consensus

A user shares their experience using ChatGPT for complex medical caregiving and proposes the idea of aggregating multiple AI models to improve reliability by seeking consensus among different LLMs.

This article argues that AI creates a fast feedback loop where humans and machines mutually shape truth, accelerating consensus shifts and making truth increasingly synthetic and detached from reality.

This paper presents a distributed approach for constrained multi-agent reinforcement learning that uses state-augmented policy learning and neighbor-to-neighbor consensus over dual variables to satisfy global resource constraints while scaling linearly with the number of agents. Experiments on smart grid demand response demonstrate that consensus coordination is essential for feasibility, scaling to thousands of agents unlike centralized training approaches.

PolyGnosis is an adversarial multi-model consensus system built as a Hermes skill. It runs three AI models in parallel with different expert personas, then has a hostile critic phase, scoring via RRF and Borda Count, and a synthesis gate—all built agentically using DeepSeek V4-Pro.

This paper reveals that aggregating complete reasoning traces from multiple LLM agents, rather than just their final answers, can correct errors even when agents unanimously agree, introducing the 'aggregation paradox' and the Self-Consistent Mixture of Agents method.

This paper identifies that language model reasoning trajectories during test-time sampling cluster into 'reasoning basins', causing majority vote failures when the dominant basin is incorrect. It introduces ARBITER, a model-agnostic method that uses conservative additive evidence from the model's own outputs and hidden states to improve accuracy without external data.

An exploration of how using multiple AI models for agent workflows reveals hidden uncertainties and reasoning gaps, suggesting that future systems may rely on cross-model consensus rather than single-model chains.

Empirical study shows small language models achieve 100% adversarial robustness with System 1 intuition but collapse under System 2 reasoning when used as edge-native governance firewalls in decentralized autonomous organizations.

The article explains consensus algorithms (like Paxos) through a board game metaphor, using visual diagrams to illustrate voting, leader election, and fault tolerance in distributed systems.