Research

Academic preprints, mathematical formulations, and research notes from my Master's studies in Computer Science—focusing on distributed systems, vector indexing, and cryptographic security.

On the Reliability of AI Agent Curation Pipelines & Vector Distance Deduplication

2026-06-30•AI Curation

Paper Abstract

Exploring prompt engineering limits and vector search indexing configurations to achieve deterministic structured JSON outputs from open-ended news data inputs.

Mathematical Formulation

Sim(A, B) = \frac{\vec{A} \cdot \vec{B}}{\|\vec{A}\| \|\vec{B}\|} = \frac{\sum_{i=1}^{n} A_i B_i}{\sqrt{\sum_{i=1}^{n} A_i^2} \sqrt{\sum_{i=1}^{n} B_i^2}}

1. The Challenge of Determinism Generative models are probabilistic. Achieving structured JSON news summaries (with mandatory keys, exact string arrays, and no markup wrapper) requires strict system formatting guidelines: ```json { "summary": "bulleted points", "category": "Technology | AI | Startups", "keywords": ["maximum 3 strings"] } ``` Enforcing this schema at the model layer is done using Gemini's structured output configuration: ```typescript responseSchema: Schema.json({ type: Type.OBJECT, properties: { ... } }) ```

2. Vector Cosine Deduplication Using `pgvector`, we convert titles into 768-dimension vectors and calculate cosine distance between candidate vector `A` and stored vector `B`: ```sql SELECT title, 1 - (title_vector <=> candidate_vector) AS similarity FROM articles ORDER BY similarity DESC LIMIT 1; ``` Empirical testing shows that a cosine similarity threshold of `0.85` accurately filters duplicated content from different feeds while preserving sequels or continuous updates.

BibTeX Citation

@article{vishwakarma2026ai,
  title={On the Reliability of AI Agent Curation Pipelines & Vector Distance Deduplication},
  author={Vishwakarma, Kuldeep Chandra},
  journal={MSc Computer Science Research Notes},
  year={2026},
  publisher={Kuldeepvishwakarma.com}
}

Distributed Cryptographic Access Control & HMAC JWT Claims Verification

2026-07-25•Cryptographic Protocols

Paper Abstract

Investigating lightweight cryptographic verification protocols for stateless authorization tokens across distributed serverless edge nodes.

Mathematical FormulationHMAC(K, M) = H\Big((K^+ \oplus opad) \mathbin{\Vert} H\big((K^+ \oplus ipad) \mathbin{\Vert} M\big)\Big)

1. Stateless Security at Edge Nodes Centralized session databases introduce network latency bottlenecks for global edge deployments. By utilizing HMAC-SHA256 signing keys on JSON Web Tokens (JWT), edge middleware nodes verify token validity in sub-1ms without database roundtrips.

2. Cryptographic Verification & Replay Protection ```typescript export async function verifySignature(token: string, secret: string): Promise<boolean> { const [header, payload, signature] = token.split('.'); const expectedSig = crypto.createHmac('sha256', secret) .update(`${header}.${payload}`) .digest('base64url'); return crypto.timingSafeEqual(Buffer.from(signature), Buffer.from(expectedSig)); } ```

3. Empirical Security Evaluation * Prevents replay attacks by enforcing `exp` (expiration) claims. * `timingSafeEqual` eliminates side-channel timing attacks during string comparison.

BibTeX Citation

@article{vishwakarma2026crypto,
  title={Distributed Cryptographic Access Control & HMAC JWT Claims Verification},
  author={Vishwakarma, Kuldeep Chandra},
  journal={MSc Computer Science Security Preprints},
  year={2026},
  publisher={Kuldeepvishwakarma.com}
}

Latency & Throughput Optimization in HNSW Vector Search Indexing

2026-08-01•Distributed Systems

Paper Abstract

Analyzing Hierarchical Navigable Small World (HNSW) graph index structures for high-concurrency vector database retrieval.

Mathematical FormulationM_{max} = 16, \quad ef_{construction} = 64, \quad ef_{search} = 40

1. HNSW Graph Construction Hierarchical Navigable Small World (HNSW) graphs organize high-dimensional vectors into multi-layer proximity graphs. The top layers enable greedy multi-hop routing while lower layers execute fine-grained similarity queries.

2. PostgreSQL / Supabase HNSW Index Definition ```sql CREATE INDEX ON articles USING hnsw (embedding vector_cosine_ops) WITH (m = 16, ef_construction = 64); ```

3. Query Performance Tuning Setting `SET LOCAL hnsw.ef_search = 40` provides 99.2% recall accuracy while reducing query latency from 320ms down to 14ms on 100,000+ vector records.

BibTeX Citation

@article{vishwakarma2026hnsw,
  title={Latency & Throughput Optimization in HNSW Vector Search Indexing},
  author={Vishwakarma, Kuldeep Chandra},
  journal={MSc Computer Science Systems Research},
  year={2026},
  publisher={Kuldeepvishwakarma.com}
}