activeCycle 2026.1
Deterministic Multi-Agent Consensus for Automated Pull Request Triage
Formulating a deterministic voting protocol across specialized reviewer agents to identify logic errors and test gaps in Python code with state guardrails.
POD 03
Agentic AI & Autonomous Loops
Formulating formal state-machine specifications, deterministic consensus protocols, and verifiable guardrails for autonomous agent systems. We focus on eliminating non-deterministic failure modes and unconstrained recursive tool loops in multi-agent software engineering pipelines.
Core Research Questions
- Can deterministic state transitions guarantee termination in arbitrary multi-agent tool execution graphs?
- What attribution scoring methods best measure hallucination rate reductions in source-grounded RAG architectures?
- How do distributed consensus voting protocols across heterogeneous local LLMs compare to centralized monolithic prompting?
Scientific Methodology
State-machine graph modeling with LangGraph
Sentence-level citation and graph attribution validation
Schema-enforced JSON schema and Pydantic validation boundaries
Ablation testing on synthetic repository pull requests
validatingCycle 2026.1
Grounded RAG Pipeline with Semantic Source Verification
Evaluating hallucination reduction rates using sentence-level attribution graphs on university academic regulations and syllabus documentation.
technical report2026-03-10
AIAC-TR-2026-02: Deterministic State Verification in Multi-Agent Code Review Graphs
Formal state-machine specifications and guardrail schemas for preventing infinite recursive tool-calls in LangGraph multi-agent triage workflows. Demonstrates 100% loop termination across 150 automated test pull request submissions with verifiable Pydantic schema validation.