WIP
Research
Belief-Dynamics
Research on false consensus attractors in multi-agent LLM debate systems
// DESCRIPTION
Investigating how groups of LLM agents can converge on incorrect beliefs through debate dynamics.
Research Questions
- How do LLM agents reach consensus in debates?
- What factors lead to false consensus (hallucination amplification)?
- Can we design debate protocols that avoid these pitfalls?
Experiments
- E1: Naive debate baseline (49% hallucination rate)
- E2: Structured argumentation
- E3: Devil's advocate injection
- E4: Confidence-weighted voting
- E5: Multi-round refinement (running)
Preliminary Findings
Naive debate shows 49% hallucination rate, indicating significant false consensus risk in multi-agent systems.
// HIGHLIGHTS
- Novel research direction in multi-agent AI
- E1-E4 complete, E5 running
- ICML 2026 target