feat(scoring): Integrate Subnet 19 LLM for Enhanced Tweet Quality Assessment

[teslashibe]

Problem Statement

Current scoring relies on basic engagement metrics that are easily manipulated. We need deeper semantic analysis of tweet content and engagement patterns to identify authentic, valuable content.

Proposed Solution

Leverage Subnet 19's LLM capabilities to create a sophisticated content quality scorer that analyzes:

1. Content Analysis

• Semantic coherence and readability
• Topic relevance and consistency
• Originality vs repetitive patterns
• Writing quality and sophistication
• Information density and value

2. Engagement Analysis

• Conversational depth and quality
• Contextual relevance of replies
• Thread narrative coherence
• Citation and reference quality
• Community value assessment

Technical Implementation

LLM Integration

# Example scoring prompt template
QUALITY_ASSESSMENT_PROMPT = """
Analyze the following tweet content and engagement:

Tweet: {tweet_text}
Thread Context: {thread_context}
Engagement: {engagement_data}

Score the following dimensions (0-10):
1. Content Quality:
- Coherence and clarity
- Information value
- Originality
- Writing quality

2. Engagement Quality:
- Conversation depth
- Response relevance
- Community value

Provide numeric scores and brief justification.
"""

Scoring Pipeline

1. Batch tweets for assessment
2. Send to Subnet 19 LLM endpoint
3. Parse quality scores
4. Weight and integrate with existing metrics

Example Quality Metrics

class TweetQualityMetrics:
    def __init__(self):
        self.coherence_score = 0.0
        self.information_value = 0.0
        self.originality = 0.0
        self.writing_quality = 0.0
        self.conversation_depth = 0.0
        self.response_relevance = 0.0
        self.community_value = 0.0

    def get_weighted_score(self):
        weights = {
            'coherence': 0.15,
            'info_value': 0.20,
            'originality': 0.15,
            'writing': 0.15,
            'conversation': 0.15,
            'relevance': 0.10,
            'community': 0.10
        }
        # Calculate weighted score

Integration Points

1. Register quality scorer with Subnet 19's LLM endpoints
2. Add quality metrics to post_scorer.py
3. Update scoring weights to include LLM assessment
4. Implement caching for LLM scoring results

Anti-Gaming Measures

• Maintain scoring complexity opacity
• Randomize assessment timing
• Cross-reference historical patterns
• Detect coordinated content patterns

Success Metrics

• Improved correlation between scores and human-judged quality
• Reduced gaming indicators
• More diverse, higher-quality content
• Stronger community engagement patterns

Timeline

• TBD

Would you like me to expand on any particular aspect of this feature ticket or create a discord announcement for sharing with the community?