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ARXIV:2605.11789 · AGENT SIMULATION · SUBMITTED 13 MAY · 20:19 UTC · FRESHNESS FRESH
ARXIV:2605.11789AGENT SIMULATIONSUBMITTED 13 MAY · 20:19 UTCFRESHNESS FRESHAlison Moldovan-Mauer · Benedikt Mangold · arXiv
LLM-based multi-agent simulations are used to study the systemic costs of incivility in debates, finding convergence latency is larger for smaller models.
Opportunity summary
Pain LLM-based multi-agent simulations are used to study the systemic costs of incivility in debates, finding convergence latency is larger for smaller models.
Evidence 0 refs | 0 sources | 0% coverage
Blocker Evidence unverified
LLM-based multi-agent simulations are used to study the systemic costs of incivility in debates, finding convergence latency is larger for smaller models. Human subject research in this domain is constrained by ethical oversight, limited…
Unconstructive debate and uncivil communication carry well-documented costs for productivity and cohesion, yet isolating their effect on operational efficiency has proven difficult. Human subject research in this domain is constrained by ethical oversight, limited…
ScienceToStartup currently rates this 2.0/10 on the public viability pass. We further identify a significant first-mover advantage, whereby the agent initiating the discussion wins significantly above chance regardless of toxicity condition.
Agent Simulation moved forward this cycle; last verified May 2026. Public score 2.0/10.
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LLM-based multi-agent simulations are used to study the systemic costs of incivility in debates, finding convergence latency is larger for smaller models.
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10.48550/arXiv.2605.11789LLM-based multi-agent simulations are used to study the systemic costs of incivility in debates, finding convergence latency is larger for smaller models.
Abstract
Unconstructive debate and uncivil communication carry well-documented costs for productivity and cohesion, yet isolating their effect on operational efficiency has proven difficult. Human subject research in this domain is constrained by ethical oversight, limited reproducibility, and the inherent unpredictability of naturalistic settings. We address this gap by leveraging Large Language Model (LLM) based Multi-Agent Systems as a controlled sociological sandbox, enabling systematic manipulation of communicative behavior at scale. Using a Monte Carlo simulation framework, we generate thousands of structured 1-on-1 adversarial debates across varying toxicity conditions, measuring convergence time, defined as the number of rounds required to reach a conclusion, as a proxy for interactional efficiency. Building on a prior study, we replicate and extend its findings across two additional LLM agents of varying parameter size, allowing us to assess whether the effects of toxic behavior on debate dynamics generalize across model scale. The convergence latency of 25% reported in the previous study was confirmed. It was found that this latency is significantly bigger for models with fewer parameters. We further identify a significant first-mover advantage, whereby the agent initiating the discussion wins significantly above chance regardless of toxicity condition.
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PROBLEM
LLM-based multi-agent simulations are used to study the systemic costs of incivility in debates, finding convergence latency is larger for smaller models. Human subject research in this domain is constrained by ethical oversight, limited reproducibility, and the inherent unpredi...
METHOD
Unconstructive debate and uncivil communication carry well-documented costs for productivity and cohesion, yet isolating their effect on operational efficiency has proven difficult. Human subject research in this domain is constrained by ethical oversight, limited reproducibilit...
RESULT
ScienceToStartup currently rates this 2.0/10 on the public viability pass. We further identify a significant first-mover advantage, whereby the agent initiating the discussion wins significantly above chance regardless of toxicity condition.
WHY NOW
Agent Simulation moved forward this cycle; last verified May 2026. Public score 2.0/10.
Abstract-backed public claims while anchored extraction refreshes.
LLM-based multi-agent simulations are used to study the systemic costs of incivility in debates, finding convergence latency is larger for smaller models. Human subject research in this domain is constrained by ethical oversight, limited reproducibility, and the inherent unpredictability of naturalistic settings.
Abstract-backed fallback claim; anchored extraction has not materialized a public claim row yet.
partial
Unconstructive debate and uncivil communication carry well-documented costs for productivity and cohesion, yet isolating their effect on operational efficiency has proven difficult. Human subject research in this domain is constrained by ethical oversight, limited reproducibility, and the inherent unpredictability of naturalistic settings.
Abstract-backed fallback claim; anchored extraction has not materialized a public claim row yet.
partial
ScienceToStartup currently rates this 2.0/10 on the public viability pass. We further identify a significant first-mover advantage, whereby the agent initiating the discussion wins significantly above chance regardless of toxicity condition.
Abstract-backed fallback claim; anchored extraction has not materialized a public claim row yet.
partial
Agent Simulation moved forward this cycle; last verified May 2026. Public score 2.0/10.
Abstract-backed fallback claim; anchored extraction has not materialized a public claim row yet.
partial
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LLM-based multi-agent simulations are used to study the systemic costs of incivility in debates, finding convergence latency is larger for smaller models.
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Agent Simulation
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