relevant memory
|
|
- At project end, write post-mortem
|
|
- Update issues/solutions as needed
|
|
|
|
3. **Train agents on memory usage**
|
|
- Update prompts to reference memory
|
|
- Add memory consultation to workflows
|
|
|
|
### 9.3 Medium-Term (This Quarter)
|
|
|
|
1. **Analyze first quarter's metrics**
|
|
- Establish baseline metrics
|
|
- Identify major trends
|
|
- Set improvement goals
|
|
|
|
2. **Evaluate and refine processes**
|
|
- Test process changes
|
|
- Update PLAYBOOK.md
|
|
- Adjust metrics if needed
|
|
|
|
3. **Quarterly review document**
|
|
- Compile comprehensive review
|
|
- Set goals for next quarter
|
|
- Update methodology if needed
|
|
|
|
### 9.4 Metrics to Track (Summary)
|
|
|
|
| Metric | Category | Target Trend |
|
|
|--------|----------|---------------|
|
|
| Project duration | Efficiency | Decreasing |
|
|
| Handoff failure rate | Reliability | Decreasing |
|
|
| Iterations per project | Efficiency | Decreasing |
|
|
| Source coverage | Quality | Increasing |
|
|
| Citation accuracy | Quality | Increasing (>95%) |
|
|
| Completion rate | Reliability | Increasing |
|
|
| Quality score variance | Consistency | Decreasing |
|
|
|
|
---
|
|
|
|
## 10. Conclusion
|
|
|
|
Self-improvement in multi-agent research systems is not automatic—it requires deliberate architectural choices. Unlike human organizations that naturally accumulate memory through personnel continuity, AI research systems must explicitly preserve learnings through documentation, metrics tracking, and structured improvement cycles.
|
|
|
|
This paper proposed a comprehensive approach:
|
|
|
|
1. **Metrics tracking**: Capture project-level and aggregate metrics, store longitudinally, analyze trends
|
|
2. **Continuous improvement**: Implement weekly/monthly/quarterly review cycles with defined interventions
|
|
3. **Feedback loops**: Ensure information flows from project completion to process improvement
|
|
4. **Cross-session preservation**: Use documentation as memory, update institutional knowledge actively
|
|
5. **Agent learning**: Provide explicit context from past projects, enable agents to reference memory
|
|
6. **Memory architecture**: Three-tier system (active/project/institutional) with defined access patterns
|
|
|
|
The Research Fortress can improve over time—but only if we build the systems that enable learning. The recommendations here provide a foundation: start tracking metrics, preserve learnings, and iterate. The system that results will be better than the one that started, and the one after that will be better still.
|
|
|
|
---
|
|
|
|
## Appendix A: Metrics Data Schema
|
|
|
|
```json
|
|
{
|
|
"version": "1.0",
|
|
"project": {
|
|
"id": "string",
|
|
"name": "string",
|
|
"start_time": "ISO8601",
|
|
"end_time": "ISO8601",
|
|
"duration_minutes": "number",
|
|
"status": "completed|abandoned|in-progress"
|
|
},
|
|
"workflow": {
|
|
"agents_used": ["string"],
|
|
"handoffs": [
|
|
{
|
|
"from": "string",
|
|
"to": "string",
|
|
"timestamp": "ISO8601",
|
|
"success": "boolean",
|
|
"issues": ["string"]
|
|
}
|
|
],
|
|
"iterations": "number"
|
|
},
|
|
"quality": {
|
|
"source_coverage": "number (0-1)",
|
|
"citation_accuracy": "number (0-1)",
|
|
"coherence": "number (0-1)",
|
|
"claim_sourcing": "number (0-1)",
|
|
"overall": "number (0-1)"
|
|
},
|
|
"lessons": {
|
|
"what_worked": ["string"],
|
|
"what_didnt": ["string"],
|
|
"recommendations": ["string"]
|
|
}
|
|
}
|
|
```
|
|
|
|
---
|
|
|
|
## Appendix B: Memory File Template
|
|
|
|
### memory/issues.md
|
|
```markdown
|
|
# Recurring Issues
|
|
|
|
## High Frequency
|
|
- [Issue description]
|
|
- [Frequency: X projects]
|
|
- [Impact: high/medium/low]
|
|
|
|
## Medium Frequency
|
|
- ...
|
|
|
|
## Resolved
|
|
- [Previously problematic issue]
|
|
- [Resolution: ...]
|
|
```
|
|
|
|
### memory/solutions.md
|
|
```markdown
|
|
# What Works
|
|
|
|
## Research Phase
|
|
- [Approach]: [Why it works]
|
|
|
|
## Writing Phase
|
|
- [Approach]: [Why it works]
|
|
|
|
## Review Phase
|
|
- [Approach]: [Why it works]
|
|
```
|
|
|
|
---
|
|
|
|
*This paper contributes to the Research Fortress methodology. Level 5 will explore unsolved problems at the frontier of AI research.*
|