Research Paper

Core Innovation

This paper introduces the Hierarchical Long-Term Semantic Memory (HLTM) framework that structures textual data into a schema-aligned memory tree capturing multi-level semantic knowledge. It advances prior work by enabling scalable ingestion, privacy-aware storage, low-latency retrieval, and cross-domain adaptability for LLM agents in production environments.

Why It Matters

Hiring platforms require personalized, context-aware AI to handle complex, noisy user data efficiently. HLTM addresses scalability, privacy, and latency challenges, enabling faster and more accurate candidate recommendations. This transforms recruitment workflows by enhancing decision quality and operational efficiency at scale.

Potential Customers & Pain Points

• Recruitment platforms – Need accurate fast candidate matching
• HR departments – Require privacy-compliant personalized hiring tools
• Enterprise AI teams – Demand scalable low-latency memory systems for LLM agents

Business Model

Enterprise SaaS licensing targeting recruitment platforms and HR departments, with tiered pricing based on data volume and query throughput.

Competitive Landscape

• Eightfold AI
• HireVue
• Pymetrics
• SeekOut

Implementation Challenges

• Integrating HLTM with diverse enterprise data systems
• Ensuring compliance with evolving privacy regulations
• Maintaining low-latency retrieval at large scale

Validation Strategy

• Pilot deployment with LinkedIn Hiring Assistant to measure accuracy and latency improvements
• Customer feedback collection from HR teams using personalized hiring workflows
• Benchmarking against existing semantic memory and retrieval systems in recruitment