arXiv AI
Measuring Consensus in AI Agent Networks
Read the original on arXiv AI →The Flow has not summarised this story yet — read it at arXiv AI.
Related stories
arXiv Computation and Language
Sep 3
AI agents reshape consensus formation in human groups
The study investigates how large language model (LLM) agents influence consensus formation in mixed human‑AI groups during a collaborative description game. Three regimes emerge: low agent proportions lead to human‑led consensus, intermediate proportions disrupt convergence, and high proportions produce strong, agent‑led consensus. The resulting consensus differs in semantic grounding and communicative form, with human‑led consensus being concrete and holistic, and agent‑led consensus being abstract and geometrically segmented.
arXiv AI
6d ago
TeamGym: A Gym for Team Decision-Making by AI Agents with Distributed Information
TeamGym is a new environment that evaluates and trains language‑model agents on team decision‑making tasks drawn from real‑world legal, finance, medical, and software engineering texts. It introduces the concept of pivotal information—a minimal set of facts necessary for a correct decision—and tracks how agents share and retrieve this information. Experiments show that distributing pivotal facts and requiring agents to extract them independently significantly reduces success rates, but reinforcement‑learning training can improve decision quality and foster coordination among team members.
arXiv AI
Aug 11
Illusion of Alignment: Detecting Hidden Disagreement in Collaborative Dialogue
arXiv:2608. 08210v1 Announce Type: new Abstract: Collaborative dialogue can end with apparent agreement while participants still differ on goals, assumptions, or execution plans, creating an \textbf{illusion of alignment (IoA)}.
arXiv Computation and Language
Oct 1
Multi-agent discussion gains less when dissent is withheld
arXiv:2609.38324v1 Announce Type: cross Abstract: Multi-agent systems of LLMs add discussion to majority voting and are therefore expected to be more capable. However, empirical reports conflict on w...
arXiv AI
Aug 24
Consilience: Conformally Calibrated Communication Control for Hidden-Profile Multi-Agent Reasoning
Consilience is an inference‑time orchestration framework that steers and certifies communication among multi‑agent large language models in hidden‑profile settings. It summarizes each discussion turn with a compact state of uncertainty, disagreement, evidence gain, redundancy, and premature consensus, then selects a communication intervention (challenge, clarify, seek evidence, or route) and speaker. A round‑wise conformal calibration procedure guarantees that the controller’s proposed action has bounded one‑step regret with high probability, and an acceptance mechanism enforces this guarantee for the executed action. Experiments on HiddenBench‑style tasks show that Consilience improves decision accuracy and communication efficiency over fixed and unstructured protocols, sometimes outperforming a full‑information baseline.
arXiv AI
Oct 1
CollabFlow: Recursive Self-Improvement of Agent Collaboration
CollabFlow introduces a recursive self‑improvement framework for multi‑agent collaboration in large language model systems. It trains a Collab‑Director to assemble teams of agents, uses a frozen executor to run them, and retrains the director each round based on outcomes. The system incorporates evidence‑conditioned communication protocols within collaboration graphs and a Collaborative Trajectory Balance objective to maintain diverse high‑performing teams across rounds, achieving superior performance on twelve datasets.