consensus

The author built a control room over 47 days to demonstrate that blockchain's issue is visibility rather than trust, providing tools to observe consensus activity and network topology.

This paper introduces Reasoning Jury, a system that uses a jury of open-weight LLMs with a moderated consensus mechanism to evaluate long reasoning traces, significantly outperforming frontier models at identifying reasoning defects while costing a fraction of the price.

This paper introduces ConRub-Med, a reinforcement learning approach that uses consensus rubrics from multiple language models to reward open-ended medical question answering, achieving state-of-the-art results on several benchmarks including HealthBench-Hard.

An in-depth technical article explaining the ABD algorithm, quorum replication, and why ABD does not solve consensus, with runnable Python examples.

This paper studies whether cheap open-weight LLMs can judge natural-language mathematical proofs as reliably as frontier models at far lower cost. On IMO-GradingBench, three cheap judges match frontier pass/fail agreement, and the authors recommend an all-three-pass consensus rule for cost-effective deployment.

This paper presents a dependency-aware autoscaling framework for serverless environments, integrating graph-based bottleneck identification, multi-model forecasting (MLP, LSTM, CNN) via a probabilistic ensemble, and cost-aware scaling control. Experiments show 99.88% prediction accuracy and reduced infrastructure costs.

A research paper introducing Canon, a label-free self-distillation method that uses consensus among sampled solutions to provide dense token-level supervision for training large language models on reasoning tasks, improving pass@1 by up to 12 points and outperforming label-free reinforcement learning at a fraction of the compute.

The author critiques Demis Hassabis' latest essay, arguing it reads like corporate strategy and abandons his earlier vision for global AI governance, while noting broad consensus among AI CEOs and raising questions about geopolitical framing and blind spots.

This paper investigates how the runtime interaction graph affects convention formation in populations of open-weight language models (1.1B–32B parameters) using a naming game protocol, finding that homophilous routing amplifies fragmentation while bridge-seeking routing can repair consensus under memory conditions.

This paper investigates the optimal placement of expensive 'oracle' correctors within a swarm of unreliable agents to achieve correct consensus, revealing a submodular property and a budget-correctness frontier that depends on the cost–strength curvature.

The paper proposes Epistemic State Replication (ESR), a belief-replication layer for agentic distributed systems that shifts replication from data visibility to knowledge visibility, allowing semantically equivalent decisions despite divergent generative model outputs.

An essay arguing that LLMs, by returning the most probable continuation, may inadvertently suppress genuine novelty and deviation, leading to a cultural convergence toward the average rather than the truly new.

The article discusses various techniques to make LLM-powered agents more deterministic, such as golden sets, guardrails, consensus mechanisms, regression tests, coded logic, and hyperparameter tuning, and asks for additional successful methods.

This paper models the impact of delayed verification in multi-agent LLM systems, revealing that delayed correction can destabilize consensus and cause oscillations. It derives closed-form stability thresholds and provides a greedy approximation for optimal corrector placement, validated with experiments on five open models.

The article describes replacing single-model code review with a consensus of multiple AI models, where only explicit approvals count, leading to more reliable code reviews at the cost of longer discussions.

This paper presents improvements to IsabeLLM, an automated theorem proving tool built on Isabelle, by integrating a retrieval-augmented generation framework, error tracing, and counterexample generation. The improved tool is evaluated on the formal verification of Bitcoin's Proof of Work consensus protocol.

Proposes SSD-FL, a serverless semi-decentralized federated learning methodology that optimizes cluster formation in heterogeneous environments using effective loss functions and Cheeger inequality-based iterative clustering, improving convergence and communication efficiency.

A reflection arguing that in multi-model setups, the consensus output is less valuable than the disagreements, which reveal genuinely contested parts of a problem. The post questions whether consensus should be the goal and how to distinguish productive disagreement from noise.

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.