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ARXIV:2603.24676 · MULTI-AGENT LLMS · SUBMITTED 02 APR · 02:30 UTC · FRESHNESS STALE
ARXIV:2603.24676MULTI-AGENT LLMSSUBMITTED 02 APR · 02:30 UTCFRESHNESS STALEHidenori Tanaka · arXiv
A theoretical framework to understand how multi-agent LLM systems reach consensus through memetic drift, rather than pure reasoning.
Opportunity summary
Pain A theoretical framework to understand how multi-agent LLM systems reach consensus through memetic drift, rather than pure reasoning.
Evidence 0 refs | 0 sources | 17% coverage
Blocker Evidence unverified
A theoretical framework to understand how multi-agent LLM systems reach consensus through memetic drift, rather than pure reasoning. Yet it remains unclear whether their outcomes reflect collective reasoning, systematic bias, or mere chance.
Multi-agent systems powered by large language models (LLMs) are increasingly deployed in settings that shape consequential decisions, both directly and indirectly. Yet it remains unclear whether their outcomes reflect collective reasoning, systematic bias, or…
ScienceToStartup currently rates this 3.0/10 on the public viability pass. Together, these results provide a framework for studying the collective mechanisms of social representation formation in multi-agent systems.
Multi-Agent LLMs moved forward this cycle; last verified April 2026. Public score 3.0/10.
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A theoretical framework to understand how multi-agent LLM systems reach consensus through memetic drift, rather than pure reasoning.
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10.48550/arXiv.2603.24676A theoretical framework to understand how multi-agent LLM systems reach consensus through memetic drift, rather than pure reasoning.
Abstract
Multi-agent systems powered by large language models (LLMs) are increasingly deployed in settings that shape consequential decisions, both directly and indirectly. Yet it remains unclear whether their outcomes reflect collective reasoning, systematic bias, or mere chance. Recent work has sharpened this question with naming games, showing that even when no individual agent favors any label a priori, populations rapidly break symmetry and reach consensus. Here, we reveal the mechanism by introducing a minimal model, Quantized Simplex Gossip (QSG), and trace the microscopic origin of this agreement to mutual in-context learning. In QSG, agents maintain internal belief states but learn from one another's sampled outputs, so one agent's arbitrary choice becomes the next agent's evidence and can compound toward agreement. By analogy with neutral evolution, we call this sampling-driven regime memetic drift. QSG predicts a crossover from a drift-dominated regime, where consensus is effectively a lottery, to a selection regime, where weak biases are amplified and shape the outcome. We derive scaling laws for drift-induced polarization as a function of population size, communication bandwidth, in-context adaptation rate, and agents' internal uncertainty, and we validate them in both QSG simulations and naming-game experiments with LLM populations. Together, these results provide a framework for studying the collective mechanisms of social representation formation in multi-agent systems.
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PROBLEM
A theoretical framework to understand how multi-agent LLM systems reach consensus through memetic drift, rather than pure reasoning. Yet it remains unclear whether their outcomes reflect collective reasoning, systematic bias, or mere chance.
METHOD
Multi-agent systems powered by large language models (LLMs) are increasingly deployed in settings that shape consequential decisions, both directly and indirectly. Yet it remains unclear whether their outcomes reflect collective reasoning, systematic bias, or mere chance.
RESULT
ScienceToStartup currently rates this 3.0/10 on the public viability pass. Together, these results provide a framework for studying the collective mechanisms of social representation formation in multi-agent systems.
WHY NOW
Multi-Agent LLMs moved forward this cycle; last verified April 2026. Public score 3.0/10.
Abstract-backed public claims while anchored extraction refreshes.
A theoretical framework to understand how multi-agent LLM systems reach consensus through memetic drift, rather than pure reasoning. Yet it remains unclear whether their outcomes reflect collective reasoning, systematic bias, or mere chance.
Abstract-backed fallback claim; anchored extraction has not materialized a public claim row yet.
partial
Multi-agent systems powered by large language models (LLMs) are increasingly deployed in settings that shape consequential decisions, both directly and indirectly. Yet it remains unclear whether their outcomes reflect collective reasoning, systematic bias, or mere chance.
Abstract-backed fallback claim; anchored extraction has not materialized a public claim row yet.
partial
ScienceToStartup currently rates this 3.0/10 on the public viability pass. Together, these results provide a framework for studying the collective mechanisms of social representation formation in multi-agent systems.
Abstract-backed fallback claim; anchored extraction has not materialized a public claim row yet.
partial
Multi-Agent LLMs moved forward this cycle; last verified April 2026. Public score 3.0/10.
Abstract-backed fallback claim; anchored extraction has not materialized a public claim row yet.
partial
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A theoretical framework to understand how multi-agent LLM systems reach consensus through memetic drift, rather than pure reasoning.
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Multi-Agent LLMs
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Commercial read
3.0/10 public viability
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