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ARXIV:2605.10698 · MULTI-AGENT REASONING · SUBMITTED 12 MAY · 20:15 UTC · FRESHNESS FRESH
ARXIV:2605.10698MULTI-AGENT REASONINGSUBMITTED 12 MAY · 20:15 UTCFRESHNESS FRESHDahlia Shehata · Ming Li · arXiv
A framework to quantify the 'Bystander Effect' in multi-agent LLM reasoning, revealing how social pressure leads to 'Alignment Hallucinations' and degraded performance.
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Pain A framework to quantify the 'Bystander Effect' in multi-agent LLM reasoning, revealing how social pressure leads to 'Alignment Hallucinations' and degraded performance.
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A framework to quantify the 'Bystander Effect' in multi-agent LLM reasoning, revealing how social pressure leads to 'Alignment Hallucinations' and degraded performance. We challenge this by demonstrating that simulated social pressure triggers an algorithmic…
Multi-agent systems (MAS) assume that collaborating inherently improves Large Language Model (LLM) reasoning. We challenge this by demonstrating that simulated social pressure triggers an algorithmic ``Bystander Effect,'' inducing severe cognitive loafing.
ScienceToStartup currently rates this 7.0/10 on the public viability pass. Multi-agent systems (MAS) assume that collaborating inherently improves Large Language Model (LLM) reasoning. Code availability is flagged in the production record; the public repository…
Multi-Agent Reasoning moved forward this cycle; last verified May 2026. Public score 7.0/10. Production flags indicate code availability.
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A framework to quantify the 'Bystander Effect' in multi-agent LLM reasoning, revealing how social pressure leads to 'Alignment Hallucinations' and degraded performance.
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10.48550/arXiv.2605.10698A framework to quantify the 'Bystander Effect' in multi-agent LLM reasoning, revealing how social pressure leads to 'Alignment Hallucinations' and degraded performance.
Abstract
Multi-agent systems (MAS) assume that collaborating inherently improves Large Language Model (LLM) reasoning. We challenge this by demonstrating that simulated social pressure triggers an algorithmic ``Bystander Effect,'' inducing severe cognitive loafing. By evaluating 22,500 deterministic trajectories across 3 dataset contexts (GAIA, SWE-bench, Multi-Challenge) with 3 state-of-the-art (SOTA) models, we semantically audit internal reasoning traces against external outputs. We formalize the \textit{Interaction Depth Limit} ($D_L$), the exact plurality threshold where an agent's logical sovereignty collapses into social compliance. Crucially, we uncover the \textit{Sovereignty Gap}: models frequently compute the correct derivation internally but suffer ``Alignment Hallucinations'' -- actively subjugating empirical evidence to sycophantically appease a simulated swarm. We prove that multi-agent social load is strictly non-commutative; the "brand" identity of the ``Lead Anchor'' auditor disproportionately dictates the swarm's integrity. These findings expose architectural vulnerabilities, proving that unstructured multi-agent topologies can degrade independent reasoning.
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PROBLEM
A framework to quantify the 'Bystander Effect' in multi-agent LLM reasoning, revealing how social pressure leads to 'Alignment Hallucinations' and degraded performance. We challenge this by demonstrating that simulated social pressure triggers an algorithmic ``Bystander Effect,'...
METHOD
Multi-agent systems (MAS) assume that collaborating inherently improves Large Language Model (LLM) reasoning. We challenge this by demonstrating that simulated social pressure triggers an algorithmic ``Bystander Effect,'' inducing severe cognitive loafing.
RESULT
ScienceToStartup currently rates this 7.0/10 on the public viability pass. Multi-agent systems (MAS) assume that collaborating inherently improves Large Language Model (LLM) reasoning. Code availability is flagged in the production record; the public repository link still needs...
WHY NOW
Multi-Agent Reasoning moved forward this cycle; last verified May 2026. Public score 7.0/10. Production flags indicate code availability.
Abstract-backed public claims while anchored extraction refreshes.
A framework to quantify the 'Bystander Effect' in multi-agent LLM reasoning, revealing how social pressure leads to 'Alignment Hallucinations' and degraded performance. We challenge this by demonstrating that simulated social pressure triggers an algorithmic ``Bystander Effect,'' inducing severe cognitive loafing.
Abstract-backed fallback claim; anchored extraction has not materialized a public claim row yet.
partial
Multi-agent systems (MAS) assume that collaborating inherently improves Large Language Model (LLM) reasoning. We challenge this by demonstrating that simulated social pressure triggers an algorithmic ``Bystander Effect,'' inducing severe cognitive loafing.
Abstract-backed fallback claim; anchored extraction has not materialized a public claim row yet.
partial
ScienceToStartup currently rates this 7.0/10 on the public viability pass. Multi-agent systems (MAS) assume that collaborating inherently improves Large Language Model (LLM) reasoning. Code availability is flagged in the production record; the public repository link still needs proof alignment.
Abstract-backed fallback claim; anchored extraction has not materialized a public claim row yet.
partial
Multi-Agent Reasoning moved forward this cycle; last verified May 2026. Public score 7.0/10. Production flags indicate code availability.
Abstract-backed fallback claim; anchored extraction has not materialized a public claim row yet.
partial
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A framework to quantify the 'Bystander Effect' in multi-agent LLM reasoning, revealing how social pressure leads to 'Alignment Hallucinations' and degraded performance.
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