Key Algorithms & Techniques

This document outlines the core algorithmic foundations of the Aevum Encyclopedia platform. Our architecture combines advanced large language models, dense vector retrieval, and symbolic reasoning to deliver sub-second access to over 2.4 million verified articles across 140+ languages.

1. Hybrid Semantic Search

Aevum employs a hybrid search architecture that combines lexical matching (BM25) with dense vector retrieval. This dual-approach ensures high recall for long-tail queries while maintaining precision through semantic understanding.

1.1 Embedding Pipeline

All articles are processed through our proprietary Aevum-BERT-4 model, producing 1,536-dimensional embeddings. These vectors capture semantic relationships, contextual nuance, and cross-lingual alignment.

function search(query: String, filters: Object) {
    // 1. Dense Vector Retrieval (ANN)
    dense_results = faiss_search(
        index="aevum_bert_1536",
        query=encode(query),
        k=100
    )

    // 2. Lexical Matching (BM25)
    lexical_results = bm25_search(
        index="inverted_index",
        query=normalize(query),
        k=50
    )

    // 3. Reciprocal Rank Fusion
    return rrf_merge(dense_results, lexical_results, k=60)
}

2. Knowledge Graph Construction

The Aevum Knowledge Graph (AevumGraph) represents entities, concepts, and relationships across all disciplines. Our graph contains over 850 million nodes and 2.4 billion edges, enabling complex reasoning and "serendipitous discovery" features.

2.1 Entity Linking & Disambiguation

We use a multi-stage neural entity linker that resolves mentions to graph nodes. The system handles polysemy and coreference using a contextual attention mechanism trained on 500B tokens of structured Wikipedia-derived data.

2.2 Temporal Reasoning

AevumGraph supports temporal edges, allowing queries like "Show me the evolution of quantum error correction from 2010 to 2025." Our temporal index uses a compressed segment tree structure to efficiently store time-bound facts.

3. AI-Powered Verification

Trust is paramount. Every assertion in Aevum is backed by a verification score generated by our Multi-Agent Debate System (MADS).

3.1 MADS Architecture

When new content is submitted, three specialized AI agents debate the claims:

• Claim Extractor: Identifies discrete, verifiable statements.
• Evidence Retriever: Fetches primary sources from our curated corpus.
• Judge Model: Evaluates the alignment between claims and evidence.

Content only passes if the Judge Model assigns a confidence score > 0.92. Ambiguous claims are routed to human moderators for final review.

async def verify_content(article: Article) -> VerificationResult:
    claims = await extract_claims(article.text)
    
    evidence_map = {}
    for claim in claims:
        sources = await retrieve_sources(claim, k=3)
        evidence_map[claim.id] = judge_alignment(claim, sources)
    
    avg_score = mean([e.score for e in evidence_map.values()])
    
    if avg_score > 0.92:
        return VerificationResult.status="VERIFIED"
    elif avg_score > 0.75:
        return VerificationResult.status="HUMAN_REVIEW"
    else:
        return VerificationResult.status="REJECTED"

4. Multilingual Processing

Aevum supports 140+ languages using a combination of cross-lingual embeddings and lightweight adapters for low-resource languages.

4.1 Zero-Shot Translation

Our embedding space is aligned such that semantically similar content clusters together regardless of language. This enables zero-shot translation for languages without parallel training data, leveraging the geometric structure of the latent space.

4.2 Resource-Efficient Adapters

For low-resource languages, we deploy LoRA adapters fine-tuned on synthetic data generated by our high-capacity base model. This reduces parameter count by 95% while maintaining 92% of base model accuracy.

5. Performance & Scale

The platform handles over 50 million queries per day with strict latency SLAs. Key optimization techniques include:

• Sharded Vector Index: FAISS indexes are partitioned across 400 nodes with consistent hashing.
• Graph Caching: Hot subgraphs are materialized in Redis with automatic eviction based on access frequency.
• Edge Computing: Personalization and filtering logic runs on Cloudflare Workers for global low-latency.
• Query Optimization: Adaptive index selection based on query type and historical performance metrics.