# Web Research Integration: ReAct Pattern

## Research Source

**URL**: https://www.promptingguide.ai/techniques/react
**Topic**: ReAct (Reasoning and Acting) pattern for multi-agent systems
**Date Researched**: 2025-10-10

## Key Concepts Extracted

### 1. Interleaved Reasoning and Acting

**From Source**:
> ReAct generates "reasoning traces" and "task-specific actions" in an interconnected manner, allowing LLMs to "induce, track, and update action plans" while enabling interaction with external information sources.

**Applied In This Variant**:
- Every quality evaluation begins with explicit reasoning (THOUGHT phase)
- Actions (evaluations, rankings) are informed by prior reasoning
- Observations from actions feed back into next reasoning cycle
- Quality assessment and iteration generation are interleaved, not sequential

**Evidence in Implementation**:
- `.claude/commands/infinite-quality.md`: Structured THOUGHT → ACTION → OBSERVATION phases
- `.claude/commands/evaluate.md`: "THOUGHT Phase: Reasoning About Evaluation" before scoring
- `.claude/commands/rank.md`: "THOUGHT Phase: Reasoning About Ranking" before analysis
- All commands document reasoning before executing actions

### 2. Thought-Action-Observation Loop

**From Source**:
> The core loop cycles: Thought (generates reasoning strategy) → Action (interfaces with tools) → Observation (captures results) → [repeat]

**Applied In This Variant**:

**THOUGHT Phase**:
- Analyze specification quality criteria
- Reason about evaluation strategy
- Plan quality-driven creative directions
- Consider what constitutes quality in this context

**ACTION Phase**:
- Execute evaluations using defined criteria
- Generate iterations with quality targets
- Score across multiple dimensions
- Rank and segment iterations

**OBSERVATION Phase**:
- Analyze evaluation results
- Identify quality patterns and trade-offs
- Extract actionable insights
- Inform next wave strategy

**Evidence in Implementation**:
- `README.md`: Complete workflow section documenting T-A-O cycles
- `CLAUDE.md`: "ReAct Pattern Integration" section with cycle details
- `evaluators/`: Each evaluator has THOUGHT, ACTION, OBSERVATION phases
- Infinite mode: Each wave uses observations from previous wave to inform next reasoning

### 3. Reducing Hallucination Through External Grounding

**From Source**:
> ReAct reduces fact hallucination by grounding in external information and supports switching between reasoning approaches.

**Applied In This Variant**:
- Evaluations grounded in concrete evidence from code
- Every score requires specific examples (lines of code, features, patterns)
- Quality standards externalized in `specs/quality_standards.md`
- Evaluation criteria in separate `evaluators/` files (external knowledge)
- Reasoning must cite specific evidence, not make unsupported claims

**Evidence in Implementation**:
- `evaluators/technical_quality.md`: "Evidence to look for" sections with concrete examples
- `evaluators/creativity_score.md`: Requires specific creative elements as evidence
- `evaluators/spec_compliance.md`: Checklist-based approach with binary evidence
- All evaluation outputs include "evidence" field with specific line numbers and examples

### 4. Adaptive and Contextual Problem-Solving

**From Source**:
> Creates a "synergy between 'acting' and 'reasoning'" that allows more adaptive and contextually informed problem-solving.

**Applied In This Variant**:
- Quality evaluation adapts based on spec context
- Infinite mode strategy evolves based on observations
- Evaluation criteria can be customized (scoring weights)
- System learns what quality means from top performers

**Evidence in Implementation**:
- `config/scoring_weights.json`: Configurable weights for different contexts
- Alternative profiles (technical-focus, creative-focus, etc.) adapt to needs
- Infinite mode adapts strategy based on wave observations
- Quality reports include "Recommendations for Next Wave" informed by current results

### 5. Few-Shot Exemplars and Reasoning Trajectories

**From Source**:
> Use few-shot exemplars demonstrating reasoning trajectories and design flexible prompts adaptable to different task types.

**Applied In This Variant**:
- `specs/example_spec.md`: Provides example quality criteria and success patterns
- `templates/quality_report.md`: Template showing reasoning structure
- `evaluators/`: Each includes calibration examples showing reasoning → score
- `README.md`: Multiple scoring examples with reasoning demonstrated

**Evidence in Implementation**:
- Calibration examples in each evaluator showing reasoning process
- Report template shows how to reason about patterns
- Example spec demonstrates how to think about quality
- Documentation includes "Success Examples" and "Example Use Cases"

## ReAct Pattern Implementation Summary

### Core Pattern: THOUGHT → ACTION → OBSERVATION

This variant embeds ReAct at three levels:

**1. Command Level** (`.claude/commands/*.md`):
- Each command has explicit THOUGHT, ACTION, OBSERVATION phases
- Reasoning precedes execution
- Results inform next actions

**2. Wave Level** (Infinite mode):
- Wave N observations inform Wave N+1 thoughts
- Strategy adapts based on quality trends
- Continuous improvement through feedback loops

**3. Evaluation Level** (Individual assessments):
- Pre-evaluation reasoning about criteria
- Systematic application of standards
- Post-evaluation analysis and reflection

### Synergy Between Reasoning and Acting

**Traditional Approach** (Without ReAct):
```
Generate iterations → Evaluate → Report
(Linear, no reasoning, no adaptation)
```

**ReAct-Enhanced Approach** (This Variant):
```
THOUGHT: Reason about quality goals and strategy
  ↓
ACTION: Generate with quality targets
  ↓
OBSERVATION: Evaluate and analyze patterns
  ↓
THOUGHT: Learn from observations, adapt strategy
  ↓
ACTION: Generate next wave with refinements
  ↓
[Continuous loop...]
```

## Specific Implementations Inspired by ReAct

### 1. Explicit Reasoning Documentation

**ReAct Principle**: Make reasoning visible and trackable

**Implementation**:
- All evaluations include "reasoning" field
- Quality reports have "Strategic Insights" section with reasoning
- Rankings explain why certain iterations rank higher
- Every score is justified with evidence

**Files**:
- All command files in `.claude/commands/`
- All evaluator files in `evaluators/`
- Template in `templates/quality_report.md`

### 2. Iterative Strategy Refinement

**ReAct Principle**: Update action plans based on observations

**Implementation**:
- Infinite mode uses wave observations to plan next wave
- Quality gaps identified in rankings inform creative directions
- Success factors from top performers guide strategy
- Recommendations section provides actionable next steps

**Files**:
- `.claude/commands/infinite-quality.md`: Phase 4 "Reasoning About Results"
- `.claude/commands/rank.md`: "Recommendations for Next Wave" section
- `.claude/commands/quality-report.md`: "Strategic Recommendations" phase

### 3. Multi-Path Reasoning

**ReAct Principle**: Support switching between reasoning approaches

**Implementation**:
- Three parallel evaluation dimensions (technical, creative, compliance)
- Each dimension has different reasoning approach
- Trade-off analysis recognizes competing quality criteria
- Alternative scoring profiles for different contexts

**Files**:
- `evaluators/technical_quality.md`: Evidence-based technical reasoning
- `evaluators/creativity_score.md`: Aesthetic and innovation reasoning
- `evaluators/spec_compliance.md`: Checklist-based compliance reasoning
- `config/scoring_weights.json`: Multiple reasoning profiles

### 4. External Knowledge Grounding

**ReAct Principle**: Ground reasoning in external information

**Implementation**:
- Evaluation criteria externalized in separate files
- Quality standards documented and referenceable
- Specific code examples required for all scores
- Spec compliance checked against external specification

**Files**:
- `specs/quality_standards.md`: External quality knowledge base
- `evaluators/*.md`: Formalized evaluation knowledge
- All evaluations require evidence from actual iteration code

### 5. Observable Feedback Loops

**ReAct Principle**: Observation captures results to inform reasoning

**Implementation**:
- Every evaluation produces structured observations (JSON)
- Rankings aggregate observations across iterations
- Quality reports synthesize observations into insights
- Insights feed back into next wave planning

**Files**:
- Output structure: `quality_reports/evaluations/*.json`
- Output structure: `quality_reports/rankings/*.md`
- Output structure: `quality_reports/reports/*.md`

## Comparison: Before vs After ReAct Integration

### Without ReAct (Hypothetical Basic Variant)

```
1. Generate 10 iterations
2. Score each iteration (no reasoning shown)
3. Rank by score
4. Report: "Top iteration: X with score Y"
```

**Problems**:
- No reasoning transparency
- No adaptation between iterations
- No learning from results
- Opaque scoring process

### With ReAct (This Variant)

```
1. THOUGHT: Analyze spec, reason about quality criteria
2. ACTION: Generate iterations with quality targets
3. OBSERVATION: Evaluate with documented reasoning
   - Technical reasoning: "Code is clean because..."
   - Creative reasoning: "This is original because..."
   - Compliance reasoning: "Requirements met: ✓ X, ✓ Y, ✗ Z"
4. THOUGHT: Analyze patterns in results
   - "Top iterations succeed because of pattern P"
   - "Low scores caused by factor F"
5. ACTION: Generate next wave incorporating lessons
6. [Loop continues with adaptive improvement]
```

**Benefits**:
- Complete reasoning transparency
- Adaptive strategy improvement
- Learning from observations
- Evidence-based scoring

## Key Innovation: ReAct for Quality Assessment

The primary innovation of this variant is applying ReAct to **quality evaluation**, not just generation:

**Traditional AI Evaluation**:
- "This iteration scores 75/100"
- No reasoning shown
- Opaque process

**ReAct-Enhanced Evaluation**:
```
THOUGHT: What makes code quality excellent?
- Clean structure, good comments, DRY principle...

ACTION: Examine iteration code
- Line 45-67: Excellent validation with clear errors [Evidence]
- Line 120-135: Some code duplication [Evidence]

OBSERVATION: Score 20/25 on code quality
Reasoning: Strong fundamentals with minor DRY violations
Evidence: Specific line examples provided above

Impact on Strategy: Extract validation pattern from this iteration,
apply to future iterations while addressing duplication
```

This makes quality assessment:
- **Transparent**: Reasoning is documented
- **Fair**: Consistent criteria applied
- **Actionable**: Insights drive improvement
- **Adaptive**: Learns and evolves

## Validation: Does This Implementation Follow ReAct?

**Checklist from Source**:

✅ **Interleaved reasoning and acting**: Yes - THOUGHT and ACTION phases alternate
✅ **Thought-Action-Observation loop**: Yes - All commands follow this structure
✅ **Induces and updates action plans**: Yes - Strategy adapts based on observations
✅ **Grounds in external information**: Yes - Evaluations cite specific evidence
✅ **Reduces hallucination**: Yes - Every claim requires concrete evidence
✅ **Supports switching reasoning approaches**: Yes - Multiple evaluation dimensions
✅ **Few-shot exemplars**: Yes - Examples and calibration throughout
✅ **Improves interpretability**: Yes - All reasoning documented

**Conclusion**: This variant successfully implements the ReAct pattern for quality evaluation and continuous improvement.

## Learning Applied vs Learning Demonstrated

**What We Learned from URL**:
1. ReAct interleaves reasoning and acting
2. T-A-O loop structure
3. External grounding reduces hallucination
4. Adaptive, contextual problem-solving
5. Few-shot reasoning trajectories

**How We Applied It**:
1. ✅ Every command has THOUGHT-ACTION-OBSERVATION phases
2. ✅ Infinite mode implements continuous T-A-O loops across waves
3. ✅ All evaluations require specific code evidence, no unsupported claims
4. ✅ Strategy adapts based on wave observations, scoring configurable by context
5. ✅ Examples and calibration throughout documentation

**Evidence of Application**:
- Structure of all command files
- Evaluator reasoning requirements
- Infinite mode adaptive strategy
- Quality report insights feeding next wave
- Evidence-based scoring throughout

---

**Conclusion**: This variant successfully integrates ReAct pattern principles to create a quality evaluation system that reasons explicitly, acts systematically, observes carefully, and adapts continuously. The web research directly informed the architecture and implementation of all major components.