05Agentic AI · RLHF

Autonomous Multi-Agent Research Platform

A Planner–Retriever–Reasoner–Validator agent loop with persistent memory and tool use across PubMed, ArXiv, and Semantic Scholar APIs.

+41%
synthesis quality
−68%
hallucinated citations
5K+
concurrent sessions
<200ms
median latency

In short

  • PPO reward model trained on 12K researcher preference pairs — +41% synthesis quality and −68% hallucinated citations vs base GPT-4o on SciEval.
  • Deployed on GKE with LangSmith, Prometheus, and Grafana observability.
01

Architecture

A Planner–Retriever–Reasoner–Validator loop with persistent memory and tool use across the PubMed, ArXiv, and Semantic Scholar APIs. The validator stage exists because retrieval-grounded synthesis still invents citations when nothing checks it.

02

Alignment

A PPO reward model trained on 12K researcher preference pairs. Against base GPT-4o on SciEval that produced +41% synthesis quality and −68% hallucinated citations.

03

Operations

  • Deployed on GKE, serving 5K+ concurrent sessions at sub-200ms median latency.
  • Observability through LangSmith for agent traces plus Prometheus and Grafana for system metrics.