Our research team investigates AI-powered recruiting, talent acquisition, agent systems, and the future of work, from domain-adapted retrieval models to lifecycle reviews of the field.
A structural look at why production agent systems drift across the prompt, architecture, evaluation, and context layers, even when the spec and business goal stay fixed.
A recruitment-domain semantic reranking system that uses LLM-synthesized supervision and boundary-aware reranking to improve candidate retrieval recall.
A lifecycle-oriented review of AI recruiting systems, covering semantic matching, generative AI, multimodal assessment, bias, explainability, and human oversight.