consensus

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.

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.